Merge pull request #165 from jfn6030217/asf-site
Asf site -removed abandoned cruft left over from WordPress
diff --git a/.DS_Store b/.DS_Store
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diff --git a/.gitignore b/.gitignore
index 3c3629e..fd4f2b0 100644
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node_modules
+.DS_Store
diff --git a/2016/03/index.html b/2016/03/index.html
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- While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that effort, came Apache Spot. Since we started talking about the project, these are the three questions I am asked the most.
- </p>
- <p>
- <strong>What Is Apache Spot?</strong>
- <br />
- Apache Spot is an open source, flow and packet analytics solution built on Hadoop. It combines big data processing, at-scale machine learning, and unique security analytics to put potential threats in front of defenders. While I am a data scientist today, I was a security investigator just a few years ago. I wanted to develop a solution that would put new tools and technology in play for defenders, but without requiring them to walk away from security and get a math degree.
- </p>
- <p>
- We wanted to start with the hard problems, so we looked at the emerging need to analyze data that was produced at a scale outside what a lot of security solutions could handle. The data is being created today, and lack of visibility into that data gives attackers a profound advantage. Also, in this new era of security, many defenders (public and private sector) have to answer to their citizens and customers when these threats occur. In other words, an event that says “this attack was blocked” is insufficient; an organization needs to see what happened before, during, and after a particular machine was attacked at a particular time. The problem is summarized in a slide from a <a href="http://www.youtube.com/watch?v=mOZjMuBLYyM" target="_blank">FloCon talk</a>
- <br />
- <a href="../wp-content/uploads/2016/03/FloCon2015.png" rel="attachment wp-att-66"><img class="aligncenter size-full wp-image-66" src="../wp-content/uploads/2016/03/FloCon2015.png" alt="open source packet and flow analytics" width="960" height="720" srcset="../wp-content/uploads/2016/03/FloCon2015.png 960w, ../wp-content/uploads/2016/03/FloCon2015-300x225.png 300w, ../wp-content/uploads/2016/03/FloCon2015-768x576.png 768w" sizes="(max-width: 960px) 100vw, 960px" /></a>
- </p>
- <p>
- The gist is that while processing is a challenge at higher scales, the amount of insight gained is higher when analyzing flows and packets from key protocols (like DNS). And that’s how we got here.
- </p>
- <p>
- <strong>Why Intel?</strong>
- </p>
- <p>
- At Intel, I have worked in IT, for a security product company (McAfee), and in the Data Center Group. Intel IT was an early pioneer of the concept of proactive investigations to protect intellectual property. McAfee (now Intel Security Group) has a broad customer base in the realms of network, endpoint, and content security, to name only a few. And the Intel Data Center group has strategic partnerships with Cloudera and Accenture, as well as some pretty cool analytics efforts of their own. Add the performance benefits we achieve with Intel Architecture, especially the Intel MPI Library and Intel Math Kernel Library, and it certainly makes sense to me.
- </p>
- <p>
- <strong>Why Open Source?</strong>
- </p>
- <p>
- I learned from my earlier efforts in security analytics, that to invite collaboration from academia, the public sector, and the private sector, open source software is an excellent choice. We are now seeking to build a community of developers, data scientists, and security enthusiasts to grow Apache Spot into something we can all be proud of. We have also chosen an Apache software license, so that it can enrich commercial software offerings as well.
- </p>
- <p>
- The greatest thing for me since we announced at RSA is to hear OTHER people talk about Apache Spot (formerly Open Network Insight or ONI), here are some of my favorites, from <a href="http://vision.cloudera.com/open-network-insight-changing-infosec-data-science-forever/" target="_blank">a Data Scientist @ eBay </a>, <a href="https://newsroom.accenture.com/news/accenture-introduces-the-accenture-cyber-intelligence-platform-to-help-organizations-continuously-predict-detect-and-combat-cyber-attacks.htm" target="_blank">a Security Provider</a>, and <a href="http://vision.cloudera.com/adaptive-security-at-big-data-scale-for-next-generation-digital-security/" target="_blank">a Big Data company</a>.
- </p>
- <p>
- Fork us on Github!
- </p>
- <p>
- Grant Babb
- </p>
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- <h1 class="entry-title single-title" itemprop="headline" rel="bookmark">Apache Spot (Incubating) and Cybersecurity — Using NetFlows to Detect Threats to Critical Infrastructure</h1>
-
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- <time class="updated entry-time" datetime="2016-08-08" itemprop="datePublished">
- August 8, 2016
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- <p>
- The “first” documented cybersecurity case was the worm replication, which was initiated by Robert T. Morris on November 2, 1988. Wow! Here we are in 2016, 28 years later, with viruses and worms giving way to Trojan horses and polymorphic code. Nowadays, we are also fighting against DDoS, phishing, spear phishing attacks, command and controls along with APTs such as Aurora, Zeus, Red October and Stuxnet. What happened with our security controls on each attack?
- </p>
- <p>
- Despite heroic efforts, internal and external security controls, no matter if they are preventive, detective or corrective, can be bypassed by different situations or misconfigurations. Capabilities to detect bugs or vulnerabilities in code, protocol, etc. are still limited. When we consider how to close these gaps, we have two options:
- </p>
- <ol>
- <li>
- Collect information from each device that is part of the environment.
- </li>
- <li>
- Collect information from the critical infrastructure that is used for most, if not all, of the systems.
- </li>
- </ol>
- <p>
- This blog will describe the second approach.
- </p>
- <p>
- Critical infrastructure, including nationally significant infrastructure, can be broadly defined as the systems, assets, facilities and networks that provide essential services. For nations, this means protecting the national security, economic security and prosperity as well as the health and safety of their citizenry. If we extrapolate this definition on the IT enterprise environments, we can define the critical infrastructure as the service, or services, that need to be up and running properly 99.99999% of the time, most of the time to support other critical infrastructure, such as databases, HR systems, manufacturing systems, etc.
- </p>
- <p>
- Effectively protecting critical infrastructure means that millions, if not billions, of different scenarios must be identified and monitored. To do this, the cybersecurity problem must be broken into small pieces.
- </p>
- <p>
- Let’s consider DNS — your communications to the Web. How can you know which communications are being established by your critical servers? And, what if you want to do it for most of your infrastructure?
- </p>
- <p>
- First idea: Use NetFlow, which is a network protocol that helps us collect IP traffic information and monitor network traffic. NetFlow has the details on the communications of all of your network traffic. However, the normal data on an enterprise environment includes billions of NetFlow events per day. To use this data to identify issues, it must be stored and analyzed. Storage alone is costly. Analyzing what amount Big Data stores is an entire other challenge.
- </p>
- <p>
- Apache Spot offers a solution. It was designed to gather, store and analyze Big Data. In fact, Apache Spot is an ideal solution for this cybersecurity challenge. Apache Spot can integrate many different data sources in a data lake then add operational context to the data by linking configuration, inventory, service databases and other data stores. This helps you to prioritize the actions to take under different attack, malware, APT and hacking scenarios. With Apache Spot, attacks that bypass our external or internal security controls can be identified. By delivering risk-prioritized, actionable insights, Apache Spot can support the growing need for security analytics.
- </p>
- <p>
- Not only can Apache Spot collect, store and analyze billions of NetFlow packets, but it can also be adapted to meet the unique requirements of your organization. How? Apache Spot is an open-source project.
- </p>
- <p>
- <strong>But Wait, There’s More</strong>
- </p>
- <p>
- Check out “<a href="../how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/"><u>How Apache Spot Helps Create Well-Stocked Data Lakes and Catch Powerful Insights</u></a>” to learn more about the underlying Apache Spot architecture.
- </p>
- <p>
- This is the first of a series of blogs that we will be writing about cybersecurity, so check back to read more.
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- <title>Comments on: Apache Spot (Incubating): Three Most-Asked Questions</title>
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<link rel='dns-prefetch' href='//fonts.googleapis.com' />
<link rel='dns-prefetch' href='//s.w.org' />
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<link rel='stylesheet' id='googleFonts-css' href='http://fonts.googleapis.com/css?family=Lato%3A400%2C700%2C400italic%2C700italic' type='text/css' media='all' />
<link rel='stylesheet' id='bones-stylesheet-css' href='../../library/css/style.css' type='text/css' media='all' />
@@ -183,13 +182,6 @@
Grant Babb
</p>
</section>
- <footer class="article-footer">
-
- filed under: <a href="../../category/security-analytics/" rel="category tag">Security Analytics</a>
- <p class="tags">
- <span class="tags-title">Tags:</span><a href="../../tag/github/" rel="tag">github</a>, <a href="../../tag/open-network-insight/" rel="tag">open network insight</a>, <a href="../../tag/open-source/" rel="tag">open source</a>
- </p>
- </footer>
</article>
@@ -220,26 +212,6 @@
</li>
</ul>
</div>
- <div id="archives-2" class="widget widget_archive">
- <h4 class="widgettitle">Archives</h4>
- <ul>
- <li>
- <a href='../../2017/03/'>March 2017</a>
- </li>
- <li>
- <a href='../../2016/10/'>October 2016</a>
- </li>
- <li>
- <a href='../../2016/09/'>September 2016</a>
- </li>
- <li>
- <a href='../../2016/08/'>August 2016</a>
- </li>
- <li>
- <a href='../../2016/03/'>March 2016</a>
- </li>
- </ul>
- </div>
</div>
diff --git a/blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/feed/index.html b/blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/feed/index.html
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- xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
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- <title>Comments on: Apache Spot (Incubating) and Cybersecurity — Using NetFlows to Detect Threats to Critical Infrastructure</title>
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index ef7b716..5633843 100755
--- a/blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/index.html
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@@ -29,7 +29,6 @@
<link rel='dns-prefetch' href='//fonts.googleapis.com' />
<link rel='dns-prefetch' href='//s.w.org' />
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<link rel='stylesheet' id='googleFonts-css' href='http://fonts.googleapis.com/css?family=Lato%3A400%2C700%2C400italic%2C700italic' type='text/css' media='all' />
<link rel='stylesheet' id='bones-stylesheet-css' href='../../library/css/style.css' type='text/css' media='all' />
@@ -190,11 +189,6 @@
This is the first of a series of blogs that we will be writing about cybersecurity, so check back to read more.
</p>
</section>
- <footer class="article-footer">
-
- filed under: <a href="../../category/cybersecurity/" rel="category tag">Cybersecurity</a>
-
- </footer>
</article>
@@ -225,27 +219,6 @@
</li>
</ul>
</div>
- <div id="archives-2" class="widget widget_archive">
- <h4 class="widgettitle">Archives</h4>
- <ul>
- <li>
- <a href='../../2017/03/'>March 2017</a>
- </li>
- <li>
- <a href='../../2016/10/'>October 2016</a>
- </li>
- <li>
- <a href='../../2016/09/'>September 2016</a>
- </li>
- <li>
- <a href='../../2016/08/'>August 2016</a>
- </li>
- <li>
- <a href='../../2016/03/'>March 2016</a>
- </li>
- </ul>
- </div>
-
</div>
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+++ b/blog/apache-spot-product-architecture-overview/index.html
@@ -29,7 +29,6 @@
<link rel='dns-prefetch' href='//fonts.googleapis.com' />
<link rel='dns-prefetch' href='//s.w.org' />
- <link rel="alternate" type="application/rss+xml" title="Apache Spot » Feed" href="../../feed/" />
<link rel='stylesheet' id='googleFonts-css' href='http://fonts.googleapis.com/css?family=Lato%3A400%2C700%2C400italic%2C700italic' type='text/css' media='all' />
<link rel='stylesheet' id='bones-stylesheet-css' href='../../library/css/style.css' type='text/css' media='all' />
@@ -179,11 +178,6 @@
<p>In the next releases, Apache Spot will share IoC (suspicious results ranked) with other security tools. Suspicious results scored as a critical can be shared using McAfee Open DXL with the Open Security Controller and/or McAfee ePO to adjust or tune your security policies in real time.</p>
<p class="small">[1] Blei, David M., Andrew Y. Ng, and Michael I. Jordan. “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3, no. Jan (2003): 993-1022.</p>
</section>
- <footer class="article-footer">
-
- filed under: <a href="../../category/uncategorized/" rel="category tag">Uncategorized</a>
-
- </footer>
</article>
@@ -214,26 +208,6 @@
</li>
</ul>
</div>
- <div id="archives-2" class="widget widget_archive">
- <h4 class="widgettitle">Archives</h4>
- <ul>
- <li>
- <a href='../../2017/03/'>March 2017</a>
- </li>
- <li>
- <a href='../../2016/10/'>October 2016</a>
- </li>
- <li>
- <a href='../../2016/09/'>September 2016</a>
- </li>
- <li>
- <a href='../../2016/08/'>August 2016</a>
- </li>
- <li>
- <a href='../../2016/03/'>March 2016</a>
- </li>
- </ul>
- </div>
</div>
diff --git a/blog/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/feed/index.html b/blog/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/feed/index.html
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@@ -29,7 +29,6 @@
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<link rel='stylesheet' id='googleFonts-css' href='http://fonts.googleapis.com/css?family=Lato%3A400%2C700%2C400italic%2C700italic' type='text/css' media='all' />
<link rel='stylesheet' id='bones-stylesheet-css' href='../../library/css/style.css' type='text/css' media='all' />
@@ -172,11 +171,6 @@
Because Apache Spot is an open-source project, most of the components depicted here can be modified by the end user.
</p>
</section>
- <footer class="article-footer">
-
- filed under: <a href="../../category/uncategorized/" rel="category tag">Uncategorized</a>
-
- </footer>
</article>
@@ -207,26 +201,6 @@
</li>
</ul>
</div>
- <div id="archives-2" class="widget widget_archive">
- <h4 class="widgettitle">Archives</h4>
- <ul>
- <li>
- <a href='../../2017/03/'>March 2017</a>
- </li>
- <li>
- <a href='../../2016/10/'>October 2016</a>
- </li>
- <li>
- <a href='../../2016/09/'>September 2016</a>
- </li>
- <li>
- <a href='../../2016/08/'>August 2016</a>
- </li>
- <li>
- <a href='../../2016/03/'>March 2016</a>
- </li>
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- </div>
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diff --git a/blog/index.html b/blog/index.html
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<link rel='stylesheet' id='googleFonts-css' href='http://fonts.googleapis.com/css?family=Lato%3A400%2C700%2C400italic%2C700italic' type='text/css' media='all' />
<link rel='stylesheet' id='bones-stylesheet-css' href='../library/css/style.css' type='text/css' media='all' />
@@ -306,26 +305,6 @@
</li>
</ul>
</div>
- <div id="archives-2" class="widget widget_archive">
- <h4 class="widgettitle">Archives</h4>
- <ul>
- <li>
- <a href='../2017/03/'>March 2017</a>
- </li>
- <li>
- <a href='../2016/10/'>October 2016</a>
- </li>
- <li>
- <a href='../2016/09/'>September 2016</a>
- </li>
- <li>
- <a href='../2016/08/'>August 2016</a>
- </li>
- <li>
- <a href='../2016/03/'>March 2016</a>
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- </ul>
- </div>
</div>
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<link rel='stylesheet' id='googleFonts-css' href='http://fonts.googleapis.com/css?family=Lato%3A400%2C700%2C400italic%2C700italic' type='text/css' media='all' />
<link rel='stylesheet' id='bones-stylesheet-css' href='../../library/css/style.css' type='text/css' media='all' />
@@ -220,12 +219,6 @@
</p>
</section>
- <footer class="article-footer">
-
- filed under: <a href="../../category/data-science/" rel="category tag">Data Science</a>, <a href="../../category/ipython-notebooks/" rel="category tag">Ipython Notebooks</a>, <a href="../../category/threat-analysis-tools/" rel="category tag">Threat Analysis Tools</a>
-
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</article>
</main>
@@ -255,26 +248,6 @@
</li>
</ul>
</div>
- <div id="archives-2" class="widget widget_archive">
- <h4 class="widgettitle">Archives</h4>
- <ul>
- <li>
- <a href='../../2017/03/'>March 2017</a>
- </li>
- <li>
- <a href='../../2016/10/'>October 2016</a>
- </li>
- <li>
- <a href='../../2016/09/'>September 2016</a>
- </li>
- <li>
- <a href='../../2016/08/'>August 2016</a>
- </li>
- <li>
- <a href='../../2016/03/'>March 2016</a>
- </li>
- </ul>
- </div>
</div>
diff --git a/blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/feed/index.html b/blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/feed/index.html
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<link rel='stylesheet' id='googleFonts-css' href='http://fonts.googleapis.com/css?family=Lato%3A400%2C700%2C400italic%2C700italic' type='text/css' media='all' />
<link rel='stylesheet' id='bones-stylesheet-css' href='../../library/css/style.css' type='text/css' media='all' />
@@ -161,11 +160,6 @@
By supporting Apache Spot, Cybraics is hoping to learn as much as we contribute while we collaborate and take part in this Open Source Initiative. With the end result a set of guidelines for organizations to deploy a data analytics platform to find threats within their ecosystem, and a community that will share their experiences with the next generation of data and security professionals, perhaps we can all start actually working together to start leveling the playing field.
</p>
</section>
- <footer class="article-footer">
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- filed under: <a href="../../category/uncategorized/" rel="category tag">Uncategorized</a>
-
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</article>
@@ -196,26 +190,6 @@
</li>
</ul>
</div>
- <div id="archives-2" class="widget widget_archive">
- <h4 class="widgettitle">Archives</h4>
- <ul>
- <li>
- <a href='../../2017/03/'>March 2017</a>
- </li>
- <li>
- <a href='../../2016/10/'>October 2016</a>
- </li>
- <li>
- <a href='../../2016/09/'>September 2016</a>
- </li>
- <li>
- <a href='../../2016/08/'>August 2016</a>
- </li>
- <li>
- <a href='../../2016/03/'>March 2016</a>
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- <title>Apache Spot and Cybersecurity — Using NetFlows to Detect Threats to Critical Infrastructure</title>
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- <comments>http://spot.incubator.apache.org/blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/#respond</comments>
- <pubDate>Mon, 08 Aug 2016 08:00:32 +0000</pubDate>
- <dc:creator><![CDATA[oni-admin]]></dc:creator>
- <category><![CDATA[Cybersecurity]]></category>
-
- <guid isPermaLink="false">http://spot.incubator.apache.org/?p=117</guid>
- <description><![CDATA[The “first” documented cybersecurity case was the worm replication, which was initiated by Robert T. Morris on November 2, 1988. Wow! Here we are in 2016, 28 years later, with viruses and worms giving way to Trojan horses and polymorphic code. Nowadays, we are also fighting against DDoS, phishing, spear phishing attacks, command and controls... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/" title="Read Apache Spot and Cybersecurity — Using NetFlows to Detect Threats to Critical Infrastructure">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p>The “first” documented cybersecurity case was the worm replication, which was initiated by Robert T. Morris on November 2, 1988. Wow! Here we are in 2016, 28 years later, with viruses and worms giving way to Trojan horses and polymorphic code. Nowadays, we are also fighting against DDoS, phishing, spear phishing attacks, command and controls along with APTs such as Aurora, Zeus, Red October and Stuxnet. What happened with our security controls on each attack?</p>
-<p>Despite heroic efforts, internal and external security controls, no matter if they are preventive, detective or corrective, can be bypassed by different situations or misconfigurations. Capabilities to detect bugs or vulnerabilities in code, protocol, etc. are still limited. When we consider how to close these gaps, we have two options:</p>
-<ol>
-<li>Collect information from each device that is part of the environment.</li>
-<li>Collect information from the critical infrastructure that is used for most, if not all, of the systems.</li>
-</ol>
-<p>This blog will describe the second approach.</p>
-<p>Critical infrastructure, including nationally significant infrastructure, can be broadly defined as the systems, assets, facilities and networks that provide essential services. For nations, this means protecting the national security, economic security and prosperity as well as the health and safety of their citizenry. If we extrapolate this definition on the IT enterprise environments, we can define the critical infrastructure as the service, or services, that need to be up and running properly 99.99999% of the time, most of the time to support other critical infrastructure, such as databases, HR systems, manufacturing systems, etc.</p>
-<p>Effectively protecting critical infrastructure means that millions, if not billions, of different scenarios must be identified and monitored. To do this, the cybersecurity problem must be broken into small pieces.</p>
-<p>Let’s consider DNS — your communications to the Web. How can you know which communications are being established by your critical servers? And, what if you want to do it for most of your infrastructure?</p>
-<p>First idea: Use NetFlow, which is a network protocol that helps us collect IP traffic information and monitor network traffic. NetFlow has the details on the communications of all of your network traffic. However, the normal data on an enterprise environment includes billions of NetFlow events per day. To use this data to identify issues, it must be stored and analyzed. Storage alone is costly. Analyzing what amount Big Data stores is an entire other challenge.</p>
-<p>Apache Spot offers a solution. It was designed to gather, store and analyze Big Data. In fact, Apache Spot is an ideal solution for this cybersecurity challenge. Apache Spot can integrate many different data sources in a data lake then add operational context to the data by linking configuration, inventory, service databases and other data stores. This helps you to prioritize the actions to take under different attack, malware, APT and hacking scenarios. With Apache Spot, attacks that bypass our external or internal security controls can be identified. By delivering risk-prioritized, actionable insights, Apache Spot can support the growing need for security analytics.</p>
-<p>Not only can Apache Spot collect, store and analyze billions of NetFlow packets, but it can also be adapted to meet the unique requirements of your organization. How? Apache Spot is an open-source project.</p>
-<p><strong>But Wait, There’s More</strong></p>
-<p>Check out “<a href="http://spot.incubator.apache.org/blog/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/"><u>How Apache Spot Helps Create Well-Stocked Data Lakes and Catch Powerful Insights</u></a>” to learn more about the underlying Apache Spot architecture.</p>
-<p>This is the first of a series of blogs that we will be writing about cybersecurity, so check back to read more.</p>
-]]></content:encoded>
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- The “first” documented cybersecurity case was the worm replication, which was initiated by Robert T. Morris on November 2, 1988. Wow! Here we are in 2016, 28 years later, with viruses and worms giving way to Trojan horses and polymorphic code. Nowadays, we are also fighting against DDoS, phishing, spear phishing attacks, command and controls… <a class="excerpt-read-more" href="../../blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/" title="Read Apache Spot and Cybersecurity — Using NetFlows to Detect Threats to Critical Infrastructure">Read more »</a>
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- <guid isPermaLink="false">http://spot.incubator.apache.org/?p=136</guid>
- <description><![CDATA[Why Does Apache Spot Include iPython notebooks? The project team wants Apache Spot to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/jupyter-notebooks-for-data-analysis/" title="Read Jupyter Notebooks for Data Analysis">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p><strong>Why Does Apache Spot Include iPython notebooks? </strong></p>
-<p>The project team wants Apache Spot to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython Notebook is an appropriate platform for easy data exploration. One of its biggest advantages is that it provides parallel and distributed computing to enable code execution and debugging in an interactive environment – thus the ‘i’ in iPython.</p>
-<p>The iPython notebook is a web based interactive computational environment that provides access to the Python shell. While iPython notebooks were originally designed to work with the Python language, they support a number of other programming languages, including Ruby, Scala, Julia, R, Go, C, C++, Java and Perl. There are also multiple additional packages that can be used to get the most out of this highly-customizable tool.</p>
-<p>Starting on version 4.0, most notebook functionalities are now part of the Project Jupyter, while iPython remains as the kernel to work with Python code in the notebooks.</p>
-<img src="http://spot.incubator.apache.org/library/images/iPython-1.png" alt="ipython" width="361" height="107" class="aligncenter size-full wp-image-140" />
-<p><strong>IPython with Apache Spot for Network Threat Detection</strong></p>
-<p><em>NOTE: This is not intended to be a step-by-step tutorial on how to code a threat analysis in Apache Spot, but more like an introduction on how to approach the suspicions of a security breach.</em></p>
-<p>Although machine learning (ML) will do most of the work detecting anomalies in the traffic, Apache Spot also includes two notebook templates that can get you started on this. The <em>Threat_Investigation_master.ipynb</em> is designed to query the raw data table to find all connections in a day that are related to any threat you select – even connections that were not necessarily flagged as suspicious by ML on a first run. This gives us the chance to get a new data subset and here is where the fun begins.</p>
-<p>If you suspect of a specific type of attack in your network, you can get the whole story by answering the Five ‘W’s.</p>
-<p><strong><em>What? </em></strong></p>
-<p>Maybe there’s been an increase in the logs collected by the system, which indicates abnormal amounts of communication in your network. Or, the amount of POST requests in your network have risen overnight. This is the mystery that needs to be solved by researching through the anomalies previously detected by ML.</p>
-<p><strong><em>Who?</em></strong></p>
-<p>Assuming you have a network context, you can identify the name of the infected machine inside the network, as well as the name of the IP or DNS on the other side of the connection (if it is a known host). If you don’t have a network context or are using DHCP, this can be a little tricky to detect using only Netflow logs. But, that’s where DNS and Proxy logs, come in handy. Including a network context file with Apache Spot is really simple and can go a long way when identifying a threat.</p>
-<p><strong><em>When?</em></strong></p>
-<p>To have a broader visibility on the attack, you can customize the queries on the Threat investigation notebook to review the data through a wider time lapse – instead of just checking through the current day. With this, you could find an increase of a certain type of requests to one (or many) URIs and predict its future behavior.</p>
-<p><strong><em>Where?</em></strong></p>
-<p>When working only with DNS, having a destination URL might not say much about where your information is going to, but Apache Spot allows you to connect with a geolocation database to identify the location of the suspected attackers IP. Taking advantage of this option, you can visually locate the other end of the connection on a map. You might find that it’s pointing to a country banned by your company, indicating a leak.</p>
-<p><strong><em>Why?</em></strong></p>
-<p>This answer to “why” will depend highly on the result of the analysis. For instance, an excessive amount of POST requests from one machine inside the network to an unidentified URI can indicate a data mining attack. Tracing back to patient zero, you can find that this could have originated with a phishing email, malicious software installed by an employee or a one-time visitor’s infected machine that connected to your network.</p>
-<p><strong>How to Get Answers to the Five Ws Questions</strong></p>
-<p>All of the previous questions can be answered by looking at the raw data collected. Although performing elaborated queries directly to your database can seem tempting, this type of analysis with Hive, or even Impala, can be very time consuming. A better approach would be to use Pandas to read and transform your dataset into a relational structured dataframe. This lets you work with as if it were an offline structured relational database.</p>
-<p>Once you have your desired results and data subsets, you can use MatplotLib to easily graph your findings. (We cover this subject in more depth in another post.) Another advantage of the notebook is that you can download it as HTML or a PDF file to store locally and use it in a presentation – or just keep it for future reference.</p>
-<p><strong>Wrap Up</strong></p>
-<p>This post was meant to be just a brief introduction of how you can use iPython notebooks in Apache Spot to perform further data analysis and include it our executive report (in addition to the already included Story board). Although this is not the only way you can do this, it is a very interactive and fun way to do it. You’ll also see that the overall processing time is very short – thanks to the iPython notebook task parallelism ability.</p>
-<p>We want to hear from YOU! Have you used iPython notebooks before? How do you feel about having this tool in Apache Spot? If you’re interested in further data analysis through interactive charts, a new post is coming soon on D3 and jQuery data visualization. Also, check back soon to read more on this and other Cybersecurity subjects.</p>
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- <link>http://spot.incubator.apache.org/blog/jupyter-notebooks-for-data-analysis/</link>
- <comments>http://spot.incubator.apache.org/blog/jupyter-notebooks-for-data-analysis/#respond</comments>
- <pubDate>Thu, 22 Sep 2016 21:28:12 +0000</pubDate>
- <dc:creator><![CDATA[oni-admin]]></dc:creator>
- <category><![CDATA[Data Science]]></category>
- <category><![CDATA[Ipython Notebooks]]></category>
- <category><![CDATA[Threat Analysis Tools]]></category>
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- <guid isPermaLink="false">http://spot.incubator.apache.org/?p=136</guid>
- <description><![CDATA[Why Does Apache Spot Include iPython notebooks? The project team wants Apache Spot to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/jupyter-notebooks-for-data-analysis/" title="Read Jupyter Notebooks for Data Analysis">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p><strong>Why Does Apache Spot Include iPython notebooks? </strong></p>
-<p>The project team wants Apache Spot to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython Notebook is an appropriate platform for easy data exploration. One of its biggest advantages is that it provides parallel and distributed computing to enable code execution and debugging in an interactive environment – thus the ‘i’ in iPython.</p>
-<p>The iPython notebook is a web based interactive computational environment that provides access to the Python shell. While iPython notebooks were originally designed to work with the Python language, they support a number of other programming languages, including Ruby, Scala, Julia, R, Go, C, C++, Java and Perl. There are also multiple additional packages that can be used to get the most out of this highly-customizable tool.</p>
-<p>Starting on version 4.0, most notebook functionalities are now part of the Project Jupyter, while iPython remains as the kernel to work with Python code in the notebooks.</p>
-<img src="http://spot.incubator.apache.org/library/images/iPython-1.png" alt="ipython" width="361" height="107" class="aligncenter size-full wp-image-140" />
-<p><strong>IPython with Apache Spot for Network Threat Detection</strong></p>
-<p><em>NOTE: This is not intended to be a step-by-step tutorial on how to code a threat analysis in Apache Spot, but more like an introduction on how to approach the suspicions of a security breach.</em></p>
-<p>Although machine learning (ML) will do most of the work detecting anomalies in the traffic, Apache Spot also includes two notebook templates that can get you started on this. The <em>Threat_Investigation_master.ipynb</em> is designed to query the raw data table to find all connections in a day that are related to any threat you select – even connections that were not necessarily flagged as suspicious by ML on a first run. This gives us the chance to get a new data subset and here is where the fun begins.</p>
-<p>If you suspect of a specific type of attack in your network, you can get the whole story by answering the Five ‘W’s.</p>
-<p><strong><em>What? </em></strong></p>
-<p>Maybe there’s been an increase in the logs collected by the system, which indicates abnormal amounts of communication in your network. Or, the amount of POST requests in your network have risen overnight. This is the mystery that needs to be solved by researching through the anomalies previously detected by ML.</p>
-<p><strong><em>Who?</em></strong></p>
-<p>Assuming you have a network context, you can identify the name of the infected machine inside the network, as well as the name of the IP or DNS on the other side of the connection (if it is a known host). If you don’t have a network context or are using DHCP, this can be a little tricky to detect using only Netflow logs. But, that’s where DNS and Proxy logs, come in handy. Including a network context file with Apache Spot is really simple and can go a long way when identifying a threat.</p>
-<p><strong><em>When?</em></strong></p>
-<p>To have a broader visibility on the attack, you can customize the queries on the Threat investigation notebook to review the data through a wider time lapse – instead of just checking through the current day. With this, you could find an increase of a certain type of requests to one (or many) URIs and predict its future behavior.</p>
-<p><strong><em>Where?</em></strong></p>
-<p>When working only with DNS, having a destination URL might not say much about where your information is going to, but Apache Spot allows you to connect with a geolocation database to identify the location of the suspected attackers IP. Taking advantage of this option, you can visually locate the other end of the connection on a map. You might find that it’s pointing to a country banned by your company, indicating a leak.</p>
-<p><strong><em>Why?</em></strong></p>
-<p>This answer to “why” will depend highly on the result of the analysis. For instance, an excessive amount of POST requests from one machine inside the network to an unidentified URI can indicate a data mining attack. Tracing back to patient zero, you can find that this could have originated with a phishing email, malicious software installed by an employee or a one-time visitor’s infected machine that connected to your network.</p>
-<p><strong>How to Get Answers to the Five Ws Questions</strong></p>
-<p>All of the previous questions can be answered by looking at the raw data collected. Although performing elaborated queries directly to your database can seem tempting, this type of analysis with Hive, or even Impala, can be very time consuming. A better approach would be to use Pandas to read and transform your dataset into a relational structured dataframe. This lets you work with as if it were an offline structured relational database.</p>
-<p>Once you have your desired results and data subsets, you can use MatplotLib to easily graph your findings. (We cover this subject in more depth in another post.) Another advantage of the notebook is that you can download it as HTML or a PDF file to store locally and use it in a presentation – or just keep it for future reference.</p>
-<p><strong>Wrap Up</strong></p>
-<p>This post was meant to be just a brief introduction of how you can use iPython notebooks in Apache Spot to perform further data analysis and include it our executive report (in addition to the already included Story board). Although this is not the only way you can do this, it is a very interactive and fun way to do it. You’ll also see that the overall processing time is very short – thanks to the iPython notebook task parallelism ability.</p>
-<p>We want to hear from YOU! Have you used iPython notebooks before? How do you feel about having this tool in Apache Spot? If you’re interested in further data analysis through interactive charts, a new post is coming soon on D3 and jQuery data visualization. Also, check back soon to read more on this and other Cybersecurity subjects.</p>
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- Why Does Apache Spot Include iPython notebooks? The project team wants Apache Spot to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython… <a class="excerpt-read-more" href="../../blog/jupyter-notebooks-for-data-analysis/" title="Read Jupyter Notebooks for Data Analysis">Read more »</a>
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- <dc:creator><![CDATA[oni-admin]]></dc:creator>
- <category><![CDATA[Security Analytics]]></category>
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- <guid isPermaLink="false">http://spot.incubator.apache.org/?p=62</guid>
- <description><![CDATA[While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/apache-spot-3-most-asked-questions/" title="Read Apache Spot: Three Most-Asked Questions">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p>While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that effort, came Apache Spot. Since we started talking about the project, these are the three questions I am asked the most.</p>
-<p><strong>What Is Apache Spot?</strong><br />
-Apache Spot is an open source, flow and packet analytics solution built on Hadoop. It combines big data processing, at-scale machine learning, and unique security analytics to put potential threats in front of defenders. While I am a data scientist today, I was a security investigator just a few years ago. I wanted to develop a solution that would put new tools and technology in play for defenders, but without requiring them to walk away from security and get a math degree.</p>
-<p>We wanted to start with the hard problems, so we looked at the emerging need to analyze data that was produced at a scale outside what a lot of security solutions could handle. The data is being created today, and lack of visibility into that data gives attackers a profound advantage. Also, in this new era of security, many defenders (public and private sector) have to answer to their citizens and customers when these threats occur. In other words, an event that says “this attack was blocked” is insufficient; an organization needs to see what happened before, during, and after a particular machine was attacked at a particular time. The problem is summarized in a slide from a <a href="http://www.youtube.com/watch?v=mOZjMuBLYyM" target="_blank">FloCon talk</a><br />
-<a href="http://spot.incubator.apache.org/library/images/FloCon2015.png" rel="attachment wp-att-66"><img class="aligncenter size-full wp-image-66" src="http://spot.incubator.apache.org/library/images/FloCon2015.png" alt="open source packet and flow analytics" /></a></p>
-<p>The gist is that while processing is a challenge at higher scales, the amount of insight gained is higher when analyzing flows and packets from key protocols (like DNS). And that’s how we got here.</p>
-<p><strong>Why Intel?</strong></p>
-<p>At Intel, I have worked in IT, for a security product company (McAfee), and in the Data Center Group. Intel IT was an early pioneer of the concept of proactive investigations to protect intellectual property. McAfee (now Intel Security Group) has a broad customer base in the realms of network, endpoint, and content security, to name only a few. And the Intel Data Center group has strategic partnerships with Cloudera and Accenture, as well as some pretty cool analytics efforts of their own. Add the performance benefits we achieve with Intel Architecture, especially the Intel MPI Library and Intel Math Kernel Library, and it certainly makes sense to me.</p>
-<p><strong>Why Open Source?</strong></p>
-<p>I learned from my earlier efforts in security analytics, that to invite collaboration from academia, the public sector, and the private sector, open source software is an excellent choice. We are now seeking to build a community of developers, data scientists, and security enthusiasts to grow Apache Spot into something we can all be proud of. We have also chosen an Apache software license, so that it can enrich commercial software offerings as well.</p>
-<p>The greatest thing for me since we announced at RSA is to hear OTHER people talk about Apache Spot (formerly Open Network Insight or ONI), here are some of my favorites, from <a href="http://vision.cloudera.com/apache-spot-changing-infosec-data-science-forever/" target="_blank">a Data Scientist @ eBay </a>, <a href="https://newsroom.accenture.com/news/accenture-introduces-the-accenture-cyber-intelligence-platform-to-help-organizations-continuously-predict-detect-and-combat-cyber-attacks.htm" target="_blank">a Security Provider</a>, and <a href="http://vision.cloudera.com/adaptive-security-at-big-data-scale-for-next-generation-digital-security/" target="_blank">a Big Data company</a>.</p>
-<p>Fork us on Github!</p>
-<p>Grant Babb</p>
-]]></content:encoded>
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- While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that… <a class="excerpt-read-more" href="../../blog/apache-spot-3-most-asked-questions/" title="Read Apache Spot: Three Most-Asked Questions">Read more »</a>
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- <pubDate>Thu, 22 Sep 2016 21:28:12 +0000</pubDate>
- <dc:creator><![CDATA[oni-admin]]></dc:creator>
- <category><![CDATA[Data Science]]></category>
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- <description><![CDATA[Why Does Apache Spot Include iPython notebooks? The project team wants Apache Spot to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/jupyter-notebooks-for-data-analysis/" title="Read Jupyter Notebooks for Data Analysis">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p><strong>Why Does Apache Spot Include iPython notebooks? </strong></p>
-<p>The project team wants Apache Spot to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython Notebook is an appropriate platform for easy data exploration. One of its biggest advantages is that it provides parallel and distributed computing to enable code execution and debugging in an interactive environment – thus the ‘i’ in iPython.</p>
-<p>The iPython notebook is a web based interactive computational environment that provides access to the Python shell. While iPython notebooks were originally designed to work with the Python language, they support a number of other programming languages, including Ruby, Scala, Julia, R, Go, C, C++, Java and Perl. There are also multiple additional packages that can be used to get the most out of this highly-customizable tool.</p>
-<p>Starting on version 4.0, most notebook functionalities are now part of the Project Jupyter, while iPython remains as the kernel to work with Python code in the notebooks.</p>
-<img src="http://spot.incubator.apache.org/library/images/iPython-1.png" alt="ipython" class="aligncenter size-full wp-image-140" />
-<p><strong>IPython with Apache Spot for Network Threat Detection</strong></p>
-<p><em>NOTE: This is not intended to be a step-by-step tutorial on how to code a threat analysis in Apache Spot, but more like an introduction on how to approach the suspicions of a security breach.</em></p>
-<p>Although machine learning (ML) will do most of the work detecting anomalies in the traffic, Apache Spot also includes two notebook templates that can get you started on this. The <em>Threat_Investigation_master.ipynb</em> is designed to query the raw data table to find all connections in a day that are related to any threat you select – even connections that were not necessarily flagged as suspicious by ML on a first run. This gives us the chance to get a new data subset and here is where the fun begins.</p>
-<p>If you suspect of a specific type of attack in your network, you can get the whole story by answering the Five ‘W’s.</p>
-<p><strong><em>What? </em></strong></p>
-<p>Maybe there’s been an increase in the logs collected by the system, which indicates abnormal amounts of communication in your network. Or, the amount of POST requests in your network have risen overnight. This is the mystery that needs to be solved by researching through the anomalies previously detected by ML.</p>
-<p><strong><em>Who?</em></strong></p>
-<p>Assuming you have a network context, you can identify the name of the infected machine inside the network, as well as the name of the IP or DNS on the other side of the connection (if it is a known host). If you don’t have a network context or are using DHCP, this can be a little tricky to detect using only Netflow logs. But, that’s where DNS and Proxy logs, come in handy. Including a network context file with Apache Spot is really simple and can go a long way when identifying a threat.</p>
-<p><strong><em>When?</em></strong></p>
-<p>To have a broader visibility on the attack, you can customize the queries on the Threat investigation notebook to review the data through a wider time lapse – instead of just checking through the current day. With this, you could find an increase of a certain type of requests to one (or many) URIs and predict its future behavior.</p>
-<p><strong><em>Where?</em></strong></p>
-<p>When working only with DNS, having a destination URL might not say much about where your information is going to, but Apache Spot allows you to connect with a geolocation database to identify the location of the suspected attackers IP. Taking advantage of this option, you can visually locate the other end of the connection on a map. You might find that it’s pointing to a country banned by your company, indicating a leak.</p>
-<p><strong><em>Why?</em></strong></p>
-<p>This answer to “why” will depend highly on the result of the analysis. For instance, an excessive amount of POST requests from one machine inside the network to an unidentified URI can indicate a data mining attack. Tracing back to patient zero, you can find that this could have originated with a phishing email, malicious software installed by an employee or a one-time visitor’s infected machine that connected to your network.</p>
-<p><strong>How to Get Answers to the Five Ws Questions</strong></p>
-<p>All of the previous questions can be answered by looking at the raw data collected. Although performing elaborated queries directly to your database can seem tempting, this type of analysis with Hive, or even Impala, can be very time consuming. A better approach would be to use Pandas to read and transform your dataset into a relational structured dataframe. This lets you work with as if it were an offline structured relational database.</p>
-<p>Once you have your desired results and data subsets, you can use MatplotLib to easily graph your findings. (We cover this subject in more depth in another post.) Another advantage of the notebook is that you can download it as HTML or a PDF file to store locally and use it in a presentation – or just keep it for future reference.</p>
-<p><strong>Wrap Up</strong></p>
-<p>This post was meant to be just a brief introduction of how you can use iPython notebooks in Apache Spot to perform further data analysis and include it our executive report (in addition to the already included Story board). Although this is not the only way you can do this, it is a very interactive and fun way to do it. You’ll also see that the overall processing time is very short – thanks to the iPython notebook task parallelism ability.</p>
-<p>We want to hear from YOU! Have you used iPython notebooks before? How do you feel about having this tool in Apache Spot? If you’re interested in further data analysis through interactive charts, a new post is coming soon on D3 and jQuery data visualization. Also, check back soon to read more on this and other Cybersecurity subjects.</p>
-]]></content:encoded>
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- Why Does Apache Spot Include iPython notebooks? The project team wants Apache Spot to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython… <a class="excerpt-read-more" href="../../blog/jupyter-notebooks-for-data-analysis/" title="Read Jupyter Notebooks for Data Analysis">Read more »</a>
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- <title>Jupyter Notebooks for Data Analysis</title>
- <link>http://spot.incubator.apache.org/blog/jupyter-notebooks-for-data-analysis/</link>
- <comments>http://spot.incubator.apache.org/blog/jupyter-notebooks-for-data-analysis/#respond</comments>
- <pubDate>Thu, 22 Sep 2016 21:28:12 +0000</pubDate>
- <dc:creator><![CDATA[oni-admin]]></dc:creator>
- <category><![CDATA[Uncategorized]]></category>
-
- <guid isPermaLink="false">http://spot.incubator.apache.org/?p=136</guid>
- <description><![CDATA[Why Does Apache Spot Include iPython notebooks? The project team wants Apache Spot needs to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/jupyter-notebooks-for-data-analysis/" title="Read Jupyter Notebooks for Data Analysis">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p><strong>Why Does Apache Spot Include iPython notebooks? </strong></p>
-<p>The project team wants Apache Spot needs to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython Notebook is an appropriate platform for easy data exploration. One of its biggest advantages is that it provides parallel and distributed computing to enable code execution and debugging in an interactive environment – thus the ‘i’ in iPython.</p>
-<p>The iPython notebook is a web based interactive computational environment that provides access to the Python shell. While iPython notebooks were originally designed to work with the Python language, they support a number of other programming languages, including Ruby, Scala, Julia, R, Go, C, C++, Java and Perl. There are also multiple additional packages that can be used to get the most out of this highly-customizable tool.</p>
-<p>Starting on version 4.0, most notebook functionalities are now part of the Project Jupyter, while iPython remains as the kernel to work with Python code in the notebooks.</p>
-<img src="http://spot.incubator.apache.org/library/images/iPython-1.png" alt="ipython" class="aligncenter size-full wp-image-140" />
-<p><strong>IPython with Apache Spot for Network Threat Detection</strong></p>
-<p><em>NOTE: This is not intended to be a step-by-step tutorial on how to code a threat analysis in Apache Spot, but more like an introduction on how to approach the suspicions of a security breach.</em></p>
-<p>Although machine learning (ML) will do most of the work detecting anomalies in the traffic, Apache Spot also includes two notebook templates that can get you started on this. The <em>Threat_Investigation_master.ipynb</em> is designed to query the raw data table to find all connections in a day that are related to any threat you select – even connections that were not necessarily flagged as suspicious by ML on a first run. This gives us the chance to get a new data subset and here is where the fun begins.</p>
-<p>If you suspect of a specific type of attack in your network, you can get the whole story by answering the Five ‘W’s.</p>
-<p><strong><em>What? </em></strong></p>
-<p>Maybe there’s been an increase in the logs collected by the system, which indicates abnormal amounts of communication in your network. Or, the amount of POST requests in your network have risen overnight. This is the mystery that needs to be solved by researching through the anomalies previously detected by ML.</p>
-<p><strong><em>Who?</em></strong></p>
-<p>Assuming you have a network context, you can identify the name of the infected machine inside the network, as well as the name of the IP or DNS on the other side of the connection (if it is a known host). If you don’t have a network context or are using DHCP, this can be a little tricky to detect using only Netflow logs. But, that’s where DNS and Proxy logs, come in handy. Including a network context file with Apache Spot is really simple and can go a long way when identifying a threat.</p>
-<p><strong><em>When?</em></strong></p>
-<p>To have a broader visibility on the attack, you can customize the queries on the Threat investigation notebook to review the data through a wider time lapse – instead of just checking through the current day. With this, you could find an increase of a certain type of requests to one (or many) URIs and predict its future behavior.</p>
-<p><strong><em>Where?</em></strong></p>
-<p>When working only with DNS, having a destination URL might not say much about where your information is going to, but Apache Spot allows you to connect with a geolocation database to identify the location of the suspected attackers IP. Taking advantage of this option, you can visually locate the other end of the connection on a map. You might find that it’s pointing to a country banned by your company, indicating a leak.</p>
-<p><strong><em>Why?</em></strong></p>
-<p>This answer to “why” will depend highly on the result of the analysis. For instance, an excessive amount of POST requests from one machine inside the network to an unidentified URI can indicate a data mining attack. Tracing back to patient zero, you can find that this could have originated with a phishing email, malicious software installed by an employee or a one-time visitor’s infected machine that connected to your network.</p>
-<p><strong>How to Get Answers to the Five Ws Questions</strong></p>
-<p>All of the previous questions can be answered by looking at the raw data collected. Although performing elaborated queries directly to your database can seem tempting, this type of analysis with Hive, or even Impala, can be very time consuming. A better approach would be to use Pandas to read and transform your dataset into a relational structured dataframe. This lets you work with as if it were an offline structured relational database.</p>
-<p>Once you have your desired results and data subsets, you can use MatplotLib to easily graph your findings. (We cover this subject in more depth in another post.) Another advantage of the notebook is that you can download it as HTML or a PDF file to store locally and use it in a presentation – or just keep it for future reference.</p>
-<p><strong>Wrap Up</strong></p>
-<p>This post was meant to be just a brief introduction of how you can use iPython notebooks in Apache Spot to perform further data analysis and include it our executive report (in addition to the already included Story board). Although this is not the only way you can do this, it is a very interactive and fun way to do it. You’ll also see that the overall processing time is very short – thanks to the iPython notebook task parallelism ability.</p>
-<p>We want to hear from YOU! Have you used iPython notebooks before? How do you feel about having this tool in Apache Spot? If you’re interested in further data analysis through interactive charts, a new post is coming soon on D3 and jQuery data visualization. Also, check back soon to read more on this and other Cybersecurity subjects.</p>
-]]></content:encoded>
- <wfw:commentRss>http://spot.incubator.apache.org/blog/jupyter-notebooks-for-data-analysis/feed/</wfw:commentRss>
- <slash:comments>0</slash:comments>
- </item>
-
- <item>
- <title>How Apache (Incubating) Spot Helps Create Well-Stocked Data Lakes and Catch Powerful Insights</title>
- <link>http://spot.incubator.apache.org/blog/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/</link>
- <comments>http://spot.incubator.apache.org/blog/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/#respond</comments>
- <pubDate>Mon, 08 Aug 2016 07:59:41 +0000</pubDate>
- <dc:creator><![CDATA[oni-admin]]></dc:creator>
- <category><![CDATA[Uncategorized]]></category>
-
- <guid isPermaLink="false">http://spot.incubator.apache.org/?p=113</guid>
- <description><![CDATA[About four years ago, the era of the Big Data analytics began. Paired with advanced analytics, massive volumes of data can be culled to not only inform critical decisions, but also to simulate sophisticated “what if” scenarios that allow companies to gain competitive advantages by generating and predicting different scenarios. For example, a financial services... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/" title="Read How Apache Spot Helps Create Well-Stocked Data Lakes and Catch Powerful Insights">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p>About four years ago, the era of the Big Data analytics began. Paired with advanced analytics, massive volumes of data can be culled to not only inform critical decisions, but also to simulate sophisticated “what if” scenarios that allow companies to gain competitive advantages by generating and predicting different scenarios. For example, a financial services company can more accurately determine what other products to offer a customer, and in what order, based on a wide variety of data, then use advanced analytics to gather insights. Creating a data lake that can be effectively used for predictive analytics raises tough questions — what data sources should we use? How should this data be collected and ingested? What are the best algorithms to analyze the data, and how should we present these results to our decision maker?</p>
-<p>Apache Spot can help to solve most of these issues. Following is a description of the Apache Spot, which is designed to facilitate Big Data analytics scenarios like the financial services company’s question about the right product to offer customers.</p>
-<a href="http://spot.incubator.apache.org/library/images/ONI_Architecture-Diagram_1300_v4.png"><img src="http://spot.incubator.apache.org/library/images/ONI_Architecture-Diagram_1300_v4.png" alt="oni_architecture-diagram_1300_v4" class="alignnone size-full wp-image-114" /></a>
-<h3><strong>Apache Spot Core Components</strong></h3>
-<p>The Apache Spot Core is composed of three main components — data integration (collectors), data store (HDFS here, but can also be a non-SQL database) and machine learning.</p>
-<p>In this diagram, the top left shows Apache Spot Data Sources, which include the collection of the information that will be used to create a data lake. The process is simple. Define a pull or push from the source of information then capture this information on Apache Spot’s “collectors.” The collectors are processes that interpret the information that is sent, then write it to the HDFS system in the Apache Spot cluster. The HDFS stores the data lake and ensures that resources can grow while remaining economical at every size. The Apache Spot algorithms are part of machine learning and are used to detect the uncommon information in the data lake.</p>
-<h3><strong>Operational Analytics</strong></h3>
-<p>As part of operational analytics, Apache Spot executes different batch processes that add information to machine learning results to provide meaning and context. Using the financial services product example, basic customer data could be augmented with information about other customers in the same region along with information about which products those customers recommended or complained about. Basically, the data scientists can “play” with the data using different algorithms to identify insights.</p>
-<h3><strong>Visualizing Results</strong></h3>
-<p>The Apache Spot GUI displays the results that the machine learning algorithms generate. Results are represented such that it is easy to identify both the most common things as well as find the most suspicious or uncommon information that is part of the data lake.</p>
-<h3><strong>Customizable Open Source</strong></h3>
-<p>Because Apache Spot is an open-source project, most of the components depicted here can be modified by the end user.</p>
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- By Rob Kent, Vice President of Marketing at Cybraics Competition is widely considered to be a healthy and positive thing, traditionally viewed as providing options for customers and fueling innovation. In the world of cybersecurity there is no shortage of competition, in fact cybersecurity is one of the most crowded and fast-growing areas of technology…. <a class="excerpt-read-more" href="../../blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/" title="Read Strength in Numbers: Why Consider Open Source Cybersecurity Analytics">Read more »</a>
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- <link>http://spot.incubator.apache.org/blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/</link>
- <comments>http://spot.incubator.apache.org/blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/#respond</comments>
- <pubDate>Fri, 21 Oct 2016 15:48:33 +0000</pubDate>
- <dc:creator><![CDATA[oni-admin]]></dc:creator>
- <category><![CDATA[Uncategorized]]></category>
-
- <guid isPermaLink="false">http://spot.incubator.apache.org/?p=149</guid>
- <description><![CDATA[By Rob Kent, Vice President of Marketing at Cybraics Competition is widely considered to be a healthy and positive thing, traditionally viewed as providing options for customers and fueling innovation. In the world of cybersecurity there is no shortage of competition, in fact cybersecurity is one of the most crowded and fast-growing areas of technology.... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/strength-in-numbers-why-consider-open-source-cybersecurity-analytics/" title="Read Strength in Numbers: Why Consider Open Source Cybersecurity Analytics">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p>By Rob Kent, Vice President of Marketing at Cybraics</p>
- <p>Competition is widely considered to be a healthy and positive thing, traditionally viewed as providing options for customers and fueling innovation. In the world of cybersecurity there is no shortage of competition, in fact cybersecurity is one of the most crowded and fast-growing areas of technology. The problem is, with so much competition, are we losing sight of the real goal: protecting our customers against the adversary? With so much focus on competing and winning customers, are we negating one of the most fundamental advantages that we could have in the fight against cybercrime? Cooperation. Our adversaries are not shy about working together… the community is strong and growing, and while there is no doubt some healthy competition, the sharing of tools and techniques is certainly far more common than in the commercial world. Like Open Source software, security can only benefit and grow through peer-reviewed submissions. Citing Linus Law by Eric S. Raymond from his book The Cathedral and the Bazaar, “Given a large enough beta-tester and co-developer base, almost every problem will be characterized quickly and the fix will be obvious to someone.” </p>
- <p>This, of course, is not new information. People have been pointing this out for years. What is encouraging is that a few organizations are finally stepping up to the plate to try and change this paradigm in cybersecurity. When it comes to cybersecurity, many have touted big data analytics as one of the key initiatives to combat adversaries. The problem is that big data in itself has its own set of challenges, one of the issues is that it is often looked at as a costly problem to store and scale as opposed to being used as another tool in an organization’s arsenal. Apache Spot, a project pioneered by Intel and Cloudera, is aiming to fix this problem. </p>
- <p>By creating an open source community, the hope is that identifying security threats within large data sets will become a manageable task for all organizations, despite their size or scale. Organizations overwhelmed with their data or those that aren’t seeing results will have a community to turn to along with a common reference to compare results. By working together on Apache Spot, organizations will be able to share their experiences on how they’ve tackled or how they need to tackle these issues with a common system to reference. Data is never going to get smaller across organizations’ core systems, so in many ways an organization just now beginning to dig into their data stores is basically starting from scratch. Having the ability to reference an open common model gives the opportunity for both those starting out and those seasoned, to have an open exchange of varying knowledge. </p>
- <p>By supporting Apache Spot, Cybraics is hoping to learn as much as we contribute while we collaborate and take part in this Open Source Initiative. With the end result a set of guidelines for organizations to deploy a data analytics platform to find threats within their ecosystem, and a community that will share their experiences with the next generation of data and security professionals, perhaps we can all start actually working together to start leveling the playing field. </p>
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- <title>Jupyter Notebooks for Data Analysis</title>
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- <pubDate>Thu, 22 Sep 2016 21:28:12 +0000</pubDate>
- <dc:creator><![CDATA[oni-admin]]></dc:creator>
- <category><![CDATA[Uncategorized]]></category>
-
- <guid isPermaLink="false">http://spot.incubator.apache.org/?p=136</guid>
- <description><![CDATA[Why Does Apache Spot Include iPython notebooks? The project team wants Apache Spot needs to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/jupyter-notebooks-for-data-analysis/" title="Read Jupyter Notebooks for Data Analysis">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p><strong>Why Does Apache Spot Include iPython notebooks? </strong></p>
-<p>The project team wants Apache Spot needs to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython Notebook is an appropriate platform for easy data exploration. One of its biggest advantages is that it provides parallel and distributed computing to enable code execution and debugging in an interactive environment – thus the ‘i’ in iPython.</p>
-<p>The iPython notebook is a web based interactive computational environment that provides access to the Python shell. While iPython notebooks were originally designed to work with the Python language, they support a number of other programming languages, including Ruby, Scala, Julia, R, Go, C, C++, Java and Perl. There are also multiple additional packages that can be used to get the most out of this highly-customizable tool.</p>
-<p>Starting on version 4.0, most notebook functionalities are now part of the Project Jupyter, while iPython remains as the kernel to work with Python code in the notebooks.</p>
-<img src="http://spot.incubator.apache.org/library/images/iPython-1.png" alt="ipython" class="aligncenter size-full wp-image-140" />
-<p><strong>IPython with Apache Spot for Network Threat Detection</strong></p>
-<p><em>NOTE: This is not intended to be a step-by-step tutorial on how to code a threat analysis in Apache Spot, but more like an introduction on how to approach the suspicions of a security breach.</em></p>
-<p>Although machine learning (ML) will do most of the work detecting anomalies in the traffic, Apache Spot also includes two notebook templates that can get you started on this. The <em>Threat_Investigation_master.ipynb</em> is designed to query the raw data table to find all connections in a day that are related to any threat you select – even connections that were not necessarily flagged as suspicious by ML on a first run. This gives us the chance to get a new data subset and here is where the fun begins.</p>
-<p>If you suspect of a specific type of attack in your network, you can get the whole story by answering the Five ‘W’s.</p>
-<p><strong><em>What? </em></strong></p>
-<p>Maybe there’s been an increase in the logs collected by the system, which indicates abnormal amounts of communication in your network. Or, the amount of POST requests in your network have risen overnight. This is the mystery that needs to be solved by researching through the anomalies previously detected by ML.</p>
-<p><strong><em>Who?</em></strong></p>
-<p>Assuming you have a network context, you can identify the name of the infected machine inside the network, as well as the name of the IP or DNS on the other side of the connection (if it is a known host). If you don’t have a network context or are using DHCP, this can be a little tricky to detect using only Netflow logs. But, that’s where DNS and Proxy logs, come in handy. Including a network context file with Apache Spot is really simple and can go a long way when identifying a threat.</p>
-<p><strong><em>When?</em></strong></p>
-<p>To have a broader visibility on the attack, you can customize the queries on the Threat investigation notebook to review the data through a wider time lapse – instead of just checking through the current day. With this, you could find an increase of a certain type of requests to one (or many) URIs and predict its future behavior.</p>
-<p><strong><em>Where?</em></strong></p>
-<p>When working only with DNS, having a destination URL might not say much about where your information is going to, but Apache Spot allows you to connect with a geolocation database to identify the location of the suspected attackers IP. Taking advantage of this option, you can visually locate the other end of the connection on a map. You might find that it’s pointing to a country banned by your company, indicating a leak.</p>
-<p><strong><em>Why?</em></strong></p>
-<p>This answer to “why” will depend highly on the result of the analysis. For instance, an excessive amount of POST requests from one machine inside the network to an unidentified URI can indicate a data mining attack. Tracing back to patient zero, you can find that this could have originated with a phishing email, malicious software installed by an employee or a one-time visitor’s infected machine that connected to your network.</p>
-<p><strong>How to Get Answers to the Five Ws Questions</strong></p>
-<p>All of the previous questions can be answered by looking at the raw data collected. Although performing elaborated queries directly to your database can seem tempting, this type of analysis with Hive, or even Impala, can be very time consuming. A better approach would be to use Pandas to read and transform your dataset into a relational structured dataframe. This lets you work with as if it were an offline structured relational database.</p>
-<p>Once you have your desired results and data subsets, you can use MatplotLib to easily graph your findings. (We cover this subject in more depth in another post.) Another advantage of the notebook is that you can download it as HTML or a PDF file to store locally and use it in a presentation – or just keep it for future reference.</p>
-<p><strong>Wrap Up</strong></p>
-<p>This post was meant to be just a brief introduction of how you can use iPython notebooks in Apache Spot to perform further data analysis and include it our executive report (in addition to the already included Story board). Although this is not the only way you can do this, it is a very interactive and fun way to do it. You’ll also see that the overall processing time is very short – thanks to the iPython notebook task parallelism ability.</p>
-<p>We want to hear from YOU! Have you used iPython notebooks before? How do you feel about having this tool in Apache Spot? If you’re interested in further data analysis through interactive charts, a new post is coming soon on D3 and jQuery data visualization. Also, check back soon to read more on this and other Cybersecurity subjects.</p>
-]]></content:encoded>
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- <item>
- <title>Apache Spot (Incubating) and Cybersecurity — Using NetFlows to Detect Threats to Critical Infrastructure</title>
- <link>http://spot.incubator.apache.org/blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/</link>
- <comments>http://spot.incubator.apache.org/blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/#respond</comments>
- <pubDate>Mon, 08 Aug 2016 08:00:32 +0000</pubDate>
- <dc:creator><![CDATA[oni-admin]]></dc:creator>
- <category><![CDATA[Cybersecurity]]></category>
-
- <guid isPermaLink="false">http://spot.incubator.apache.org/?p=117</guid>
- <description><![CDATA[The “first” documented cybersecurity case was the worm replication, which was initiated by Robert T. Morris on November 2, 1988. Wow! Here we are in 2016, 28 years later, with viruses and worms giving way to Trojan horses and polymorphic code. Nowadays, we are also fighting against DDoS, phishing, spear phishing attacks, command and controls... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/apache-spot-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/" title="Read Apache Spot and Cybersecurity — Using NetFlows to Detect Threats to Critical Infrastructure">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p>The “first” documented cybersecurity case was the worm replication, which was initiated by Robert T. Morris on November 2, 1988. Wow! Here we are in 2016, 28 years later, with viruses and worms giving way to Trojan horses and polymorphic code. Nowadays, we are also fighting against DDoS, phishing, spear phishing attacks, command and controls along with APTs such as Aurora, Zeus, Red October and Stuxnet. What happened with our security controls on each attack?</p>
-<p>Despite heroic efforts, internal and external security controls, no matter if they are preventive, detective or corrective, can be bypassed by different situations or misconfigurations. Capabilities to detect bugs or vulnerabilities in code, protocol, etc. are still limited. When we consider how to close these gaps, we have two options:</p>
-<ol>
-<li>Collect information from each device that is part of the environment.</li>
-<li>Collect information from the critical infrastructure that is used for most, if not all, of the systems.</li>
-</ol>
-<p>This blog will describe the second approach.</p>
-<p>Critical infrastructure, including nationally significant infrastructure, can be broadly defined as the systems, assets, facilities and networks that provide essential services. For nations, this means protecting the national security, economic security and prosperity as well as the health and safety of their citizenry. If we extrapolate this definition on the IT enterprise environments, we can define the critical infrastructure as the service, or services, that need to be up and running properly 99.99999% of the time, most of the time to support other critical infrastructure, such as databases, HR systems, manufacturing systems, etc.</p>
-<p>Effectively protecting critical infrastructure means that millions, if not billions, of different scenarios must be identified and monitored. To do this, the cybersecurity problem must be broken into small pieces.</p>
-<p>Let’s consider DNS — your communications to the Web. How can you know which communications are being established by your critical servers? And, what if you want to do it for most of your infrastructure?</p>
-<p>First idea: Use NetFlow, which is a network protocol that helps us collect IP traffic information and monitor network traffic. NetFlow has the details on the communications of all of your network traffic. However, the normal data on an enterprise environment includes billions of NetFlow events per day. To use this data to identify issues, it must be stored and analyzed. Storage alone is costly. Analyzing what amount Big Data stores is an entire other challenge.</p>
-<p>Apache Spot offers a solution. It was designed to gather, store and analyze Big Data. In fact, Apache Spot is an ideal solution for this cybersecurity challenge. Apache Spot can integrate many different data sources in a data lake then add operational context to the data by linking configuration, inventory, service databases and other data stores. This helps you to prioritize the actions to take under different attack, malware, APT and hacking scenarios. With Apache Spot, attacks that bypass our external or internal security controls can be identified. By delivering risk-prioritized, actionable insights, Apache Spot can support the growing need for security analytics.</p>
-<p>Not only can Apache Spot collect, store and analyze billions of NetFlow packets, but it can also be adapted to meet the unique requirements of your organization. How? Apache Spot is an open-source project.</p>
-<p><strong>But Wait, There’s More</strong></p>
-<p>Check out “<a href="http://spot.incubator.apache.org/blog/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/"><u>How Apache Spot Helps Create Well-Stocked Data Lakes and Catch Powerful Insights</u></a>” to learn more about the underlying Apache Spot architecture.</p>
-<p>This is the first of a series of blogs that we will be writing about cybersecurity, so check back to read more.</p>
-]]></content:encoded>
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- <item>
- <title>How Apache Spot Helps Create Well-Stocked Data Lakes and Catch Powerful Insights</title>
- <link>http://spot.incubator.apache.org/blog/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/</link>
- <comments>http://spot.incubator.apache.org/blog/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/#respond</comments>
- <pubDate>Mon, 08 Aug 2016 07:59:41 +0000</pubDate>
- <dc:creator><![CDATA[oni-admin]]></dc:creator>
- <category><![CDATA[Uncategorized]]></category>
-
- <guid isPermaLink="false">http://spot.incubator.apache.org/?p=113</guid>
- <description><![CDATA[About four years ago, the era of the Big Data analytics began. Paired with advanced analytics, massive volumes of data can be culled to not only inform critical decisions, but also to simulate sophisticated “what if” scenarios that allow companies to gain competitive advantages by generating and predicting different scenarios. For example, a financial services... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/" title="Read How Apache Spot Helps Create Well-Stocked Data Lakes and Catch Powerful Insights">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p>About four years ago, the era of the Big Data analytics began. Paired with advanced analytics, massive volumes of data can be culled to not only inform critical decisions, but also to simulate sophisticated “what if” scenarios that allow companies to gain competitive advantages by generating and predicting different scenarios. For example, a financial services company can more accurately determine what other products to offer a customer, and in what order, based on a wide variety of data, then use advanced analytics to gather insights. Creating a data lake that can be effectively used for predictive analytics raises tough questions — what data sources should we use? How should this data be collected and ingested? What are the best algorithms to analyze the data, and how should we present these results to our decision maker?</p>
-<p>Apache Spot can help to solve most of these issues. Following is a description of the Apache Spot, which is designed to facilitate Big Data analytics scenarios like the financial services company’s question about the right product to offer customers.</p>
-<a href="http://spot.incubator.apache.org/library/images/ONI_Architecture-Diagram_1300_v4.png"><img src="http://spot.incubator.apache.org/library/images/ONI_Architecture-Diagram_1300_v4.png" alt="oni_architecture-diagram_1300_v4" class="alignnone size-full wp-image-114" /></a>
-<h3><strong>Apache Spot Core Components</strong></h3>
-<p>The Apache Spot Core is composed of three main components — data integration (collectors), data store (HDFS here, but can also be a non-SQL database) and machine learning.</p>
-<p>In this diagram, the top left shows Apache Spot Data Sources, which include the collection of the information that will be used to create a data lake. The process is simple. Define a pull or push from the source of information then capture this information on Apache Spot’s “collectors.” The collectors are processes that interpret the information that is sent, then write it to the HDFS system in the Apache Spot cluster. The HDFS stores the data lake and ensures that resources can grow while remaining economical at every size. The Apache Spot algorithms are part of machine learning and are used to detect the uncommon information in the data lake.</p>
-<h3><strong>Operational Analytics</strong></h3>
-<p>As part of operational analytics, Apache Spot executes different batch processes that add information to machine learning results to provide meaning and context. Using the financial services product example, basic customer data could be augmented with information about other customers in the same region along with information about which products those customers recommended or complained about. Basically, the data scientists can “play” with the data using different algorithms to identify insights.</p>
-<h3><strong>Visualizing Results</strong></h3>
-<p>The Apache Spot GUI displays the results that the machine learning algorithms generate. Results are represented such that it is easy to identify both the most common things as well as find the most suspicious or uncommon information that is part of the data lake.</p>
-<h3><strong>Customizable Open Source</strong></h3>
-<p>Because Apache Spot is an open-source project, most of the components depicted here can be modified by the end user.</p>
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- <title>Apache Spot (Incubating): Three Most-Asked Questions</title>
- <link>http://spot.incubator.apache.org/blog/apache-spot-3-most-asked-questions/</link>
- <comments>http://spot.incubator.apache.org/blog/apache-spot-3-most-asked-questions/#respond</comments>
- <pubDate>Tue, 29 Mar 2016 05:48:19 +0000</pubDate>
- <dc:creator><![CDATA[oni-admin]]></dc:creator>
- <category><![CDATA[Security Analytics]]></category>
- <category><![CDATA[github]]></category>
- <category><![CDATA[open network insight]]></category>
- <category><![CDATA[open source]]></category>
-
- <guid isPermaLink="false">http://spot.incubator.apache.org/?p=62</guid>
- <description><![CDATA[While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/apache-spot-3-most-asked-questions/" title="Read Apache Spot: Three Most-Asked Questions">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p>While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that effort, came Apache Spot. Since we started talking about the project, these are the three questions I am asked the most.</p>
-<p><strong>What Is Apache Spot?</strong><br />
-Apache Spot is an open source, flow and packet analytics solution built on Hadoop. It combines big data processing, at-scale machine learning, and unique security analytics to put potential threats in front of defenders. While I am a data scientist today, I was a security investigator just a few years ago. I wanted to develop a solution that would put new tools and technology in play for defenders, but without requiring them to walk away from security and get a math degree.</p>
-<p>We wanted to start with the hard problems, so we looked at the emerging need to analyze data that was produced at a scale outside what a lot of security solutions could handle. The data is being created today, and lack of visibility into that data gives attackers a profound advantage. Also, in this new era of security, many defenders (public and private sector) have to answer to their citizens and customers when these threats occur. In other words, an event that says “this attack was blocked” is insufficient; an organization needs to see what happened before, during, and after a particular machine was attacked at a particular time. The problem is summarized in a slide from a <a href="http://www.youtube.com/watch?v=mOZjMuBLYyM" target="_blank">FloCon talk</a><br />
-<a href="http://spot.incubator.apache.org/library/images/FloCon2015.png" rel="attachment wp-att-66"><img class="aligncenter size-full wp-image-66" src="http://spot.incubator.apache.org/library/images/FloCon2015.png" alt="open source packet and flow analytics" /></a></p>
-<p>The gist is that while processing is a challenge at higher scales, the amount of insight gained is higher when analyzing flows and packets from key protocols (like DNS). And that’s how we got here.</p>
-<p><strong>Why Intel?</strong></p>
-<p>At Intel, I have worked in IT, for a security product company (McAfee), and in the Data Center Group. Intel IT was an early pioneer of the concept of proactive investigations to protect intellectual property. McAfee (now Intel Security Group) has a broad customer base in the realms of network, endpoint, and content security, to name only a few. And the Intel Data Center group has strategic partnerships with Cloudera and Accenture, as well as some pretty cool analytics efforts of their own. Add the performance benefits we achieve with Intel Architecture, especially the Intel MPI Library and Intel Math Kernel Library, and it certainly makes sense to me.</p>
-<p><strong>Why Open Source?</strong></p>
-<p>I learned from my earlier efforts in security analytics, that to invite collaboration from academia, the public sector, and the private sector, open source software is an excellent choice. We are now seeking to build a community of developers, data scientists, and security enthusiasts to grow Apache Spot into something we can all be proud of. We have also chosen an Apache software license, so that it can enrich commercial software offerings as well.</p>
-<p>The greatest thing for me since we announced at RSA is to hear OTHER people talk about Apache Spot (formerly Open Network Insight or ONI), here are some of my favorites, from <a href="http://vision.cloudera.com/apache-spot-changing-infosec-data-science-forever/" target="_blank">a Data Scientist @ eBay </a>, <a href="https://newsroom.accenture.com/news/accenture-introduces-the-accenture-cyber-intelligence-platform-to-help-organizations-continuously-predict-detect-and-combat-cyber-attacks.htm" target="_blank">a Security Provider</a>, and <a href="http://vision.cloudera.com/adaptive-security-at-big-data-scale-for-next-generation-digital-security/" target="_blank">a Big Data company</a>.</p>
-<p>Fork us on Github!</p>
-<p>Grant Babb</p>
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- <h1 class="entry-title single-title" itemprop="headline" rel="bookmark">How Apache Spot (Incubating) Helps Create Well-Stocked Data Lakes and Catch Powerful Insights</h1>
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- About four years ago, the era of the Big Data analytics began. Paired with advanced analytics, massive volumes of data can be culled to not only inform critical decisions, but also to simulate sophisticated “what if” scenarios that allow companies to gain competitive advantages by generating and predicting different scenarios. For example, a financial services company can more accurately determine what other products to offer a customer, and in what order, based on a wide variety of data, then use advanced analytics to gather insights. Creating a data lake that can be effectively used for predictive analytics raises tough questions — what data sources should we use? How should this data be collected and ingested? What are the best algorithms to analyze the data, and how should we present these results to our decision maker?
- </p>
- <p>
- Apache Spot (Incubating) can help to solve most of these issues. Following is a description of the Apache Spot (Incubating), which is designed to facilitate Big Data analytics scenarios like the financial services company’s question about the right product to offer customers.
- </p>
- <a href="../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4.png"><img src="../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4.png" alt="oni_architecture-diagram_1300_v4" width="1300" height="675" class="alignnone size-full wp-image-114" srcset="../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4.png 1300w, ../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4-300x156.png 300w, ../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4-768x399.png 768w, ../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4-1024x532.png 1024w" sizes="(max-width: 1300px) 100vw, 1300px" /></a>
- <h3><strong>Apache Spot (Incubating) Core Components</strong></h3>
- <p>
- The Apache Spot (Incubating) Core is composed of three main components — data integration (collectors), data store (HDFS here, but can also be a non-SQL database) and machine learning.
- </p>
- <p>
- In this diagram, the top left shows Apache Spot (Incubating) Data Sources, which include the collection of the information that will be used to create a data lake. The process is simple. Define a pull or push from the source of information then capture this information on Apache Spot (Incubating)’s “collectors.” The collectors are processes that interpret the information that is sent, then write it to the HDFS system in the Apache Spot (Incubating) cluster. The HDFS stores the data lake and ensures that resources can grow while remaining economical at every size. The Apache Spot (Incubating) algorithms are part of machine learning and are used to detect the uncommon information in the data lake.
- </p>
- <h3><strong>Operational Analytics</strong></h3>
- <p>
- As part of operational analytics, Apache Spot (Incubating) executes different batch processes that add information to machine learning results to provide meaning and context. Using the financial services product example, basic customer data could be augmented with information about other customers in the same region along with information about which products those customers recommended or complained about. Basically, the data scientists can “play” with the data using different algorithms to identify insights.
- </p>
- <h3><strong>Visualizing Results</strong></h3>
- <p>
- The Apache Spot (Incubating) GUI displays the results that the machine learning algorithms generate. Results are represented such that it is easy to identify both the most common things as well as find the most suspicious or uncommon information that is part of the data lake.
- </p>
- <h3><strong>Customizable Open Source</strong></h3>
- <p>
- Because Apache Spot (Incubating) is an open-source project, most of the components depicted here can be modified by the end user.
- </p>
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- About four years ago, the era of the Big Data analytics began. Paired with advanced analytics, massive volumes of data can be culled to not only inform critical decisions, but also to simulate sophisticated “what if” scenarios that allow companies to gain competitive advantages by generating and predicting different scenarios. For example, a financial services company can more accurately determine what other products to offer a customer, and in what order, based on a wide variety of data, then use advanced analytics to gather insights. Creating a data lake that can be effectively used for predictive analytics raises tough questions — what data sources should we use? How should this data be collected and ingested? What are the best algorithms to analyze the data, and how should we present these results to our decision maker?
- </p>
- <p>
- Apache Spot (Incubating) can help to solve most of these issues. Following is a description of the Apache Spot (Incubating), which is designed to facilitate Big Data analytics scenarios like the financial services company’s question about the right product to offer customers.
- </p>
- <a href="../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4.png"><img src="../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4.png" alt="oni_architecture-diagram_1300_v4" width="1300" height="675" class="alignnone size-full wp-image-114" srcset="../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4.png 1300w, ../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4-300x156.png 300w, ../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4-768x399.png 768w, ../wp-content/uploads/2016/09/ONI_Architecture-Diagram_1300_v4-1024x532.png 1024w" sizes="(max-width: 1300px) 100vw, 1300px" /></a>
- <h3><strong>Apache Spot (Incubating) Core Components</strong></h3>
- <p>
- The Apache Spot (Incubating) Core is composed of three main components — data integration (collectors), data store (HDFS here, but can also be a non-SQL database) and machine learning.
- </p>
- <p>
- In this diagram, the top left shows Apache Spot (Incubating) Data Sources, which include the collection of the information that will be used to create a data lake. The process is simple. Define a pull or push from the source of information then capture this information on Apache Spot (Incubating)’s “collectors.” The collectors are processes that interpret the information that is sent, then write it to the HDFS system in the Apache Spot (Incubating) cluster. The HDFS stores the data lake and ensures that resources can grow while remaining economical at every size. The Apache Spot (Incubating) algorithms are part of machine learning and are used to detect the uncommon information in the data lake.
- </p>
- <h3><strong>Operational Analytics</strong></h3>
- <p>
- As part of operational analytics, Apache Spot (Incubating) executes different batch processes that add information to machine learning results to provide meaning and context. Using the financial services product example, basic customer data could be augmented with information about other customers in the same region along with information about which products those customers recommended or complained about. Basically, the data scientists can “play” with the data using different algorithms to identify insights.
- </p>
- <h3><strong>Visualizing Results</strong></h3>
- <p>
- The Apache Spot (Incubating) GUI displays the results that the machine learning algorithms generate. Results are represented such that it is easy to identify both the most common things as well as find the most suspicious or uncommon information that is part of the data lake.
- </p>
- <h3><strong>Customizable Open Source</strong></h3>
- <p>
- Because Apache Spot (Incubating) is an open-source project, most of the components depicted here can be modified by the end user.
- </p>
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- <time class="updated entry-time" datetime="2016-09-22" itemprop="datePublished">
- September 22, 2016
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- <p>
- <strong>Why Does Apache Spot (Incubating) Include iPython notebooks? </strong>
- </p>
- <p>
- The project team wants Apache Spot (Incubating) to be a versatile tool that can be used by anyone. This means that data scientists and developers need to be able to query and handle the source data to find all the information they need for their decision making. The iPython Notebook is an appropriate platform for easy data exploration. One of its biggest advantages is that it provides parallel and distributed computing to enable code execution and debugging in an interactive environment – thus the ‘i’ in iPython.
- </p>
- <p>
- The iPython notebook is a web based interactive computational environment that provides access to the Python shell. While iPython notebooks were originally designed to work with the Python language, they support a number of other programming languages, including Ruby, Scala, Julia, R, Go, C, C++, Java and Perl. There are also multiple additional packages that can be used to get the most out of this highly-customizable tool.
- </p>
- <p>
- Starting on version 4.0, most notebook functionalities are now part of the Project Jupyter, while iPython remains as the kernel to work with Python code in the notebooks.
- </p>
- <img src="../wp-content/uploads/2016/10/iPython-1.png" alt="ipython" width="361" height="107" class="aligncenter size-full wp-image-140" srcset="../wp-content/uploads/2016/10/iPython-1.png 361w, ../wp-content/uploads/2016/10/iPython-1-300x89.png 300w" sizes="(max-width: 361px) 100vw, 361px" />
- <p>
- <strong>IPython with Apache Spot (Incubating) for Network Threat Detection</strong>
- </p>
- <p>
- <em>NOTE: This is not intended to be a step-by-step tutorial on how to code a threat analysis in Apache Spot (Incubating), but more like an introduction on how to approach the suspicions of a security breach.</em>
- </p>
- <p>
- Although machine learning (ML) will do most of the work detecting anomalies in the traffic, Apache Spot (Incubating) also includes two notebook templates that can get you started on this. The <em>Threat_Investigation_master.ipynb</em> is designed to query the raw data table to find all connections in a day that are related to any threat you select – even connections that were not necessarily flagged as suspicious by ML on a first run. This gives us the chance to get a new data subset and here is where the fun begins.
- </p>
- <p>
- If you suspect of a specific type of attack in your network, you can get the whole story by answering the Five ‘W’s.
- </p>
- <p>
- <strong><em>What? </em></strong>
- </p>
- <p>
- Maybe there’s been an increase in the logs collected by the system, which indicates abnormal amounts of communication in your network. Or, the amount of POST requests in your network have risen overnight. This is the mystery that needs to be solved by researching through the anomalies previously detected by ML.
- </p>
- <p>
- <strong><em>Who?</em></strong>
- </p>
- <p>
- Assuming you have a network context, you can identify the name of the infected machine inside the network, as well as the name of the IP or DNS on the other side of the connection (if it is a known host). If you don’t have a network context or are using DHCP, this can be a little tricky to detect using only Netflow logs. But, that’s where DNS and Proxy logs, come in handy. Including a network context file with Apache Spot (Incubating) is really simple and can go a long way when identifying a threat.
- </p>
- <p>
- <strong><em>When?</em></strong>
- </p>
- <p>
- To have a broader visibility on the attack, you can customize the queries on the Threat investigation notebook to review the data through a wider time lapse – instead of just checking through the current day. With this, you could find an increase of a certain type of requests to one (or many) URIs and predict its future behavior.
- </p>
- <p>
- <strong><em>Where?</em></strong>
- </p>
- <p>
- When working only with DNS, having a destination URL might not say much about where your information is going to, but Apache Spot (Incubating) allows you to connect with a geolocation database to identify the location of the suspected attackers IP. Taking advantage of this option, you can visually locate the other end of the connection on a map. You might find that it’s pointing to a country banned by your company, indicating a leak.
- </p>
- <p>
- <strong><em>Why?</em></strong>
- </p>
- <p>
- This answer to “why” will depend highly on the result of the analysis. For instance, an excessive amount of POST requests from one machine inside the network to an unidentified URI can indicate a data mining attack. Tracing back to patient zero, you can find that this could have originated with a phishing email, malicious software installed by an employee or a one-time visitor’s infected machine that connected to your network.
- </p>
- <p>
- <strong>How to Get Answers to the Five Ws Questions</strong>
- </p>
- <p>
- All of the previous questions can be answered by looking at the raw data collected. Although performing elaborated queries directly to your database can seem tempting, this type of analysis with Hive, or even Impala, can be very time consuming. A better approach would be to use Pandas to read and transform your dataset into a relational structured dataframe. This lets you work with as if it were an offline structured relational database.
- </p>
- <p>
- Once you have your desired results and data subsets, you can use MatplotLib to easily graph your findings. (We cover this subject in more depth in another post.) Another advantage of the notebook is that you can download it as HTML or a PDF file to store locally and use it in a presentation – or just keep it for future reference.
- </p>
- <p>
- <strong>Wrap Up</strong>
- </p>
- <p>
- This post was meant to be just a brief introduction of how you can use iPython notebooks in Apache Spot (Incubating) to perform further data analysis and include it our executive report (in addition to the already included Story board). Although this is not the only way you can do this, it is a very interactive and fun way to do it. You’ll also see that the overall processing time is very short – thanks to the iPython notebook task parallelism ability.
- </p>
- <p>
- We want to hear from YOU! Have you used iPython notebooks before? How do you feel about having this tool in Apache Spot (Incubating)? If you’re interested in further data analysis through interactive charts, a new post is coming soon on D3 and jQuery data visualization. Also, check back soon to read more on this and other Cybersecurity subjects.
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- filed under: <a href="../category/data-science/" rel="category tag">Data Science</a>, <a href="../category/ipython-notebooks/" rel="category tag">Ipython Notebooks</a>, <a href="../category/threat-analysis-tools/" rel="category tag">Threat Analysis Tools</a>
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- <a href="../how-apache-spot-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/">How Apache Spot (Incubating) (Incubating) Helps Create Well-Stocked Data Lakes and Catch Powerful Insights</a>
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- March 29, 2016
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- While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that effort, came Apache Spot. Since we started talking about the project, these are the three questions I am asked the most.
- </p>
- <p>
- <strong>What Is Apache Spot?</strong>
- <br />
- Apache Spot is an open source, flow and packet analytics solution built on Hadoop. It combines big data processing, at-scale machine learning, and unique security analytics to put potential threats in front of defenders. While I am a data scientist today, I was a security investigator just a few years ago. I wanted to develop a solution that would put new tools and technology in play for defenders, but without requiring them to walk away from security and get a math degree.
- </p>
- <p>
- We wanted to start with the hard problems, so we looked at the emerging need to analyze data that was produced at a scale outside what a lot of security solutions could handle. The data is being created today, and lack of visibility into that data gives attackers a profound advantage. Also, in this new era of security, many defenders (public and private sector) have to answer to their citizens and customers when these threats occur. In other words, an event that says “this attack was blocked” is insufficient; an organization needs to see what happened before, during, and after a particular machine was attacked at a particular time. The problem is summarized in a slide from a <a href="http://www.youtube.com/watch?v=mOZjMuBLYyM" target="_blank">FloCon talk</a>
- <br />
- <a href="../wp-content/uploads/2016/03/FloCon2015.png" rel="attachment wp-att-66"><img class="aligncenter size-full wp-image-66" src="../wp-content/uploads/2016/03/FloCon2015.png" alt="open source packet and flow analytics" width="960" height="720" srcset="../wp-content/uploads/2016/03/FloCon2015.png 960w, ../wp-content/uploads/2016/03/FloCon2015-300x225.png 300w, ../wp-content/uploads/2016/03/FloCon2015-768x576.png 768w" sizes="(max-width: 960px) 100vw, 960px" /></a>
- </p>
- <p>
- The gist is that while processing is a challenge at higher scales, the amount of insight gained is higher when analyzing flows and packets from key protocols (like DNS). And that’s how we got here.
- </p>
- <p>
- <strong>Why Intel?</strong>
- </p>
- <p>
- At Intel, I have worked in IT, for a security product company (McAfee), and in the Data Center Group. Intel IT was an early pioneer of the concept of proactive investigations to protect intellectual property. McAfee (now Intel Security Group) has a broad customer base in the realms of network, endpoint, and content security, to name only a few. And the Intel Data Center group has strategic partnerships with Cloudera and Accenture, as well as some pretty cool analytics efforts of their own. Add the performance benefits we achieve with Intel Architecture, especially the Intel MPI Library and Intel Math Kernel Library, and it certainly makes sense to me.
- </p>
- <p>
- <strong>Why Open Source?</strong>
- </p>
- <p>
- I learned from my earlier efforts in security analytics, that to invite collaboration from academia, the public sector, and the private sector, open source software is an excellent choice. We are now seeking to build a community of developers, data scientists, and security enthusiasts to grow Apache Spot into something we can all be proud of. We have also chosen an Apache software license, so that it can enrich commercial software offerings as well.
- </p>
- <p>
- The greatest thing for me since we announced at RSA is to hear OTHER people talk about Apache Spot (formerly Open Network Insight or ONI), here are some of my favorites, from <a href="http://vision.cloudera.com/open-network-insight-changing-infosec-data-science-forever/" target="_blank">a Data Scientist @ eBay </a>, <a href="https://newsroom.accenture.com/news/accenture-introduces-the-accenture-cyber-intelligence-platform-to-help-organizations-continuously-predict-detect-and-combat-cyber-attacks.htm" target="_blank">a Security Provider</a>, and <a href="http://vision.cloudera.com/adaptive-security-at-big-data-scale-for-next-generation-digital-security/" target="_blank">a Big Data company</a>.
- </p>
- <p>
- Fork us on Github!
- </p>
- <p>
- Grant Babb
- </p>
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- filed under: <a href="../category/security-analytics/" rel="category tag">Security Analytics</a>
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- <span class="tags-title">Tags:</span><a href="../tag/github/" rel="tag">github</a>, <a href="../tag/open-network-insight/" rel="tag">open network insight</a>, <a href="../tag/open-source/" rel="tag">open source</a>
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- <a href="../open-network-insight-oni-and-cybersecurity-using-netflows-to-detect-threats-to-critical-infrastructure/">Apache Spot and Cybersecurity — Using NetFlows to Detect Threats to Critical Infrastructure</a>
- </li>
- <li>
- <a href="../how-open-network-insight-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/">How Apache Spot Helps Create Well-Stocked Data Lakes and Catch Powerful Insights</a>
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- <time class="updated entry-time" datetime="2016-08-08" itemprop="datePublished">
- August 8, 2016
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- The “first” documented cybersecurity case was the worm replication, which was initiated by Robert T. Morris on November 2, 1988. Wow! Here we are in 2016, 28 years later, with viruses and worms giving way to Trojan horses and polymorphic code. Nowadays, we are also fighting against DDoS, phishing, spear phishing attacks, command and controls along with APTs such as Aurora, Zeus, Red October and Stuxnet. What happened with our security controls on each attack?
- </p>
- <p>
- Despite heroic efforts, internal and external security controls, no matter if they are preventive, detective or corrective, can be bypassed by different situations or misconfigurations. Capabilities to detect bugs or vulnerabilities in code, protocol, etc. are still limited. When we consider how to close these gaps, we have two options:
- </p>
- <ol>
- <li>
- Collect information from each device that is part of the environment.
- </li>
- <li>
- Collect information from the critical infrastructure that is used for most, if not all, of the systems.
- </li>
- </ol>
- <p>
- This blog will describe the second approach.
- </p>
- <p>
- Critical infrastructure, including nationally significant infrastructure, can be broadly defined as the systems, assets, facilities and networks that provide essential services. For nations, this means protecting the national security, economic security and prosperity as well as the health and safety of their citizenry. If we extrapolate this definition on the IT enterprise environments, we can define the critical infrastructure as the service, or services, that need to be up and running properly 99.99999% of the time, most of the time to support other critical infrastructure, such as databases, HR systems, manufacturing systems, etc.
- </p>
- <p>
- Effectively protecting critical infrastructure means that millions, if not billions, of different scenarios must be identified and monitored. To do this, the cybersecurity problem must be broken into small pieces.
- </p>
- <p>
- Let’s consider DNS — your communications to the Web. How can you know which communications are being established by your critical servers? And, what if you want to do it for most of your infrastructure?
- </p>
- <p>
- First idea: Use NetFlow, which is a network protocol that helps us collect IP traffic information and monitor network traffic. NetFlow has the details on the communications of all of your network traffic. However, the normal data on an enterprise environment includes billions of NetFlow events per day. To use this data to identify issues, it must be stored and analyzed. Storage alone is costly. Analyzing what amount Big Data stores is an entire other challenge.
- </p>
- <p>
- Apache Spot offers a solution. It was designed to gather, store and analyze Big Data. In fact, Apache Spot is an ideal solution for this cybersecurity challenge. Apache Spot can integrate many different data sources in a data lake then add operational context to the data by linking configuration, inventory, service databases and other data stores. This helps you to prioritize the actions to take under different attack, malware, APT and hacking scenarios. With Apache Spot, attacks that bypass our external or internal security controls can be identified. By delivering risk-prioritized, actionable insights, Apache Spot can support the growing need for security analytics.
- </p>
- <p>
- Not only can Apache Spot collect, store and analyze billions of NetFlow packets, but it can also be adapted to meet the unique requirements of your organization. How? Apache Spot is an open-source project.
- </p>
- <p>
- <strong>But Wait, There’s More</strong>
- </p>
- <p>
- Check out “<a href="../how-open-network-insight-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/"><u>How Apache Spot Helps Create Well-Stocked Data Lakes and Catch Powerful Insights</u></a>” to learn more about the underlying Apache Spot architecture.
- </p>
- <p>
- This is the first of a series of blogs that we will be writing about cybersecurity, so check back to read more.
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- </li>
- <li>
- <a href="../how-open-network-insight-helps-create-well-stocked-data-lakes-and-catch-powerful-insights/">How Apache Spot Helps Create Well-Stocked Data Lakes and Catch Powerful Insights</a>
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- By Rob Kent, Vice President of Marketing at Cybraics
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- Competition is widely considered to be a healthy and positive thing, traditionally viewed as providing options for customers and fueling innovation. In the world of cybersecurity there is no shortage of competition, in fact cybersecurity is one of the most crowded and fast-growing areas of technology. The problem is, with so much competition, are we losing sight of the real goal: protecting our customers against the adversary? With so much focus on competing and winning customers, are we negating one of the most fundamental advantages that we could have in the fight against cybercrime? Cooperation. Our adversaries are not shy about working together… the community is strong and growing, and while there is no doubt some healthy competition, the sharing of tools and techniques is certainly far more common than in the commercial world. Like Open Source software, security can only benefit and grow through peer-reviewed submissions. Citing Linus Law by Eric S. Raymond from his book The Cathedral and the Bazaar, “Given a large enough beta-tester and co-developer base, almost every problem will be characterized quickly and the fix will be obvious to someone.”
- </p>
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- This, of course, is not new information. People have been pointing this out for years. What is encouraging is that a few organizations are finally stepping up to the plate to try and change this paradigm in cybersecurity. When it comes to cybersecurity, many have touted big data analytics as one of the key initiatives to combat adversaries. The problem is that big data in itself has its own set of challenges, one of the issues is that it is often looked at as a costly problem to store and scale as opposed to being used as another tool in an organization’s arsenal. Apache Spot, a project pioneered by Intel and Cloudera, is aiming to fix this problem.
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- By creating an open source community, the hope is that identifying security threats within large data sets will become a manageable task for all organizations, despite their size or scale. Organizations overwhelmed with their data or those that aren’t seeing results will have a community to turn to along with a common reference to compare results. By working together on Apache Spot, organizations will be able to share their experiences on how they’ve tackled or how they need to tackle these issues with a common system to reference. Data is never going to get smaller across organizations’ core systems, so in many ways an organization just now beginning to dig into their data stores is basically starting from scratch. Having the ability to reference an open common model gives the opportunity for both those starting out and those seasoned, to have an open exchange of varying knowledge.
- </p>
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- By supporting Apache Spot, Cybraics is hoping to learn as much as we contribute while we collaborate and take part in this Open Source Initiative. With the end result a set of guidelines for organizations to deploy a data analytics platform to find threats within their ecosystem, and a community that will share their experiences with the next generation of data and security professionals, perhaps we can all start actually working together to start leveling the playing field.
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- <description><![CDATA[While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/apache-spot-3-most-asked-questions/" title="Read Apache Spot: Three Most-Asked Questions">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p>While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that effort, came Apache Spot. Since we started talking about the project, these are the three questions I am asked the most.</p>
-<p><strong>What Is Apache Spot?</strong><br />
-Apache Spot is an open source, flow and packet analytics solution built on Hadoop. It combines big data processing, at-scale machine learning, and unique security analytics to put potential threats in front of defenders. While I am a data scientist today, I was a security investigator just a few years ago. I wanted to develop a solution that would put new tools and technology in play for defenders, but without requiring them to walk away from security and get a math degree.</p>
-<p>We wanted to start with the hard problems, so we looked at the emerging need to analyze data that was produced at a scale outside what a lot of security solutions could handle. The data is being created today, and lack of visibility into that data gives attackers a profound advantage. Also, in this new era of security, many defenders (public and private sector) have to answer to their citizens and customers when these threats occur. In other words, an event that says “this attack was blocked” is insufficient; an organization needs to see what happened before, during, and after a particular machine was attacked at a particular time. The problem is summarized in a slide from a <a href="http://www.youtube.com/watch?v=mOZjMuBLYyM" target="_blank">FloCon talk</a><br />
-<a href="http://spot.incubator.apache.org/library/images/FloCon2015.png" rel="attachment wp-att-66"><img class="aligncenter size-full wp-image-66" src="http://spot.incubator.apache.org/library/images/FloCon2015.png" alt="open source packet and flow analytics" /></a></p>
-<p>The gist is that while processing is a challenge at higher scales, the amount of insight gained is higher when analyzing flows and packets from key protocols (like DNS). And that’s how we got here.</p>
-<p><strong>Why Intel?</strong></p>
-<p>At Intel, I have worked in IT, for a security product company (McAfee), and in the Data Center Group. Intel IT was an early pioneer of the concept of proactive investigations to protect intellectual property. McAfee (now Intel Security Group) has a broad customer base in the realms of network, endpoint, and content security, to name only a few. And the Intel Data Center group has strategic partnerships with Cloudera and Accenture, as well as some pretty cool analytics efforts of their own. Add the performance benefits we achieve with Intel Architecture, especially the Intel MPI Library and Intel Math Kernel Library, and it certainly makes sense to me.</p>
-<p><strong>Why Open Source?</strong></p>
-<p>I learned from my earlier efforts in security analytics, that to invite collaboration from academia, the public sector, and the private sector, open source software is an excellent choice. We are now seeking to build a community of developers, data scientists, and security enthusiasts to grow Apache Spot into something we can all be proud of. We have also chosen an Apache software license, so that it can enrich commercial software offerings as well.</p>
-<p>The greatest thing for me since we announced at RSA is to hear OTHER people talk about Apache Spot (formerly Open Network Insight or ONI), here are some of my favorites, from <a href="http://vision.cloudera.com/apache-spot-changing-infosec-data-science-forever/" target="_blank">a Data Scientist @ eBay </a>, <a href="https://newsroom.accenture.com/news/accenture-introduces-the-accenture-cyber-intelligence-platform-to-help-organizations-continuously-predict-detect-and-combat-cyber-attacks.htm" target="_blank">a Security Provider</a>, and <a href="http://vision.cloudera.com/adaptive-security-at-big-data-scale-for-next-generation-digital-security/" target="_blank">a Big Data company</a>.</p>
-<p>Fork us on Github!</p>
-<p>Grant Babb</p>
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- <pubDate>Tue, 29 Mar 2016 05:48:19 +0000</pubDate>
- <dc:creator><![CDATA[oni-admin]]></dc:creator>
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- <description><![CDATA[While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/apache-spot-3-most-asked-questions/" title="Read Apache Spot: Three Most-Asked Questions">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p>While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that effort, came Apache Spot. Since we started talking about the project, these are the three questions I am asked the most.</p>
-<p><strong>What Is Apache Spot?</strong><br />
-Apache Spot is an open source, flow and packet analytics solution built on Hadoop. It combines big data processing, at-scale machine learning, and unique security analytics to put potential threats in front of defenders. While I am a data scientist today, I was a security investigator just a few years ago. I wanted to develop a solution that would put new tools and technology in play for defenders, but without requiring them to walk away from security and get a math degree.</p>
-<p>We wanted to start with the hard problems, so we looked at the emerging need to analyze data that was produced at a scale outside what a lot of security solutions could handle. The data is being created today, and lack of visibility into that data gives attackers a profound advantage. Also, in this new era of security, many defenders (public and private sector) have to answer to their citizens and customers when these threats occur. In other words, an event that says “this attack was blocked” is insufficient; an organization needs to see what happened before, during, and after a particular machine was attacked at a particular time. The problem is summarized in a slide from a <a href="http://www.youtube.com/watch?v=mOZjMuBLYyM" target="_blank">FloCon talk</a><br />
-<a href="http://spot.incubator.apache.org/library/images/FloCon2015.png" rel="attachment wp-att-66"><img class="aligncenter size-full wp-image-66" src="http://spot.incubator.apache.org/library/images/FloCon2015.png" alt="open source packet and flow analytics" /></a></p>
-<p>The gist is that while processing is a challenge at higher scales, the amount of insight gained is higher when analyzing flows and packets from key protocols (like DNS). And that’s how we got here.</p>
-<p><strong>Why Intel?</strong></p>
-<p>At Intel, I have worked in IT, for a security product company (McAfee), and in the Data Center Group. Intel IT was an early pioneer of the concept of proactive investigations to protect intellectual property. McAfee (now Intel Security Group) has a broad customer base in the realms of network, endpoint, and content security, to name only a few. And the Intel Data Center group has strategic partnerships with Cloudera and Accenture, as well as some pretty cool analytics efforts of their own. Add the performance benefits we achieve with Intel Architecture, especially the Intel MPI Library and Intel Math Kernel Library, and it certainly makes sense to me.</p>
-<p><strong>Why Open Source?</strong></p>
-<p>I learned from my earlier efforts in security analytics, that to invite collaboration from academia, the public sector, and the private sector, open source software is an excellent choice. We are now seeking to build a community of developers, data scientists, and security enthusiasts to grow Apache Spot into something we can all be proud of. We have also chosen an Apache software license, so that it can enrich commercial software offerings as well.</p>
-<p>The greatest thing for me since we announced at RSA is to hear OTHER people talk about Apache Spot (formerly Open Network Insight or ONI), here are some of my favorites, from <a href="http://vision.cloudera.com/apache-spot-changing-infosec-data-science-forever/" target="_blank">a Data Scientist @ eBay </a>, <a href="https://newsroom.accenture.com/news/accenture-introduces-the-accenture-cyber-intelligence-platform-to-help-organizations-continuously-predict-detect-and-combat-cyber-attacks.htm" target="_blank">a Security Provider</a>, and <a href="http://vision.cloudera.com/adaptive-security-at-big-data-scale-for-next-generation-digital-security/" target="_blank">a Big Data company</a>.</p>
-<p>Fork us on Github!</p>
-<p>Grant Babb</p>
-]]></content:encoded>
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- <h3 class="h2 entry-title"><a href="../../blog/apache-spot-3-most-asked-questions/" rel="bookmark" title="Apache Spot: Three Most-Asked Questions">Apache Spot (Incubating): Three Most-Asked Questions</a></h3>
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- While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that… <a class="excerpt-read-more" href="../../blog/apache-spot-3-most-asked-questions/" title="Read Apache Spot: Three Most-Asked Questions">Read more »</a>
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- <description><![CDATA[While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that... <a class="excerpt-read-more" href="http://spot.incubator.apache.org/blog/open-network-insight-3-most-asked-questions/" title="Read Apache Spot: Three Most-Asked Questions">Read more »</a>]]></description>
- <content:encoded><![CDATA[<p>While this is not the first blog post about Apache Spot, it is the first one by a creator of the solution. As a security data scientist in Intel’s Data Center Group, I joined a small team to start thinking about solving really hard problems in cloud analytics. The team grew, and out of that effort, came Apache Spot. Since we started talking about the project, these are the three questions I am asked the most.</p>
-<p><strong>What Is Apache Spot?</strong><br />
-Apache Spot is an open source, flow and packet analytics solution built on Hadoop. It combines big data processing, at-scale machine learning, and unique security analytics to put potential threats in front of defenders. While I am a data scientist today, I was a security investigator just a few years ago. I wanted to develop a solution that would put new tools and technology in play for defenders, but without requiring them to walk away from security and get a math degree.</p>
-<p>We wanted to start with the hard problems, so we looked at the emerging need to analyze data that was produced at a scale outside what a lot of security solutions could handle. The data is being created today, and lack of visibility into that data gives attackers a profound advantage. Also, in this new era of security, many defenders (public and private sector) have to answer to their citizens and customers when these threats occur. In other words, an event that says “this attack was blocked” is insufficient; an organization needs to see what happened before, during, and after a particular machine was attacked at a particular time. The problem is summarized in a slide from a <a href="http://www.youtube.com/watch?v=mOZjMuBLYyM" target="_blank">FloCon talk</a><br />
-<a href="http://spot.incubator.apache.org/library/images/FloCon2015.png" rel="attachment wp-att-66"><img class="aligncenter size-full wp-image-66" src="http://spot.incubator.apache.org/library/images/FloCon2015.png" alt="open source packet and flow analytics" /></a></p>
-<p>The gist is that while processing is a challenge at higher scales, the amount of insight gained is higher when analyzing flows and packets from key protocols (like DNS). And that’s how we got here.</p>
-<p><strong>Why Intel?</strong></p>
-<p>At Intel, I have worked in IT, for a security product company (McAfee), and in the Data Center Group. Intel IT was an early pioneer of the concept of proactive investigations to protect intellectual property. McAfee (now Intel Security Group) has a broad customer base in the realms of network, endpoint, and content security, to name only a few. And the Intel Data Center group has strategic partnerships with Cloudera and Accenture, as well as some pretty cool analytics efforts of their own. Add the performance benefits we achieve with Intel Architecture, especially the Intel MPI Library and Intel Math Kernel Library, and it certainly makes sense to me.</p>
-<p><strong>Why Open Source?</strong></p>
-<p>I learned from my earlier efforts in security analytics, that to invite collaboration from academia, the public sector, and the private sector, open source software is an excellent choice. We are now seeking to build a community of developers, data scientists, and security enthusiasts to grow Apache Spot into something we can all be proud of. We have also chosen an Apache software license, so that it can enrich commercial software offerings as well.</p>
-<p>The greatest thing for me since we announced at RSA is to hear OTHER people talk about Apache Spot (formerly Open Network Insight or ONI), here are some of my favorites, from <a href="http://vision.cloudera.com/open-network-insight-changing-infosec-data-science-forever/" target="_blank">a Data Scientist @ eBay </a>, <a href="https://newsroom.accenture.com/news/accenture-introduces-the-accenture-cyber-intelligence-platform-to-help-organizations-continuously-predict-detect-and-combat-cyber-attacks.htm" target="_blank">a Security Provider</a>, and <a href="http://vision.cloudera.com/adaptive-security-at-big-data-scale-for-next-generation-digital-security/" target="_blank">a Big Data company</a>.</p>
-<p>Fork us on Github!</p>
-<p>Grant Babb</p>
-]]></content:encoded>
- <wfw:commentRss>http://spot.incubator.apache.org/blog/open-network-insight-3-most-asked-questions/feed/</wfw:commentRss>
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-
-/** 1. Address `appearance` set to `searchfield` in Safari 5 and Chrome. 2. Address `box-sizing` set to `border-box` in Safari 5 and Chrome (include `-moz` to future-proof). */
-input[type="search"] { -webkit-appearance: textfield; /* 1 */ -moz-box-sizing: content-box; -webkit-box-sizing: content-box; /* 2 */ box-sizing: content-box; }
-
-/** Remove inner padding and search cancel button in Safari 5 and Chrome on OS X. */
-input[type="search"]::-webkit-search-cancel-button, input[type="search"]::-webkit-search-decoration { -webkit-appearance: none; }
-
-/** Remove inner padding and border in Firefox 4+. */
-button::-moz-focus-inner, input::-moz-focus-inner { border: 0; padding: 0; }
-
-/** 1. Remove default vertical scrollbar in IE 8/9. 2. Improve readability and alignment in all browsers. */
-textarea { overflow: auto; /* 1 */ vertical-align: top; /* 2 */ }
-
-/* ========================================================================== Tables ========================================================================== */
-/** Remove most spacing between table cells. */
-table { border-collapse: collapse; border-spacing: 0; }
-
-* { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; }
-
-.image-replacement, .ir { text-indent: 100%; white-space: nowrap; overflow: hidden; }
-
-.clearfix, .cf, .comment-respond { zoom: 1; }
-.clearfix:before, .clearfix:after, .cf:before, .comment-respond:before, .cf:after, .comment-respond:after { content: ""; display: table; }
-.clearfix:after, .cf:after, .comment-respond:after { clear: both; }
-
-/*
-use the best ampersand
-http://simplebits.com/notebook/2008/08/14/ampersands-2/
-*/
-span.amp { font-family: Baskerville,'Goudy Old Style',Palatino,'Book Antiqua',serif !important; font-style: italic; }
-
-/******************************************************************
-Site Name:
-Author:
-
-Stylesheet: Variables
-
-Here is where we declare all our variables like colors, fonts,
-base values, and defaults. We want to make sure this file ONLY
-contains variables that way our files don't get all messy.
-No one likes a mess.
-
-******************************************************************/
-/*********************
-COLORS
-Need help w/ choosing your colors? Try this site out:
-http://0to255.com/
-*********************/
-/*
-Here's a great tutorial on how to
-use color variables properly:
-http://sachagreif.com/sass-color-variables/
-*/
-/******************************************************************
-Site Name:
-Author:
-
-Stylesheet: Typography
-
-Need to import a font or set of icons for your site? Drop them in
-here or just use this to establish your typographical grid. Or not.
-Do whatever you want to...GOSH!
-
-Helpful Articles:
-http://trentwalton.com/2012/06/19/fluid-type/
-http://ia.net/blog/responsive-typography-the-basics/
-http://alistapart.com/column/responsive-typography-is-a-physical-discipline
-
-******************************************************************/
-/*********************
-FONT FACE (IN YOUR FACE)
-*********************/
-/* To embed your own fonts, use this syntax and place your fonts inside the library/fonts folder. For more information on embedding fonts, go to: http://www.fontsquirrel.com/ Be sure to remove the comment brackets.
-*/
-/* @font-face { font-family: 'Font Name'; src: url('../css/library/fonts/font-name.eot'); src: url('../css/library/fonts/font-name.eot#iefix') format('embedded-opentype'), url('../css/library/fonts/font-name.woff') format('woff'), url('../css/library/fonts/font-name.ttf') format('truetype'), url('../css/library/fonts/font-name.svg#font-name') format('svg'); font-weight: normal; font-style: normal; }
-*/
-/*
-The following is based of Typebase:
-https://github.com/devinhunt/typebase.css
-I've edited it a bit, but it's a nice starting point.
-*/
-/* i imported this one in the functions file so bones would look sweet. don't forget to remove it for your site.
-*/
-/*
-some nice typographical defaults
-more here: http://www.newnet-soft.com/blog/csstypography
-*/
-p { -ms-word-wrap: break-word; word-break: break-word; word-wrap: break-word; -webkit-hyphens: auto; -moz-hyphens: auto; hyphens: auto; -webkit-hyphenate-before: 2; -webkit-hyphenate-after: 3; hyphenate-lines: 3; -webkit-font-feature-settings: "liga", "dlig"; -moz-font-feature-settings: "liga=1, dlig=1"; -ms-font-feature-settings: "liga", "dlig"; -o-font-feature-settings: "liga", "dlig"; font-feature-settings: "liga", "dlig"; }
-
-/******************************************************************
-Site Name:
-Author:
-
-Stylesheet: Sass Functions
-
-You can do a lot of really cool things in Sass. Functions help you
-make repeated actions a lot easier. They are really similar to mixins,
-but can be used for so much more.
-
-Anyway, keep them all in here so it's easier to find when you're
-looking for one.
-
-For more info on functions, go here:
-http://sass-lang.com/documentation/Sass/Script/Functions.html
-
-******************************************************************/
-/*********************
-COLOR FUNCTIONS
-These are helpful when you're working
-with shadows and such things. It's essentially
-a quicker way to write RGBA.
-
-Example:
-box-shadow: 0 0 4px black(0.3);
-compiles to:
-box-shadow: 0 0 4px rgba(0,0,0,0.3);
-*********************/
-/*********************
-RESPONSIVE HELPER FUNCTION
-If you're creating a responsive site, then
-you've probably already read
-Responsive Web Design: http://www.abookapart.com/products/responsive-web-design
-
-Here's a nice little helper function for calculating
-target / context
-as mentioned in that book.
-
-Example:
-width: cp(650px, 1000px);
-or
-width: calc-percent(650px, 1000px);
-both compile to:
-width: 65%;
-*********************/
-/******************************************************************
-Site Name:
-Author:
-
-Stylesheet: Mixins Stylesheet
-
-This is where you can take advantage of Sass' great features: Mixins.
-I won't go in-depth on how they work exactly,
-there are a few articles below that will help do that. What I will
-tell you is that this will help speed up simple changes like
-changing a color or adding CSS3 techniques gradients.
-
-A WORD OF WARNING: It's very easy to overdo it here. Be careful and
-remember less is more.
-
-Helpful:
-http://sachagreif.com/useful-sass-mixins/
-http://thesassway.com/intermediate/leveraging-sass-mixins-for-cleaner-code
-http://web-design-weekly.com/blog/2013/05/12/handy-sass-mixins/
-
-******************************************************************/
-/*********************
-TRANSITION
-*********************/
-/*
-I totally rewrote this to be cleaner and easier to use.
-You'll need to be using Sass 3.2+ for these to work.
-Thanks to @anthonyshort for the inspiration on these.
-USAGE: @include transition(all 0.2s ease-in-out);
-*/
-/*********************
-CSS3 GRADIENTS
-Be careful with these since they can
-really slow down your CSS. Don't overdo it.
-*********************/
-/* @include css-gradient(#dfdfdf,#f8f8f8); */
-/*********************
-BOX SIZING
-*********************/
-/* @include box-sizing(border-box); */
-/* NOTE: value of "padding-box" is only supported in Gecko. So
-probably best not to use it. I mean, were you going to anyway? */
-/******************************************************************
-Site Name:
-Author:
-
-Stylesheet: Grid Stylesheet
-
-I've seperated the grid so you can swap it out easily. It's
-called at the top the style.scss stylesheet.
-
-There are a ton of grid solutions out there. You should definitely
-experiment with your own. Here are some recommendations:
-
-http://gridsetapp.com - Love this site. Responsive Grids made easy.
-http://susy.oddbird.net/ - Grids using Compass. Very elegant.
-http://gridpak.com/ - Create your own responsive grid.
-https://github.com/dope/lemonade - Neat lightweight grid.
-
-
-The grid below is a custom built thingy I modeled a bit after
-Gridset. It's VERY basic and probably shouldn't be used on
-your client projects. The idea is you learn how to roll your
-own grids. It's better in the long run and allows you full control
-over your project's layout.
-
-******************************************************************/
-.last-col { float: right; padding-right: 0 !important; }
-
-/*
-Mobile Grid Styles
-These are the widths for the mobile grid.
-There are four types, but you can add or customize
-them however you see fit.
-*/
-@media (max-width: 767px) { .m-all { float: left; padding-right: 0.75em; width: 100%; padding-right: 0; }
- .m-1of2 { float: left; padding-right: 0.75em; width: 50%; }
- .m-1of3 { float: left; padding-right: 0.75em; width: 33.33%; }
- .m-2of3 { float: left; padding-right: 0.75em; width: 66.66%; }
- .m-1of4 { float: left; padding-right: 0.75em; width: 25%; }
- .m-3of4 { float: left; padding-right: 0.75em; width: 75%; } }
-/* Portrait tablet to landscape */
-@media (min-width: 768px) and (max-width: 1029px) { .t-all { float: left; padding-right: 0.75em; width: 100%; padding-right: 0; }
- .t-1of2 { float: left; padding-right: 0.75em; width: 50%; }
- .t-1of3 { float: left; padding-right: 0.75em; width: 33.33%; }
- .t-2of3 { float: left; padding-right: 0.75em; width: 66.66%; }
- .t-1of4 { float: left; padding-right: 0.75em; width: 25%; }
- .t-3of4 { float: left; padding-right: 0.75em; width: 75%; }
- .t-1of5 { float: left; padding-right: 0.75em; width: 20%; }
- .t-2of5 { float: left; padding-right: 0.75em; width: 40%; }
- .t-3of5 { float: left; padding-right: 0.75em; width: 60%; }
- .t-4of5 { float: left; padding-right: 0.75em; width: 80%; } }
-/* Landscape to small desktop */
-@media (min-width: 1030px) { .d-all { float: left; padding-right: 0.75em; width: 100%; padding-right: 0; }
- .d-1of2 { float: left; padding-right: 0.75em; width: 50%; }
- .d-1of3 { float: left; padding-right: 0.75em; width: 33.33%; }
- .d-2of3 { float: left; padding-right: 0.75em; width: 66.66%; }
- .d-1of4 { float: left; padding-right: 0.75em; width: 25%; }
- .d-3of4 { float: left; padding-right: 0.75em; width: 75%; }
- .d-1of5 { float: left; padding-right: 0.75em; width: 20%; }
- .d-2of5 { float: left; padding-right: 0.75em; width: 40%; }
- .d-3of5 { float: left; padding-right: 0.75em; width: 60%; }
- .d-4of5 { float: left; padding-right: 0.75em; width: 80%; }
- .d-1of6 { float: left; padding-right: 0.75em; width: 16.6666666667%; }
- .d-1of7 { float: left; padding-right: 0.75em; width: 14.2857142857%; }
- .d-2of7 { float: left; padding-right: 0.75em; width: 28.5714286%; }
- .d-3of7 { float: left; padding-right: 0.75em; width: 42.8571429%; }
- .d-4of7 { float: left; padding-right: 0.75em; width: 57.1428572%; }
- .d-5of7 { float: left; padding-right: 0.75em; width: 71.4285715%; }
- .d-6of7 { float: left; padding-right: 0.75em; width: 85.7142857%; }
- .d-1of8 { float: left; padding-right: 0.75em; width: 12.5%; }
- .d-1of9 { float: left; padding-right: 0.75em; width: 11.1111111111%; }
- .d-1of10 { float: left; padding-right: 0.75em; width: 10%; }
- .d-1of11 { float: left; padding-right: 0.75em; width: 9.09090909091%; }
- .d-1of12 { float: left; padding-right: 0.75em; width: 8.33%; } }
-/*********************
-IMPORTING MODULES
-Modules are reusable blocks or elements we use throughout the project.
-We can break them up as much as we want or just keep them all in one.
-I mean, you can do whatever you want. The world is your oyster. Unless
-you hate oysters, then the world is your peanut butter & jelly sandwich.
-*********************/
-/******************************************************************
-Site Name:
-Author:
-
-Stylesheet: Alert Styles
-
-If you want to use these alerts in your design, you can. If not,
-you can just remove this stylesheet.
-
-******************************************************************/
-.alert-help, .alert-info, .alert-error, .alert-success { margin: 10px; padding: 5px 18px; border: 1px solid; }
-
-.alert-help { border-color: #e8dc59; background: #ebe16f; }
-
-.alert-info { border-color: #bfe4f4; background: #d5edf8; }
-
-.alert-error { border-color: #f8cdce; background: #fbe3e4; }
-
-.alert-success { border-color: #deeaae; background: #e6efc2; }
-
-/******************************************************************
-Site Name:
-Author:
-
-Stylesheet: Button Styles
-
-Buttons are a pretty important part of your site's style, so it's
-important to have a consistent baseline for them. Use this stylesheet
-to create all kinds of buttons.
-
-Helpful Links:
-http://galp.in/blog/2011/08/02/the-ui-guide-part-1-buttons/
-
-******************************************************************/
-/*********************
-BUTTON DEFAULTS
-We're gonna use a placeholder selector here
-so we can use common styles. We then use this
-to load up the defaults in all our buttons.
-
-Here's a quick video to show how it works:
-http://www.youtube.com/watch?v=hwdVpKiJzac
-
-*********************/
-.blue-btn, .comment-reply-link, #submit { display: inline-block; position: relative; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; text-decoration: none; color: white; font-size: 0.9em; font-size: 34px; line-height: 34px; font-weight: normal; padding: 0 24px; border-radius: 4px; border: 0; cursor: pointer; -webkit-transition: background-color 0.14s ease-in-out; transition: background-color 0.14s ease-in-out; }
-.blue-btn:hover, .comment-reply-link:hover, #submit:hover, .blue-btn:focus, .comment-reply-link:focus, #submit:focus { color: white; text-decoration: none; outline: none; }
-.blue-btn:active, .comment-reply-link:active, #submit:active { top: 1px; }
-
-/*
-An example button.
-You can use this example if you want. Just replace all the variables
-and it will create a button dependant on those variables.
-*/
-.blue-btn, .comment-reply-link, #submit { background-color: #2980b9; }
-.blue-btn:hover, .comment-reply-link:hover, #submit:hover, .blue-btn:focus, .comment-reply-link:focus, #submit:focus { background-color: #2574a8; }
-.blue-btn:active, .comment-reply-link:active, #submit:active { background-color: #2472a4; }
-
-.y-btn, .y-btn:visited { background: transparent; padding: 10px 35px; border: 2px solid #f9bf12; color: #f9bf12; text-decoration: none; font-weight: bold; font-family: "Roboto", "Helvetica Neue", Helvetica, Arial, sans-serif; text-transform: uppercase; letter-spacing: 1px; font-size: 10pt; -webkit-transition: all .3s ease; -moz-transition: all .3s ease; -ms-transition: all .3s ease; -o-transition: all .3s ease; transition: all .3s ease; display:block;margin:0 auto 15px;max-width:225px;}
-.y-btn:hover, .y-btn:focus, .y-btn:active, .y-btn:visited:hover, .y-btn:visited:focus, .y-btn:visited:active { background: #f9bf12; color: white; }
-
-.btn-margin { margin-top: 50px; }
-
-/******************************************************************
-Site Name:
-Author:
-
-Stylesheet: Form Styles
-
-We put all the form and button styles in here to setup a consistent
-look. If we need to customize them, we can do this in the main
-stylesheets and just override them. Easy Peasy.
-
-You're gonna see a few data-uri thingies down there. If you're not
-sure what they are, check this link out:
-http://css-tricks.com/data-uris/
-If you want to create your own, use this helpful link:
-http://websemantics.co.uk/online_tools/image_to_data_uri_convertor/
-
-******************************************************************/
-/*********************
-INPUTS
-*********************/
-input[type="text"], input[type="password"], input[type="datetime"], input[type="datetime-local"], input[type="date"], input[type="month"], input[type="time"], input[type="week"], input[type="number"], input[type="email"], input[type="url"], input[type="search"], input[type="tel"], input[type="color"], select, textarea, .field { display: block; height: 40px; line-height: 1em; padding: 0 12px; margin-bottom: 14px; font-size: 1em; color: #5c6b80; border-radius: 3px; vertical-align: middle; box-shadow: none; border: 0; width: 100%; max-width: 400px; font-family: "Lato", "Helvetica Neue", Helvetica, Arial, sans-serif; background-color: white; -webkit-transition: background-color 0.24s ease-in-out; transition: background-color 0.24s ease-in-out; }
-input[type="text"]:focus, input[type="text"]:active, input[type="password"]:focus, input[type="password"]:active, input[type="datetime"]:focus, input[type="datetime"]:active, input[type="datetime-local"]:focus, input[type="datetime-local"]:active, input[type="date"]:focus, input[type="date"]:active, input[type="month"]:focus, input[type="month"]:active, input[type="time"]:focus, input[type="time"]:active, input[type="week"]:focus, input[type="week"]:active, input[type="number"]:focus, input[type="number"]:active, input[type="email"]:focus, input[type="email"]:active, input[type="url"]:focus, input[type="url"]:active, input[type="search"]:focus, input[type="search"]:active, input[type="tel"]:focus, input[type="tel"]:active, input[type="color"]:focus, input[type="color"]:active, select:focus, select:active, textarea:focus, textarea:active, .field:focus, .field:active { background-color: #fafafa; outline: none; }
-input[type="text"].error, input[type="text"].is-invalid, input[type="password"].error, input[type="password"].is-invalid, input[type="datetime"].error, input[type="datetime"].is-invalid, input[type="datetime-local"].error, input[type="datetime-local"].is-invalid, input[type="date"].error, input[type="date"].is-invalid, input[type="month"].error, input[type="month"].is-invalid, input[type="time"].error, input[type="time"].is-invalid, input[type="week"].error, input[type="week"].is-invalid, input[type="number"].error, input[type="number"].is-invalid, input[type="email"].error, input[type="email"].is-invalid, input[type="url"].error, input[type="url"].is-invalid, input[type="search"].error, input[type="search"].is-invalid, input[type="tel"].error, input[type="tel"].is-invalid, input[type="color"].error, input[type="color"].is-invalid, select.error, select.is-invalid, textarea.error, textarea.is-invalid, .field.error, .field.is-invalid { color: #fbe3e4; border-color: #fbe3e4; background-color: white; background-position: 99% center; background-repeat: no-repeat; background-image: url(data:image/png;base64,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); outline-color: #fbe3e4; }
-input[type="text"].success, input[type="text"].is-valid, input[type="password"].success, input[type="password"].is-valid, input[type="datetime"].success, input[type="datetime"].is-valid, input[type="datetime-local"].success, input[type="datetime-local"].is-valid, input[type="date"].success, input[type="date"].is-valid, input[type="month"].success, input[type="month"].is-valid, input[type="time"].success, input[type="time"].is-valid, input[type="week"].success, input[type="week"].is-valid, input[type="number"].success, input[type="number"].is-valid, input[type="email"].success, input[type="email"].is-valid, input[type="url"].success, input[type="url"].is-valid, input[type="search"].success, input[type="search"].is-valid, input[type="tel"].success, input[type="tel"].is-valid, input[type="color"].success, input[type="color"].is-valid, select.success, select.is-valid, textarea.success, textarea.is-valid, .field.success, .field.is-valid { color: #e6efc2; border-color: #e6efc2; background-color: white; background-position: 99% center; background-repeat: no-repeat; background-image: url(data:image/png;base64,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); outline-color: #e6efc2; }
-input[type="text"][disabled], input[type="text"].is-disabled, input[type="password"][disabled], input[type="password"].is-disabled, input[type="datetime"][disabled], input[type="datetime"].is-disabled, input[type="datetime-local"][disabled], input[type="datetime-local"].is-disabled, input[type="date"][disabled], input[type="date"].is-disabled, input[type="month"][disabled], input[type="month"].is-disabled, input[type="time"][disabled], input[type="time"].is-disabled, input[type="week"][disabled], input[type="week"].is-disabled, input[type="number"][disabled], input[type="number"].is-disabled, input[type="email"][disabled], input[type="email"].is-disabled, input[type="url"][disabled], input[type="url"].is-disabled, input[type="search"][disabled], input[type="search"].is-disabled, input[type="tel"][disabled], input[type="tel"].is-disabled, input[type="color"][disabled], input[type="color"].is-disabled, select[disabled], select.is-disabled, textarea[disabled], textarea.is-disabled, .field[disabled], .field.is-disabled { cursor: not-allowed; border-color: #cfcfcf; opacity: 0.6; }
-input[type="text"][disabled]:focus, input[type="text"][disabled]:active, input[type="text"].is-disabled:focus, input[type="text"].is-disabled:active, input[type="password"][disabled]:focus, input[type="password"][disabled]:active, input[type="password"].is-disabled:focus, input[type="password"].is-disabled:active, input[type="datetime"][disabled]:focus, input[type="datetime"][disabled]:active, input[type="datetime"].is-disabled:focus, input[type="datetime"].is-disabled:active, input[type="datetime-local"][disabled]:focus, input[type="datetime-local"][disabled]:active, input[type="datetime-local"].is-disabled:focus, input[type="datetime-local"].is-disabled:active, input[type="date"][disabled]:focus, input[type="date"][disabled]:active, input[type="date"].is-disabled:focus, input[type="date"].is-disabled:active, input[type="month"][disabled]:focus, input[type="month"][disabled]:active, input[type="month"].is-disabled:focus, input[type="month"].is-disabled:active, input[type="time"][disabled]:focus, input[type="time"][disabled]:active, input[type="time"].is-disabled:focus, input[type="time"].is-disabled:active, input[type="week"][disabled]:focus, input[type="week"][disabled]:active, input[type="week"].is-disabled:focus, input[type="week"].is-disabled:active, input[type="number"][disabled]:focus, input[type="number"][disabled]:active, input[type="number"].is-disabled:focus, input[type="number"].is-disabled:active, input[type="email"][disabled]:focus, input[type="email"][disabled]:active, input[type="email"].is-disabled:focus, input[type="email"].is-disabled:active, input[type="url"][disabled]:focus, input[type="url"][disabled]:active, input[type="url"].is-disabled:focus, input[type="url"].is-disabled:active, input[type="search"][disabled]:focus, input[type="search"][disabled]:active, input[type="search"].is-disabled:focus, input[type="search"].is-disabled:active, input[type="tel"][disabled]:focus, input[type="tel"][disabled]:active, input[type="tel"].is-disabled:focus, input[type="tel"].is-disabled:active, input[type="color"][disabled]:focus, input[type="color"][disabled]:active, input[type="color"].is-disabled:focus, input[type="color"].is-disabled:active, select[disabled]:focus, select[disabled]:active, select.is-disabled:focus, select.is-disabled:active, textarea[disabled]:focus, textarea[disabled]:active, textarea.is-disabled:focus, textarea.is-disabled:active, .field[disabled]:focus, .field[disabled]:active, .field.is-disabled:focus, .field.is-disabled:active { background-color: #d5edf8; }
-
-input[type="password"] { letter-spacing: 0.3em; }
-
-textarea { max-width: 100%; min-height: 120px; line-height: 1.5em; }
-
-select { -webkit-appearance: none; /* 1 */ -moz-appearance: none; appearance: none; background-image: url(data:image/png;base64,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); background-repeat: no-repeat; background-position: 97.5% center; }
-
-form#searchform input#searchsubmit { background: transparent url(../images/search.png) top left no-repeat; width: 50px; padding-left: 5px; display: block; overflow: hidden; /* Hack for IE */ text-indent: -9999px; font-size: 0px; line-height: 0px; }
-
-/*********************
-BASE (MOBILE) SIZE
-This are the mobile styles. It's what people see on their phones. If
-you set a great foundation, you won't need to add too many styles in
-the other stylesheets. Remember, keep it light: Speed is Important.
-*********************/
-/******************************************************************
-Site Name:
-Author:
-
-Stylesheet: Base Mobile Stylesheet
-
-Be light and don't over style since everything here will be
-loaded by mobile devices. You want to keep it as minimal as
-possible. This is called at the top of the main stylsheet
-and will be used across all viewports.
-
-******************************************************************/
-/*********************
-GENERAL STYLES
-*********************/
-body { font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 100%; line-height: 1.5; color: #5c6b80; background-color: #f7f7f7; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; -webkit-hyphens: none; -epub-hyphens: none; -moz-hyphens: none; hyphens: none; }
-
-p { -webkit-hyphens: none; -epub-hyphens: none; -moz-hyphens: none; hyphens: none; letter-spacing: 1px; line-height: 1.8em; font-size: 11pt; }
-
-img { max-width: 100%; height: auto; }
-
-.header { box-shadow: 0 0 10px rgba(0, 0, 0, 0.25); -webkit-box-shadow: 0 0 10px rgba(0, 0, 0, 0.25); -moz-box-shadow: 0 0 10px rgba(0, 0, 0, 0.25); }
-
-/********************
-WORDPRESS BODY CLASSES
-style a page via class
-********************/
-/* for sites that are read right to left (i.e. hebrew) */
-/* home page */
-/* blog template page */
-/* archive page */
-/* date archive page */
-/* replace the number to the corresponding page number */
-/* search page */
-/* search result page */
-/* no results search page */
-/* individual paged search (i.e. body.search-paged-3) */
-/* 404 page */
-/* single post page */
-/* individual post page by id (i.e. body.postid-73) */
-/* individual paged single (i.e. body.single-paged-3) */
-/* attatchment page */
-/* individual attatchment page (i.e. body.attachmentid-763) */
-/* style mime type pages */
-/* author page */
-/* user nicename (i.e. body.author-samueladams) */
-/* paged author archives (i.e. body.author-paged-4) for page 4 */
-/* category page */
-/* individual category page (i.e. body.category-6) */
-/* replace the number to the corresponding page number */
-/* tag page */
-/* individual tag page (i.e. body.tag-news) */
-/* replace the number to the corresponding page number */
-/* custom page template page */
-/* individual page template (i.e. body.page-template-contact-php */
-/* replace the number to the corresponding page number */
-/* parent page template */
-/* child page template */
-/* replace the number to the corresponding page number */
-/* if user is logged in */
-/* paged items like search results or archives */
-/* individual paged (i.e. body.paged-3) */
-/*********************
-LAYOUT & GRID STYLES
-*********************/
-.wrap { width: 85%; margin: 0 auto; }
-
-.center { text-align: center; }
-
-.table { display: table; border-collapse: separate; }
-
-.table-row { display: table-row; }
-
-.table-cell { display: table-cell; vertical-align: middle; }
-
-.table-cell.quote-man {padding-right:35px;text-align:center;min-width:140px;}
-
-.disclaimer {color:#fff;font-size:.7em;line-height:1.3em;}
-
-/*********************
-LINK STYLES
-*********************/
-a, a:visited { color: #f9bf12; -webkit-transition: color .3s ease; -moz-transition: color .3s ease; -ms-transition: color .3s ease; -o-transition: color .3s ease; transition: color .3s ease; text-decoration: none; /* on hover */ /* on click */ /* mobile tap color */ }
-a:hover, a:focus, a:visited:hover, a:visited:focus { color: #3e3e40; }
-a:link, a:visited:link { /*
-this highlights links on iPhones/iPads.
-so it basically works like the :hover selector
-for mobile devices.
-*/ -webkit-tap-highlight-color: rgba(0, 0, 0, 0.3); }
-
-/******************************************************************
-H1, H2, H3, H4, H5 STYLES
-******************************************************************/
-h1, .h1, h2, .h2, h3, .h3, h4, .h4, h5, .h5 { text-rendering: optimizelegibility; font-weight: 500; color: #f9bf12; /*
-if you're going to use webfonts, be sure to check your weights
-http://css-tricks.com/watch-your-font-weight/
-*/ /* removing text decoration from all headline links */ }
-h1 a, .h1 a, h2 a, .h2 a, h3 a, .h3 a, h4 a, .h4 a, h5 a, .h5 a { text-decoration: none; }
-
-h1, .h1 { font-size: 2.2em; line-height: 1.333em; }
-
-h2, .h2 { font-size: 1.75em; line-height: 1.4em; margin-bottom: 0.375em; }
-
-h3, .h3 { font-size: 1.125em; }
-
-h4, .h4 { font-size: 1.1em; font-weight: 700; }
-
-h5, .h5 { font-size: 0.846em; line-height: 2.09em; text-transform: uppercase; letter-spacing: 2px; }
-
-/*********************
-HEADER STYLES
-*********************/
-.header { background-color: white; }
-
-#logo { margin: 25px 0 15px; text-align: center; }
-#logo a { color: white; }
-
-#masthead { background: url("../images/masthead-bg.png") no-repeat center center; background-size: cover; clear: both; padding: 50px 0; -webkit-box-shadow: inset 0 0 5px 8px rgba(0, 0, 0, 0.2); box-shadow: inset 0 0 5px 8px rgba(0, 0, 0, 0.2); text-align: center; }
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-#masthead p { color: #fff; line-height: 1.8em; text-align: center; letter-spacing: 1px; font-size: 11pt; }
-
-/*********************
-NAVIGATION STYLES
-*********************/
-/*
-all navs have a .nav class applied via
-the wp_menu function; this is so we can
-easily write one group of styles for
-the navs on the site so our css is cleaner
-and more scalable.
-*/
-.nav { border-bottom: 0; margin: 0; font-family: "Roboto", "Helvetica Neue", Helvetica, Arial, sans-serif; /* end .menu li */ /* highlight current page */ /* end current highlighters */ }
-.nav li { /*
-so you really have to rethink your dropdowns for mobile.
-you don't want to have it expand too much because the
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-depend on the project. Here's some great info on it:
-http://www.alistapart.com/articles/organizing-mobile/
-*/ }
-.nav li a { display: block; color: white; text-decoration: none; padding: 0.75em; /*
-remember this is for mobile ONLY, so there's no need
-to even declare hover styles here, you can do it in
-the style.scss file where it's relevant. We want to
-keep this file as small as possible!
-*/ }
-.nav li ul.sub-menu li a, .nav li ul.children li a { padding-left: 30px; }
-
-/* end .nav */
-/*********************
-POSTS & CONTENT STYLES
-*********************/
-.home { background: white; }
-.home #main img { margin: 50px 0; }
-.home .header { box-shadow: none; -webkit-box-shadow: none; -moz-box-shadow: none; }
-
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-
-#key-features { padding: 50px 0; background: white url("../images/key_features_bg.jpg") no-repeat top center; background-size: cover; border-top: 1px solid #efefef; }
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-#user-stories { background: #535457; padding: 50px 0; color: #fff; }
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-#use-case .table { padding: 25px 0 25px 0; }
-#use-case .table:first-of-type { padding-top: 0; }
-#use-case .table p { font-size: 13pt; line-height: 1.3em; font-weight: 600; }
-#use-case .table p strong { font-weight: 800; }
-#use-case .table p:first-of-type { margin-top: 0; }
-
-.arrow { margin: 50px 0 0; width: 100%; height: 150px; background-color: #f9bf12; position: relative; }
-.arrow p { margin: 20px 25px 20px 25px; font-size: 14pt; text-align: left; line-height: 1.2em; }
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-#more-info { background: #535457; padding: 50px 0; }
-#more-info p { text-align: center; }
-
-.social-icons {text-align:center;margin-bottom:50px;}
-.social-icons a {font-size:2em;}
-.social-icons a:hover {color:#fff;}
-.social-icons a:first-child {margin-right:15px;}
-.add-to-slack {margin:0 0 0 15px;}
-.add-to-slack {position:relative;top:5px;}
-
-#content { margin-top: 2em; }
-
-.hentry { padding: .75em; margin-bottom: 1.5em; }
-.hentry footer { padding: 1.5em 0; border-top: 1px solid #d6d6d6; }
-.hentry footer p { margin: 0; }
-
-/* end .hentry */
-.single-title, .page-title, .entry-title { margin: 0; }
-
-/* want to style individual post classes? Booya! */
-/* post by id (i.e. post-3) */
-/* general post style */
-/* general article on a page style */
-/* general style on an attatchment */
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-.byline { color: #9fa6b4; margin: 0; font-size: 10pt; }
-
-/* entry content */
-.entry-content { padding: 1.5em 0; /*
-image alignment on a screen this size may be
-a bit difficult. It's set to start aligning
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-*/ }
-.entry-content ol {list-style-type:decimal;padding-left:40px;}
-.entry-content ul {list-style-type:disc;padding-left:40px;}
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-.entry-content table caption { margin: 0 0 7px; font-size: 0.75em; color: #9fa6b4; text-transform: uppercase; letter-spacing: 1px; }
-.entry-content tr { border-bottom: 1px solid #626366; }
-.entry-content tr:nth-child(even) { background-color: #f8f9fa; }
-.entry-content td { padding: 7px; border-right: 1px solid #626366; }
-.entry-content td:last-child { border-right: 0; }
-.entry-content th { background-color: #f8f9fa; border-bottom: 1px solid #626366; border-right: 1px solid #626366; }
-.entry-content th:last-child { border-right: 0; }
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-.entry-content img { margin: 0 0 1.5em 0; max-width: 100%; height: auto; }
-.entry-content .size-auto, .entry-content .size-full, .entry-content .size-large, .entry-content .size-medium, .entry-content .size-thumbnail { max-width: 100%; height: auto; }
-.entry-content pre { background: #323944; color: #f8f9fa; font-size: 0.9em; padding: 1.5em; margin: 0 0 1.5em; border-radius: 3px; }
-
-/* end .entry-content */
-.wp-caption { max-width: 100%; background: #eee; padding: 5px; /* images inside wp-caption */ }
-.wp-caption img { max-width: 100%; margin-bottom: 0; width: 100%; }
-.wp-caption p.wp-caption-text { font-size: 0.85em; margin: 4px 0 7px; text-align: center; }
-
-/* end .wp-caption */
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-.tags { margin: 0; }
-
-/******************************************************************
-PAGE NAVI STYLES
-******************************************************************/
-.pagination, .wp-prev-next { margin: 1.5em 0; }
-
-.pagination { text-align: center; }
-.pagination ul { display: inline-block; white-space: nowrap; padding: 0; clear: both; border-radius: 3px; }
-.pagination li { padding: 0; margin: 0; float: left; display: inline; overflow: hidden; border-right: 1px solid #9fa6b4; }
-.pagination a, .pagination span { margin: 0; text-decoration: none; padding: 0; line-height: 1em; font-size: 1em; font-weight: normal; padding: 0.75em; min-width: 1em; display: block; color: #f9bf12; }
-.pagination a:hover, .pagination a:focus, .pagination span:hover, .pagination span:focus { background-color: #f9bf12; color: white; }
-.pagination .current { cursor: default; color: #9fa6b4; }
-.pagination .current:hover, .pagination .current:focus { background-color: transparent; color: #5c6b80; }
-
-/* end .bones_page_navi */
-/* fallback previous & next links */
-.wp-prev-next .prev-link { float: left; }
-.wp-prev-next .next-link { float: right; }
-
-/* end .wp-prev-next */
-/******************************************************************
-COMMENT STYLES
-******************************************************************/
-/* h3 comment title */
-#comments-title { padding: 0.75em; margin: 0; border-top: 1px solid #f8f9fa; /* number of comments span */ }
-
-.commentlist { margin: 0; list-style-type: none; }
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-.comment { position: relative; clear: both; overflow: hidden; padding: 1.5em; border-bottom: 1px solid #f8f9fa; /* vcard */ /* end .commentlist .vcard */ /* end children */ /* general comment classes */ }
-.comment .comment-author { padding: 7px; border: 0; }
-.comment .vcard { margin-left: 50px; }
-.comment .vcard cite.fn { font-weight: 700; font-style: normal; }
-.comment .vcard time { display: block; font-size: 0.9em; font-style: italic; }
-.comment .vcard time a { color: #9fa6b4; text-decoration: none; }
-.comment .vcard time a:hover { text-decoration: underline; }
-.comment .vcard .avatar { position: absolute; left: 16px; border-radius: 50%; }
-.comment:last-child { margin-bottom: 0; }
-.comment .children { margin: 0; /* variations */ /* change number for different depth */ }
-.comment[class*=depth-] { margin-top: 1.1em; }
-.comment.depth-1 { margin-left: 0; margin-top: 0; }
-.comment:not(.depth-1) { margin-top: 0; margin-left: 7px; padding: 7px; }
-.comment.odd { background-color: white; }
-.comment.even { background: #f8f9fa; }
-
-/* comment meta */
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-.comment_content p { margin: 0.7335em 0 1.5em; font-size: 1em; line-height: 1.5em; }
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-/* end .commentlist .comment_content */
-/* comment reply link */
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-
-/* end .commentlist .comment-reply-link */
-/* edit comment link */
-.comment-edit-link { font-style: italic; margin: 0 7px; text-decoration: none; font-size: 0.9em; }
-
-/******************************************************************
-COMMENT FORM STYLES
-******************************************************************/
-.comment-respond { padding: 1.5em; border-top: 1px solid #f8f9fa; }
-
-#reply-title { margin: 0; }
-
-.logged-in-as { color: #9fa6b4; font-style: italic; margin: 0; }
-.logged-in-as a { color: #5c6b80; }
-
-.comment-form-comment { margin: 1.5em 0 0.75em; }
-
-.form-allowed-tags { padding: 1.5em; background-color: #f8f9fa; font-size: 0.9em; }
-
-/* comment submit button */
-#submit { float: right; font-size: 1em; }
-
-/* comment form title */
-#comment-form-title { margin: 0 0 1.1em; }
-
-/* cancel comment reply link */
-/* logged in comments */
-/* allowed tags */
-#allowed_tags { margin: 1.5em 10px 0.7335em 0; }
-
-/* no comments */
-.nocomments { margin: 0 20px 1.1em; }
-
-/*********************
-SIDEBARS & ASIDES
-*********************/
-.widget ul li { /* deep nesting */ }
-
-.no-widgets { background-color: white; padding: 1.5em; text-align: center; border: 1px solid #cccccc; border-radius: 2px; margin-bottom: 1.5em; }
-
-/*********************
-FOOTER STYLES
-*********************/
-.footer { clear: both; background-color: #3e3e40; color: #f8f9fa; min-height: 50px; }
-
-/*
-if you checked out the link above:
-http://www.alistapart.com/articles/organizing-mobile/
-you'll want to style the footer nav
-a bit more in-depth. Remember to keep
-it simple because you'll have to
-override these styles for the desktop
-view.
-*/
-/* end .footer-links */
-/*********************
-LARGER MOBILE DEVICES
-This is for devices like the Galaxy Note or something that's
-larger than an iPhone but smaller than a tablet. Let's call them
-tweeners.
-*********************/
-@media only screen and (min-width: 481px) { /****************************************************************** Site Name: Author: Stylesheet: 481px and Up Stylesheet This stylesheet is loaded for larger devices. It's set to 481px because at 480px it would load on a landscaped iPhone. This isn't ideal because then you would be loading all those extra styles on that same mobile connection. A word of warning. This size COULD be a larger mobile device, so you still want to keep it pretty light and simply expand upon your base.scss styles. *****************************************************************/
- /* IMPORTANT NOTE ABOUT SASS 3.3 & UP You can't use @extend within media queries anymore, so just be aware that if you drop them in here, they won't work. */
- /********************* NAVIGATION STYLES ********************/
- /* .menu is clearfixed inside mixins.scss */
- .menu { /* end .menu ul */ }
- .menu ul { /* end .menu ul li */ /* highlight current page */ /* end current highlighters */ }
- .menu ul li { /*
-plan your menus and drop-downs wisely.
-*/ }
- .menu ul li a { /*
-you can use hover styles here even though this size
-has the possibility of being a mobile device.
-*/ }
- /* end .menu */
- /********************* POSTS & CONTENT STYLES ********************/
- #user-stories .table-cell { width: auto; display: table-cell; text-align: left; }
- #user-stories .quote-man { text-align: center; width: 200px; }
- .arrow { margin: 50px 0 0; width: 85%; height: 130px; background-color: #f9bf12; position: relative; }
- .arrow p { margin: 20px 25px 20px 25px; font-size: 14pt; text-align: left; line-height: 1.2em; }
- .arrow:after { content: ''; position: absolute; top: 0px; left: 100%; width: 0; height: 0; border: 65px solid transparent; border-left: 55px solid #f9bf12; }
- /* entry content */
- .entry-content { /* at this larger size, we can start to align images */ }
- .entry-content .alignleft, .entry-content img.alignleft { margin-right: 1.5em; display: inline; float: left; }
- .entry-content .alignright, .entry-content img.alignright { margin-left: 1.5em; display: inline; float: right; }
- .entry-content .aligncenter, .entry-content img.aligncenter { margin-right: auto; margin-left: auto; display: block; clear: both; }
- /* end .entry-content */
- /********************* FOOTER STYLES ********************/
- /* check your menus here. do they look good? do they need tweaking? */
- /* end .footer-links */ }
-/*********************
-TABLET & SMALLER LAPTOPS
-This is the average viewing window. So Desktops, Laptops, and
-in general anyone not viewing on a mobile device. Here's where
-you can add resource intensive styles.
-*********************/
-@media only screen and (min-width: 768px) { /****************************************************************** Site Name: Author: Stylesheet: Tablet & Small Desktop Stylesheet Here's where you can start getting into the good stuff. This size will work on iPads, other tablets, and desktops. So you can start working with more styles, background images, and other resources. You'll also notice the grid starts to come into play. Have fun! *****************************************************************/
- /********************* GENERAL STYLES ********************/
- /********************* LAYOUT & GRID STYLES ********************/
- .wrap { width: 760px; }
- /********************* HEADER STYLES ********************/
- #logo { float: left; text-align: left; }
- img.oniguy { margin-top: 100px; }
- .oni-guy { float: right; }
- #masthead { text-align: left; }
- #masthead h1 { text-align: left; margin: 0; }
- #masthead p { text-align: left; }
- /********************* NAVIGATION STYLES ********************/
- .nav { border: 0; float: right; margin-top: 35px; /* end .menu ul li */ /* highlight current page */ /* end current highlighters */ }
- .nav ul { background: #323944; margin-top: 0; }
- .nav li { float: left; position: relative; /*
-plan your menus and drop-downs wisely.
-*/ /* showing sub-menus */ }
- .nav li a { border-bottom: 0; color: #626366; font-family: "Roboto", "Helvetica Neue", Helvetica, Arial, sans-serif; font-weight: 600; text-transform: uppercase; font-size: 10pt; letter-spacing: 1px; padding: .75em 1.5em; /*
-you can use hover styles here even though this size
-has the possibility of being a mobile device.
-*/ }
- .nav li a:hover, .nav li a:focus { color: #f9bf12; }
- .nav li ul.sub-menu, .nav li ul.children { margin-top: 0; border: 1px solid #ccc; border-top: 0; position: absolute; visibility: hidden; z-index: 8999; /* highlight sub-menu current page */ }
- .nav li ul.sub-menu li, .nav li ul.children li { /*
-if you need to go deeper, go nuts
-just remember deeper menus suck
-for usability. k, bai.
-*/ }
- .nav li ul.sub-menu li a, .nav li ul.children li a { padding-left: 10px; border-right: 0; display: block; width: 180px; border-bottom: 1px solid #ccc; }
- .nav li ul.sub-menu li:last-child a, .nav li ul.children li:last-child a { border-bottom: 0; }
- .nav li ul.sub-menu li ul, .nav li ul.children li ul { top: 0; left: 100%; }
- .nav li:hover > ul { top: auto; visibility: visible; }
- /* end .nav */
- /********************* CONTENT ********************/
- #how-it-works { padding: 100px 0; text-align: left; }
- #how-it-works img { display: block; margin: 0 auto; }
- #key-features .d-1of2:nth-of-type(2n+1) { clear: both; }
- #key-features .d-1of2 p { margin-right: 35px; }
- #use-case .table { padding: 25px 0 25px 50px; border-left: 1px solid #535457; }
- #use-case .table:first-of-type { padding-top: 0; }
- #use-case .table:last-of-type { padding-bottom: 0; }
- #use-case .left img { margin-bottom: 0; padding-right: 25px; }
- .arrow { height: 80px; }
- .arrow p { margin: 20px 85px 20px 25px; }
- .arrow:after { border: 40px solid transparent; border-left: 40px solid #f9bf12; }
- .hentry { margin-right: 50px; }
- /********************* SIDEBARS & ASIDES ********************/
- .sidebar { margin-top: 0; }
- .widgettitle { border-bottom: 2px solid #444; margin-bottom: 0.75em; }
- .widget { padding: 0 10px; margin: 2.2em 0; }
- .widget ul li { margin-bottom: 0.75em; /* deep nesting */ }
- .widget ul li ul { margin-top: 0.75em; padding-left: 1em; }
- /* links widget */
- /* meta widget */
- /* pages widget */
- /* recent-posts widget */
- /* archives widget */
- /* tag-cloud widget */
- /* calendar widget */
- /* category widget */
- /* recent-comments widget */
- /* search widget */
- /* text widget */
- /********************* FOOTER STYLES ********************/
- /* you'll probably need to do quite a bit of overriding here if you styled them for mobile. Make sure to double check these! */
- .footer-links ul li { /*
-be careful with the depth of your menus.
-it's very rare to have multi-depth menus in
-the footer.
-*/ }
-.y-btn {display:inline-block;margin:0;}
-/*.y-btn:first-of-type {margin-right:15px;}*/
- /* end .footer-links */ }
-/*********************
-DESKTOP
-This is the average viewing window. So Desktops, Laptops, and
-in general anyone not viewing on a mobile device. Here's where
-you can add resource intensive styles.
-*********************/
-@media only screen and (min-width: 1030px) { /****************************************************************** Site Name: Author: Stylesheet: Desktop Stylsheet This is the desktop size. It's larger than an iPad so it will only be seen on the Desktop. *****************************************************************/
- .wrap { width: 1040px; }
- .nav li a { padding: .75em 2em; }
- img.oniguy { margin-top: 75px; }
- #and { display: table; border-collapse: separate; width: 100%; }
- #and .and-row { display: table-row; }
- #and .and-cell { display: table-cell; vertical-align: bottom; }
- #and p { margin: 0; }
- #and img { margin-right: 35px; }
- #use-case .table:last-of-type { padding-bottom: 125px; }
- .arrow { height: 80px; }
- .arrow p { margin: 30px 60px 20px 25px; }
- .arrow:after { border: 40px solid transparent; border-left: 40px solid #f9bf12; } }
-/*********************
-LARGE VIEWING SIZE
-This is for the larger monitors and possibly full screen viewers.
-*********************/
-@media only screen and (min-width: 1240px) { /****************************************************************** Site Name: Author: Stylesheet: Super Large Monitor Stylesheet You can add some advanced styles here if you like. This kicks in on larger screens. *****************************************************************/ }
-/*********************
-RETINA (2x RESOLUTION DEVICES)
-This applies to the retina iPhone (4s) and iPad (2,3) along with
-other displays with a 2x resolution. You can also create a media
-query for retina AND a certain size if you want. Go Nuts.
-*********************/
-@media only screen and (-webkit-min-device-pixel-ratio: 1.5), only screen and (min--moz-device-pixel-ratio: 1.5), only screen and (min-device-pixel-ratio: 1.5) { /****************************************************************** Site Name: Author: Stylesheet: Retina Screens & Devices Stylesheet When handling retina screens you need to make adjustments, especially if you're not using font icons. Here you can add them in one neat place. *****************************************************************/
- /* EXAMPLE Let's say you have an image and you need to make sure it looks ok on retina screens. Let's say we have an icon which dimension are 24px x 24px. In your regular stylesheets, it would look something like this: .icon { width: 24px; height: 24px; background: url(../css/img/test.png) no-repeat; } For retina screens, we have to make some adjustments, so that image doesn't look blurry. So, taking into account the image above and the dimensions, this is what we would put in our retina stylesheet: .icon { background: url(../css/img/test@2x.png) no-repeat; background-size: 24px 24px; } So, you would create the same icon, but at double the resolution, meaning it would be 48px x 48px. You'd name it the same, but with a @2x at the end (this is pretty standard practice). Set the background image so it matches the original dimensions and you are good to go. */ }
-/*********************
-PRINT STYLESHEET
-Feel free to customize this. Remember to add things that won't make
-sense to print at the bottom. Things like nav, ads, and forms should
-be set to display none.
-*********************/
-@media print { /****************************************************************** Site Name: Author: Stylesheet: Print Stylesheet This is the print stylesheet. There's probably not a lot of reasons to edit this stylesheet. If you want to though, go for it. *****************************************************************/
- * { background: transparent !important; color: black !important; text-shadow: none !important; filter: none !important; -ms-filter: none !important; }
- a, a:visited { color: #444 !important; text-decoration: underline; }
- a:after, a:visited:after { content: " (" attr(href) ")"; }
- a abbr[title]:after, a:visited abbr[title]:after { content: " (" attr(title) ")"; }
- .ir a:after, a[href^="javascript:"]:after, a[href^="#"]:after { content: ""; }
- pre, blockquote { border: 1px solid #999; page-break-inside: avoid; }
- thead { display: table-header-group; }
- tr, img { page-break-inside: avoid; }
- img { max-width: 100% !important; }
- @page { margin: 0.5cm; }
- p, h2, h3 { orphans: 3; widows: 3; }
- h2, h3 { page-break-after: avoid; }
- .sidebar, .page-navigation, .wp-prev-next, .respond-form, nav { display: none; } }
diff --git a/wp-content/themes/oni/library/fonts/icomoon.eot b/wp-content/themes/oni/library/fonts/icomoon.eot
deleted file mode 100755
index eab027a..0000000
--- a/wp-content/themes/oni/library/fonts/icomoon.eot
+++ /dev/null
Binary files differ
diff --git a/wp-content/themes/oni/library/fonts/icomoon.svg b/wp-content/themes/oni/library/fonts/icomoon.svg
deleted file mode 100755
index d9d34b2..0000000
--- a/wp-content/themes/oni/library/fonts/icomoon.svg
+++ /dev/null
@@ -1,13 +0,0 @@
-<?xml version="1.0" standalone="no"?>
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diff --git a/wp-content/themes/oni/library/fonts/icomoon.ttf b/wp-content/themes/oni/library/fonts/icomoon.ttf
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diff --git a/wp-content/themes/oni/library/fonts/icomoon.woff b/wp-content/themes/oni/library/fonts/icomoon.woff
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diff --git a/wp-content/themes/oni/library/images/add-to-slack.png b/wp-content/themes/oni/library/images/add-to-slack.png
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diff --git a/wp-content/themes/oni/library/images/and.png b/wp-content/themes/oni/library/images/and.png
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diff --git a/wp-content/themes/oni/library/images/apache-incubator.png b/wp-content/themes/oni/library/images/apache-incubator.png
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diff --git a/wp-content/themes/oni/library/images/apple-touch-icon.png b/wp-content/themes/oni/library/images/apple-touch-icon.png
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diff --git a/wp-content/themes/oni/library/images/key_features_bg.jpg b/wp-content/themes/oni/library/images/key_features_bg.jpg
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--- a/wp-content/themes/oni/library/images/key_features_bg.jpg
+++ /dev/null
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diff --git a/wp-content/themes/oni/library/images/logo.png b/wp-content/themes/oni/library/images/logo.png
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diff --git a/wp-content/themes/oni/library/images/masthead-bg.png b/wp-content/themes/oni/library/images/masthead-bg.png
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diff --git a/wp-content/themes/oni/library/images/quote-person.png b/wp-content/themes/oni/library/images/quote-person.png
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diff --git a/wp-content/themes/oni/library/images/search.png b/wp-content/themes/oni/library/images/search.png
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diff --git a/wp-content/themes/oni/library/images/win8-tile-icon.png b/wp-content/themes/oni/library/images/win8-tile-icon.png
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diff --git a/wp-content/themes/oni/library/js/jquery.meanmenu.js b/wp-content/themes/oni/library/js/jquery.meanmenu.js
deleted file mode 100755
index 873d459..0000000
--- a/wp-content/themes/oni/library/js/jquery.meanmenu.js
+++ /dev/null
@@ -1,285 +0,0 @@
-/*!
-* jQuery meanMenu v2.0.8
-* @Copyright (C) 2012-2014 Chris Wharton @ MeanThemes (https://github.com/meanthemes/meanMenu)
-*
-*/
-/*
-* This program is free software: you can redistribute it and/or modify
-* it under the terms of the GNU General Public License as published by
-* the Free Software Foundation, either version 3 of the License, or
-* (at your option) any later version.
-*
-* THIS SOFTWARE AND DOCUMENTATION IS PROVIDED "AS IS," AND COPYRIGHT
-* HOLDERS MAKE NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED,
-* INCLUDING BUT NOT LIMITED TO, WARRANTIES OF MERCHANTABILITY OR
-* FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE
-* OR DOCUMENTATION WILL NOT INFRINGE ANY THIRD PARTY PATENTS,
-* COPYRIGHTS, TRADEMARKS OR OTHER RIGHTS.COPYRIGHT HOLDERS WILL NOT
-* BE LIABLE FOR ANY DIRECT, INDIRECT, SPECIAL OR CONSEQUENTIAL
-* DAMAGES ARISING OUT OF ANY USE OF THE SOFTWARE OR DOCUMENTATION.
-*
-* You should have received a copy of the GNU General Public License
-* along with this program. If not, see <http://gnu.org/licenses/>.
-*
-* Find more information at http://www.meanthemes.com/plugins/meanmenu/
-*
-*/
-(function ($) {
- "use strict";
- $.fn.meanmenu = function (options) {
- var defaults = {
- meanMenuTarget: jQuery(this), // Target the current HTML markup you wish to replace
- meanMenuContainer: '#mobile-nav', // Choose where meanmenu will be placed within the HTML
- meanMenuClose: "X", // single character you want to represent the close menu button
- meanMenuCloseSize: "18px", // set font size of close button
- meanMenuOpen: "<span /><span /><span />", // text/markup you want when menu is closed
- meanRevealPosition: "right", // left right or center positions
- meanRevealPositionDistance: "0", // Tweak the position of the menu
- meanRevealColour: "", // override CSS colours for the reveal background
- meanScreenWidth: "1030", // set the screen width you want meanmenu to kick in at
- meanNavPush: "", // set a height here in px, em or % if you want to budge your layout now the navigation is missing.
- meanShowChildren: true, // true to show children in the menu, false to hide them
- meanExpandableChildren: true, // true to allow expand/collapse children
- meanExpand: "+", // single character you want to represent the expand for ULs
- meanContract: "-", // single character you want to represent the contract for ULs
- meanRemoveAttrs: false, // true to remove classes and IDs, false to keep them
- onePage: false, // set to true for one page sites
- meanDisplay: "block", // override display method for table cell based layouts e.g. table-cell
- removeElements: "" // set to hide page elements
- };
- options = $.extend(defaults, options);
-
- // get browser width
- var currentWidth = window.innerWidth || document.documentElement.clientWidth;
-
- return this.each(function () {
- var meanMenu = options.meanMenuTarget;
- var meanContainer = options.meanMenuContainer;
- var meanMenuClose = options.meanMenuClose;
- var meanMenuCloseSize = options.meanMenuCloseSize;
- var meanMenuOpen = options.meanMenuOpen;
- var meanRevealPosition = options.meanRevealPosition;
- var meanRevealPositionDistance = options.meanRevealPositionDistance;
- var meanRevealColour = options.meanRevealColour;
- var meanScreenWidth = options.meanScreenWidth;
- var meanNavPush = options.meanNavPush;
- var meanRevealClass = ".meanmenu-reveal";
- var meanShowChildren = options.meanShowChildren;
- var meanExpandableChildren = options.meanExpandableChildren;
- var meanExpand = options.meanExpand;
- var meanContract = options.meanContract;
- var meanRemoveAttrs = options.meanRemoveAttrs;
- var onePage = options.onePage;
- var meanDisplay = options.meanDisplay;
- var removeElements = options.removeElements;
-
- //detect known mobile/tablet usage
- var isMobile = false;
- if ( (navigator.userAgent.match(/iPhone/i)) || (navigator.userAgent.match(/iPod/i)) || (navigator.userAgent.match(/iPad/i)) || (navigator.userAgent.match(/Android/i)) || (navigator.userAgent.match(/Blackberry/i)) || (navigator.userAgent.match(/Windows Phone/i)) ) {
- isMobile = true;
- }
-
- if ( (navigator.userAgent.match(/MSIE 8/i)) || (navigator.userAgent.match(/MSIE 7/i)) ) {
- // add scrollbar for IE7 & 8 to stop breaking resize function on small content sites
- jQuery('html').css("overflow-y" , "scroll");
- }
-
- var meanRevealPos = "";
- var meanCentered = function() {
- if (meanRevealPosition === "center") {
- var newWidth = window.innerWidth || document.documentElement.clientWidth;
- var meanCenter = ( (newWidth/2)-22 )+"px";
- meanRevealPos = "left:" + meanCenter + ";right:auto;";
-
- if (!isMobile) {
- jQuery('.meanmenu-reveal').css("left",meanCenter);
- } else {
- jQuery('.meanmenu-reveal').animate({
- left: meanCenter
- });
- }
- }
- };
-
- var menuOn = false;
- var meanMenuExist = false;
-
-
- if (meanRevealPosition === "right") {
- meanRevealPos = "right:" + meanRevealPositionDistance + ";left:auto;";
- }
- if (meanRevealPosition === "left") {
- meanRevealPos = "left:" + meanRevealPositionDistance + ";right:auto;";
- }
- // run center function
- meanCentered();
-
- // set all styles for mean-reveal
- var $navreveal = "";
-
- var meanInner = function() {
- // get last class name
- if (jQuery($navreveal).is(".meanmenu-reveal.meanclose")) {
- $navreveal.html(meanMenuClose);
- } else {
- $navreveal.html(meanMenuOpen);
- }
- };
-
- // re-instate original nav (and call this on window.width functions)
- var meanOriginal = function() {
- jQuery('.mean-bar,.mean-push').remove();
- jQuery(meanContainer).removeClass("mean-container");
- jQuery(meanMenu).css('display', meanDisplay);
- menuOn = false;
- meanMenuExist = false;
- jQuery(removeElements).removeClass('mean-remove');
- };
-
- // navigation reveal
- var showMeanMenu = function() {
- var meanStyles = "background:"+meanRevealColour+";color:"+meanRevealColour+";"+meanRevealPos;
- if (currentWidth <= meanScreenWidth) {
- jQuery(removeElements).addClass('mean-remove');
- meanMenuExist = true;
- // add class to body so we don't need to worry about media queries here, all CSS is wrapped in '.mean-container'
- jQuery(meanContainer).addClass("mean-container");
- jQuery('.mean-container').prepend('<div class="mean-bar"><a href="#nav" class="meanmenu-reveal" style="'+meanStyles+'">Show Navigation</a><nav class="mean-nav"></nav></div>');
-
- //push meanMenu navigation into .mean-nav
- var meanMenuContents = jQuery(meanMenu).html();
- jQuery('.mean-nav').html(meanMenuContents);
-
- // remove all classes from EVERYTHING inside meanmenu nav
- if(meanRemoveAttrs) {
- jQuery('nav.mean-nav ul, nav.mean-nav ul *').each(function() {
- // First check if this has mean-remove class
- if (jQuery(this).is('.mean-remove')) {
- jQuery(this).attr('class', 'mean-remove');
- } else {
- jQuery(this).removeAttr("class");
- }
- jQuery(this).removeAttr("id");
- });
- }
-
- // push in a holder div (this can be used if removal of nav is causing layout issues)
- jQuery(meanMenu).before('<div class="mean-push" />');
- jQuery('.mean-push').css("margin-top",meanNavPush);
-
- // hide current navigation and reveal mean nav link
- jQuery(meanMenu).hide();
- jQuery(".meanmenu-reveal").show();
-
- // turn 'X' on or off
- jQuery(meanRevealClass).html(meanMenuOpen);
- $navreveal = jQuery(meanRevealClass);
-
- //hide mean-nav ul
- jQuery('.mean-nav ul').hide();
-
- // hide sub nav
- if(meanShowChildren) {
- // allow expandable sub nav(s)
- if(meanExpandableChildren){
- jQuery('.mean-nav ul ul').each(function() {
- if(jQuery(this).children().length){
- jQuery(this,'li:first').parent().append('<a class="mean-expand" href="#" style="font-size: '+ meanMenuCloseSize +'">'+ meanExpand +'</a>');
- }
- });
- jQuery('.mean-expand').on("click",function(e){
- e.preventDefault();
- if (jQuery(this).hasClass("mean-clicked")) {
- jQuery(this).text(meanExpand);
- jQuery(this).prev('ul').slideUp(300, function(){});
- } else {
- jQuery(this).text(meanContract);
- jQuery(this).prev('ul').slideDown(300, function(){});
- }
- jQuery(this).toggleClass("mean-clicked");
- });
- } else {
- jQuery('.mean-nav ul ul').show();
- }
- } else {
- jQuery('.mean-nav ul ul').hide();
- }
-
- // add last class to tidy up borders
- jQuery('.mean-nav ul li').last().addClass('mean-last');
- $navreveal.removeClass("meanclose");
- jQuery($navreveal).click(function(e){
- e.preventDefault();
- if( menuOn === false ) {
- $navreveal.css("text-align", "center");
- $navreveal.css("text-indent", "0");
- $navreveal.css("font-size", meanMenuCloseSize);
- jQuery('.mean-nav ul:first').slideDown();
- menuOn = true;
- } else {
- jQuery('.mean-nav ul:first').slideUp();
- menuOn = false;
- }
- $navreveal.toggleClass("meanclose");
- meanInner();
- jQuery(removeElements).addClass('mean-remove');
- });
-
- // for one page websites, reset all variables...
- if ( onePage ) {
- jQuery('.mean-nav ul > li > a:first-child').on( "click" , function () {
- jQuery('.mean-nav ul:first').slideUp();
- menuOn = false;
- jQuery($navreveal).toggleClass("meanclose").html(meanMenuOpen);
- });
- }
- } else {
- meanOriginal();
- }
- };
-
- if (!isMobile) {
- // reset menu on resize above meanScreenWidth
- jQuery(window).resize(function () {
- currentWidth = window.innerWidth || document.documentElement.clientWidth;
- if (currentWidth > meanScreenWidth) {
- meanOriginal();
- } else {
- meanOriginal();
- }
- if (currentWidth <= meanScreenWidth) {
- showMeanMenu();
- meanCentered();
- } else {
- meanOriginal();
- }
- });
- }
-
- jQuery(window).resize(function () {
- // get browser width
- currentWidth = window.innerWidth || document.documentElement.clientWidth;
-
- if (!isMobile) {
- meanOriginal();
- if (currentWidth <= meanScreenWidth) {
- showMeanMenu();
- meanCentered();
- }
- } else {
- meanCentered();
- if (currentWidth <= meanScreenWidth) {
- if (meanMenuExist === false) {
- showMeanMenu();
- }
- } else {
- meanOriginal();
- }
- }
- });
-
- // run main menuMenu function on load
- showMeanMenu();
- });
- };
-})(jQuery);
diff --git a/wp-content/themes/oni/library/js/libs/modernizr.custom.min.js b/wp-content/themes/oni/library/js/libs/modernizr.custom.min.js
deleted file mode 100755
index 89bb6c1..0000000
--- a/wp-content/themes/oni/library/js/libs/modernizr.custom.min.js
+++ /dev/null
@@ -1,4 +0,0 @@
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- */
-;window.Modernizr=function(a,b,c){function D(a){j.cssText=a}function E(a,b){return D(n.join(a+";")+(b||""))}function F(a,b){return typeof a===b}function G(a,b){return!!~(""+a).indexOf(b)}function H(a,b){for(var d in a){var e=a[d];if(!G(e,"-")&&j[e]!==c)return b=="pfx"?e:!0}return!1}function I(a,b,d){for(var e in a){var f=b[a[e]];if(f!==c)return d===!1?a[e]:F(f,"function")?f.bind(d||b):f}return!1}function J(a,b,c){var d=a.charAt(0).toUpperCase()+a.slice(1),e=(a+" "+p.join(d+" ")+d).split(" ");return F(b,"string")||F(b,"undefined")?H(e,b):(e=(a+" "+q.join(d+" ")+d).split(" "),I(e,b,c))}function K(){e.input=function(c){for(var d=0,e=c.length;d<e;d++)u[c[d]]=c[d]in k;return u.list&&(u.list=!!b.createElement("datalist")&&!!a.HTMLDataListElement),u}("autocomplete autofocus list placeholder max min multiple pattern required step".split(" ")),e.inputtypes=function(a){for(var 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diff --git a/wp-content/themes/oni/library/js/scripts.js b/wp-content/themes/oni/library/js/scripts.js
deleted file mode 100755
index 02dfd67..0000000
--- a/wp-content/themes/oni/library/js/scripts.js
+++ /dev/null
@@ -1,120 +0,0 @@
-/*
- * Bones Scripts File
- * Author: Eddie Machado
- *
- * This file should contain any js scripts you want to add to the site.
- * Instead of calling it in the header or throwing it inside wp_head()
- * this file will be called automatically in the footer so as not to
- * slow the page load.
- *
- * There are a lot of example functions and tools in here. If you don't
- * need any of it, just remove it. They are meant to be helpers and are
- * not required. It's your world baby, you can do whatever you want.
-*/
-
-
-/*
- * Get Viewport Dimensions
- * returns object with viewport dimensions to match css in width and height properties
- * ( source: http://andylangton.co.uk/blog/development/get-viewport-size-width-and-height-javascript )
-*/
-function updateViewportDimensions() {
- var w=window,d=document,e=d.documentElement,g=d.getElementsByTagName('body')[0],x=w.innerWidth||e.clientWidth||g.clientWidth,y=w.innerHeight||e.clientHeight||g.clientHeight;
- return { width:x,height:y };
-}
-// setting the viewport width
-var viewport = updateViewportDimensions();
-
-
-/*
- * Throttle Resize-triggered Events
- * Wrap your actions in this function to throttle the frequency of firing them off, for better performance, esp. on mobile.
- * ( source: http://stackoverflow.com/questions/2854407/javascript-jquery-window-resize-how-to-fire-after-the-resize-is-completed )
-*/
-var waitForFinalEvent = (function () {
- var timers = {};
- return function (callback, ms, uniqueId) {
- if (!uniqueId) { uniqueId = "Don't call this twice without a uniqueId"; }
- if (timers[uniqueId]) { clearTimeout (timers[uniqueId]); }
- timers[uniqueId] = setTimeout(callback, ms);
- };
-})();
-
-// how long to wait before deciding the resize has stopped, in ms. Around 50-100 should work ok.
-var timeToWaitForLast = 100;
-
-
-/*
- * Here's an example so you can see how we're using the above function
- *
- * This is commented out so it won't work, but you can copy it and
- * remove the comments.
- *
- *
- *
- * If we want to only do it on a certain page, we can setup checks so we do it
- * as efficient as possible.
- *
- * if( typeof is_home === "undefined" ) var is_home = $('body').hasClass('home');
- *
- * This once checks to see if you're on the home page based on the body class
- * We can then use that check to perform actions on the home page only
- *
- * When the window is resized, we perform this function
- * $(window).resize(function () {
- *
- * // if we're on the home page, we wait the set amount (in function above) then fire the function
- * if( is_home ) { waitForFinalEvent( function() {
- *
- * // update the viewport, in case the window size has changed
- * viewport = updateViewportDimensions();
- *
- * // if we're above or equal to 768 fire this off
- * if( viewport.width >= 768 ) {
- * console.log('On home page and window sized to 768 width or more.');
- * } else {
- * // otherwise, let's do this instead
- * console.log('Not on home page, or window sized to less than 768.');
- * }
- *
- * }, timeToWaitForLast, "your-function-identifier-string"); }
- * });
- *
- * Pretty cool huh? You can create functions like this to conditionally load
- * content and other stuff dependent on the viewport.
- * Remember that mobile devices and javascript aren't the best of friends.
- * Keep it light and always make sure the larger viewports are doing the heavy lifting.
- *
-*/
-
-/*
- * We're going to swap out the gravatars.
- * In the functions.php file, you can see we're not loading the gravatar
- * images on mobile to save bandwidth. Once we hit an acceptable viewport
- * then we can swap out those images since they are located in a data attribute.
-*/
-function loadGravatars() {
- // set the viewport using the function above
- viewport = updateViewportDimensions();
- // if the viewport is tablet or larger, we load in the gravatars
- if (viewport.width >= 768) {
- jQuery('.comment img[data-gravatar]').each(function(){
- jQuery(this).attr('src',jQuery(this).attr('data-gravatar'));
- });
- }
-} // end function
-
-
-/*
- * Put all your regular jQuery in here.
-*/
-jQuery(document).ready(function($) {
- jQuery('header nav').meanmenu();
- /*
- * Let's fire off the gravatar function
- * You can remove this if you don't need it
- */
- loadGravatars();
-
-
-}); /* end of as page load scripts */
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diff --git a/wp-json/index.html b/wp-json/index.html
deleted file mode 100755
index 108c0fa..0000000
--- a/wp-json/index.html
+++ /dev/null
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