Packages

•  package root

  This is documentation for Mothra, a collection of Scala and Spark library functions for working with Internet-related data.

  This is documentation for Mothra, a collection of Scala and Spark library functions for working with Internet-related data. Some modules contain APIs of general use to Scala programmers. Some modules make those tools more useful on Spark data-processing systems.

  Please see the documentation for the individual packages for more details on their use.

  Scala Packages

  These packages are useful in Scala code without involving Spark:

  org.cert.netsa.data

  This package, which is collected as the netsa-data library, provides types for working with various kinds of information:

  • org.cert.netsa.data.net - types for working with network data
  • org.cert.netsa.data.time - types for working with time data
  • org.cert.netsa.data.unsigned - types for working with unsigned integral values

  org.cert.netsa.io.ipfix

  The netsa-io-ipfix library provides tools for reading and writing IETF IPFIX data from various connections and files.

  org.cert.netsa.io.silk

  To read and write CERT NetSA SiLK file formats and configuration files, use the netsa-io-silk library.

  org.cert.netsa.util

  The "junk drawer" of netsa-util so far provides only two features: First, a method for equipping Scala scala.collection.Iterators with exception handling. And second, a way to query the versions of NetSA libraries present in a JVM at runtime.

  Spark Packages

  These packages require the use of Apache Spark:

  org.cert.netsa.mothra.datasources

  Spark datasources for CERT file types. This package contains utility features which add methods to Apache Spark DataFrameReader objects, allowing IPFIX and SiLK flows to be opened using simple spark.read... calls.

  The mothra-datasources library contains both IPFIX and SiLK functionality, while mothra-datasources-ipfix and mothra-datasources-silk contain only what's needed for the named datasource.

  org.cert.netsa.mothra.analysis

  A grab-bag of analysis helper functions and example analyses.

  org.cert.netsa.mothra.functions

  This single Scala object provides Spark SQL functions for working with network data. It is the entirety of the mothra-functions library.

  Definition Classes

  root

•  package org

  Definition Classes

  root

•  package cert

  Definition Classes

  org

•  package netsa

  Definition Classes

  cert

•  package mothra

  Definition Classes

  netsa

•  package datasources

  This package contains the Mothra datasources, along with mechanisms for working with those datasources.

  This package contains the Mothra datasources, along with mechanisms for working with those datasources. The primary novel feature of these datasources is the fields mechanism.

  To use the IPFIX or SiLK data sources, you can use the following methods added by the implicit CERTDataFrameReader on DataFrameReader after importing from this package:

  import org.cert.netsa.mothra.datasources._
  val silkDF = spark.read.silkFlow()                                    // to read from the default SiLK repository
  val silkRepoDF = spark.read.silkFlow(repository="...")                // to read from an alternate SiLK repository
  val silkFilesDF = spark.read.silkFlow("/path/to/silk/files")          // to read from loose SiLK files
  val ipfixDF = spark.read.ipfix(repository="/path/to/mothra/data/dir") // for packed Mothra IPFIX data
  val ipfixS3DF = spark.read.ipfix(s3Repository="bucket-name")          // for packed Mothra IPFIX data from an S3 bucket
  val ipfixFilesDF = spark.read.ipfix("/path/to/ipfix/files")           // for loose IPFIX files

  (The additional methods are defined on the implicit class CERTDataFrameReader.)

  Using the fields method allows you to configure which SiLK or IPFIX fields you wish to retrieve. (This is particularly important for IPFIX data, as IPFIX files may contains many many possible fields organized in various ways.)

  import org.cert.netsa.mothra.datasources._
  val silkDF = spark.read.fields("sIP", "dIP").silkFlow(...)
  val ipfixDF = spark.read.fields("sourceIPAddress", "destinationIPAddress").ipfix(...)

  Both of these dataframes will contain only the source and destination IP addresses from the specified data sources. You may also provide column names different from the source field names:

  val silkDF = spark.read.fields("server" -> "sIP", "client" -> "dIP").silkFlow(...)
  val ipfixDF = spark.read.fields("server" -> "sourceIPAddress", "client" -> "destinationIPAddress").ipfix(...)

  You may also mix the mapped and the default names in one call:

  val df = spark.read.fields("sIP", "dIP", "s" -> "sensor").silkFlow(...)

  Definition Classes

  mothra

  See also

  IPFIX datasource

  SiLK flow datasource

•  object FilterResult extends Enum[FilterResult]

  Definition Classes

  datasources

• Fails
• Maybe
• Nulls
• Passes

org.cert.netsa.mothra.datasources.FilterResult

Passes