Metron Profiler

The Profiler is a feature extraction mechanism that can generate a profile describing the behavior of an entity on a network. An entity might be a server, user, subnet or application. Once a profile has been generated defining what normal behavior looks-like, models can be built that identify anomalous behavior.

This is achieved by summarizing the streaming telemetry data consumed by Metron over sliding windows. A summary statistic is applied to the data received within a given window. Collecting this summary across many windows results in a time series that is useful for analysis.

Usage

Any field contained within a message can be used to generate a profile. A profile can even be produced from combining fields that originate in different data sources. A user has considerable power to transform the data used in a profile by leveraging the Stellar language. A user only need configure the desired profiles in Zookeeper and ensure that the Profiler topology is running.

Configuration

The Profiler configuration requires a JSON-formatted set of elements, many of which can contain Stellar code. The configuration contains the following elements.

profile

Required

A unique name identifying the profile. The field is treated as a string.

foreach

Required

A separate profile is maintained ‘for each’ of these. This is effectively the entity that the profile is describing. The field is expected to contain a Stellar expression whose result is the entity name.

For example, if ip_src_addr then a separate profile would be maintained for each unique IP source address in the data; 10.0.0.1, 10.0.0.2, etc.

onlyif

Optional

An expression that determines if a message should be applied to the profile. A Stellar expression that returns a Boolean is expected. A message is only applied to a profile if this expression is true. This allows a profile to filter the messages that get applied to it.

groupBy

Optional

One or more Stellar expressions used to group the profile measurements when persisted. This is intended to sort the Profile data to allow for a contiguous scan when accessing subsets of the data.

The ‘groupBy’ expressions can refer to any field within a org.apache.metron.profiler.ProfileMeasurement. A common use case would be grouping by day of week. This allows a contiguous scan to access all profile data for Mondays only. Using the following definition would achieve this.

"groupBy": [ "DAY_OF_WEEK()" ] 

init

Optional

One or more expressions executed at the start of a window period. A map is expected where the key is the variable name and the value is a Stellar expression. The map can contain 0 or more variables/expressions. At the start of each window period the expression is executed once and stored in a variable with the given name.

"init": {
  "var1": "0",
  "var2": "1"
}

update

Required

One or more expressions executed when a message is applied to the profile. A map is expected where the key is the variable name and the value is a Stellar expression. The map can include 0 or more variables/expressions. When each message is applied to the profile, the expression is executed and stored in a variable with the given name.

"update": {
  "var1": "var1 + 1",
  "var2": "var2 + 1"
}

result

Required

A Stellar expression that is executed when the window period expires. The expression is expected to summarize the messages that were applied to the profile over the window period. The expression must result in a numeric value such as a Double, Long, Float, Short, or Integer.

expires

Optional

A numeric value that defines how many days the profile data is retained. After this time, the data expires and is no longer accessible. If no value is defined, the data does not expire.

Examples

Examples of the types of profiles that can be built include the following. Each shows the configuration that would be required to produce the profile. These examples assume a fictitious input messages that looks something like the following.

{
  "ip_src_addr": "10.0.0.1",
  "protocol": "HTTPS",
  "length": "10",
  "bytes_in": "234"
},
{
  "ip_src_addr": "10.0.0.2",
  "protocol": "HTTP",
  "length": "20",
  "bytes_in": "390"
},
{
  "ip_src_addr": "10.0.0.3",
  "protocol": "DNS",
  "length": "30",
  "bytes_in": "560"
}

Example 1

The total number of bytes of HTTP data for each host. The following configuration would be used to generate this profile.

{
  "profiles": [
    {
      "profile": "example1",
      "foreach": "ip_src_addr",
      "onlyif": "protocol == 'HTTP'",
      "init": {
        "total_bytes": 0.0
      },
      "update": {
        "total_bytes": "total_bytes + bytes_in"
      },
      "result": "total_bytes",
      "expires": 30
    }
  ]
}

This creates a profile...

• Named ‘example1’
• That for each IP source address
• Only if the ‘protocol’ field equals ‘HTTP’
• Initializes a counter ‘total_bytes’ to zero
• Adds to ‘total_bytes’ the value of the message's ‘bytes_in’ field
• Returns ‘total_bytes’ as the result
• The profile data will expire in 30 days

Example 2

The ratio of DNS traffic to HTTP traffic for each host. The following configuration would be used to generate this profile.

{
  "profiles": [
    {
      "profile": "example2",
      "foreach": "ip_src_addr",
      "onlyif": "protocol == 'DNS' or protocol == 'HTTP'",
      "init": {
        "num_dns": 1.0,
        "num_http": 1.0
      },
      "update": {
        "num_dns": "num_dns + (if protocol == 'DNS' then 1 else 0)",
        "num_http": "num_http + (if protocol == 'HTTP' then 1 else 0)"
      },
      "result": "num_dns / num_http"
    }
  ]
}

This creates a profile...

• Named ‘example2’
• That for each IP source address
• Only if the ‘protocol’ field equals ‘HTTP’ or ‘DNS’
• Accumulates the number of DNS requests
• Accumulates the number of HTTP requests
• Returns the ratio of these as the result

Example 3

The average of the length field of HTTP traffic. The following configuration would be used to generate this profile.

{
  "profiles": [
    {
      "profile": "example3",
      "foreach": "ip_src_addr",
      "onlyif": "protocol == 'HTTP'",
      "update": { "s": "STATS_ADD(s, length)" },
      "result": "STATS_MEAN(s)"
    }
  ]
}

This creates a profile...

• Named ‘example3’
• That for each IP source address
• Only if the ‘protocol’ field is ‘HTTP’
• Adds the length field from each message
• Calculates the average as the result

Topology Configuration

The Profiler topology also accepts the following configuration settings.

Getting Started

This section will describe the steps required to get your first profile running.

1. Launch the ‘Quick Dev’ environment.

   $ cd metron-deployment/vagrant/quick-dev-platform/
   $ ./run.sh
   

2. After the environment has been deployed, then login to the host.

   $ vagrant ssh
   $ sudo su -
   $ cd /usr/metron/0.2.1BETA/
   

3. Create a table within HBase that will store the profile data. The table name and column family must match the Profiler topology configuration stored at /usr/metron/0.2.1BETA/config/profiler.properties.

   $ /usr/hdp/current/hbase-client/bin/hbase shell
   hbase(main):001:0> create 'profiler', 'P'
   

4. Shorten the flush intervals to more immediately see results. Edit the Profiler topology properties located at /usr/metron/0.2.1BETA/config/profiler.properties. Alter the following two properties.

   profiler.period.duration=30
   profiler.period.duration.units=SECONDS
   profiler.hbase.flush.interval.seconds=5
   

5. Create the Profiler definition in a file located at /usr/metron/0.2.1BETA/config/zookeeper/profiler.json. The following JSON will create a profile that simply counts the number of messages.

   {
     "profiles": [
       {
         "profile": "test",
         "foreach": "ip_src_addr",
         "onlyif":  "true",
         "init":    { "sum": 0 },
         "update":  { "sum": "sum + 1" },
         "result":  "sum"
       }
     ]
   }
   

6. Upload the Profiler definition to Zookeeper.

   $ bin/zk_load_configs.sh -m PUSH -i config/zookeeper/ -z node1:2181
   

7. Start the Profiler topology.

   bin/start_profiler_topology.sh
   

8. Ensure that test messages are being sent to the Profiler's input topic in Kafka. The Profiler will consume messages from the inputTopic in the Profiler definition.

9. Check the HBase table to validate that the Profiler is working.

   $ /usr/hdp/current/hbase-client/bin/hbase shell
   hbase(main):001:0> count 'profiler'
   

Implementation

Key Classes

• ProfileMeasurement - Represents a single data point within a Profile. A Profile is effectively a time series. To this end a Profile is composed of many ProfileMeasurement values which in aggregate form a time series.

• ProfilePeriod - The Profiler captures one ProfileMeasurement each ProfilePeriod. A ProfilePeriod will occur at fixed, deterministic points in time. This allows for efficient retrieval of profile data.

• RowKeyBuilder - Builds row keys that can be used to read or write profile data to HBase.

• ColumnBuilder - Defines the columns of data stored with a profile measurement.

• ProfileHBaseMapper - Defines for the HBaseBolt how profile measurements are stored in HBase. This class leverages a RowKeyBuilder and ColumnBuilder.

Storm Topology

The Profiler is implemented as a Storm topology using the following bolts and spouts.

• KafkaSpout - A spout that consumes messages from a single Kafka topic. In most cases, the Profiler topology will consume messages from the indexing topic. This topic contains fully enriched messages that are ready to be indexed. This ensures that profiles can take advantage of all the available data elements.

• ProfileSplitterBolt - The bolt responsible for filtering incoming messages and directing each to the one or more downstream bolts that are responsible for building a profile. Each message may be needed by 0, 1 or even many profiles. Each emitted tuple contains the ‘resolved’ entity name, the profile definition, and the input message.

• ProfileBuilderBolt - This bolt maintains all of the state required to build a profile. When the window period expires, the data is summarized as a ProfileMeasurement, all state is flushed, and the ProfileMeasurement is emitted. Each instance of this bolt is responsible for maintaining the state for a single Profile-Entity pair.

• HBaseBolt - A bolt that is responsible for writing to HBase. Most profiles will be flushed every 15 minutes or so. If each ProfileBuilderBolt were responsible for writing to HBase itself, there would be little to no opportunity to optimize these writes. By aggregating the writes from multiple Profile-Entity pairs these writes can be batched, for example.