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

•  package fields

  Definition Classes

  datasources

•  package ipfix

  A data source as defined by the Spark Data Source API for reading IPFIX records from Mothra data spools and from loose files.

  A data source as defined by the Spark Data Source API for reading IPFIX records from Mothra data spools and from loose files.

  You can use this by importing org.cert.netsa.mothra.datasources._ like this:

  import org.cert.netsa.mothra.datasources._
  val df1 = spark.read.ipfix("path/to/mothra/data/dir") // for packed Mothra IPFIX data
  val df2 = spark.read.ipfix("path/to/ipfix/files")     // for loose IPFIX files

  The IPFIX datasource uses the fields mechanism from org.cert.netsa.mothra.datasources. You can make use of this mechanism like these examples:

  import org.cert.netsa.mothra.datasources._
  val df1 = spark.read.fields(
    "startTime", "endTime", "sourceIPAddress", "destinationIPAddress"
  ).ipfix(...)
  
  val df2 = spark.read.fields(
    "startTime", "endTime", "TOS" -> "ipClassOfService"
  ).ipfix(...)

  with arbitrary sets of fields and field name mappings.

  Default Fields

  The default set of fields (defined in IPFIXFields.default) is:

  • "startTime" -> "func:startTime"
  • "endTime" -> "func:endTime"
  • "sourceIPAddress" -> "func:sourceIPAddress"
  • "sourcePort" -> "func:sourcePort"
  • "destinationIPAddress" -> "func:destinationIPAddress"
  • "destinationPort" -> "func:destinationPort"
  • "protocolIdentifier"
  • "observationDomainId"
  • "vlanId"
  • "reverseVlanId"
  • "silkAppLabel"
  • "packetCount" -> "packetTotalCount|packetDeltaCount"
  • "reversePacketCount" -> "reversePacketTotalCount|reversePacketDeltaCount"
  • "octetCount" -> "octetTotalCount|octetDeltaDcount"
  • "reverseOctetCount" -> "reverseOctetTotalCount|reverseOctetDeltaCount"
  • "initialTCPFlags"
  • "reverseInitialTCPFlags"
  • "unionTCPFlags"
  • "reverseUnionTCPFlags"

  Some of these defaults are defined simply as IPFIX Information Elements. For example, "protocolIdentifier" and "vlanId" are exactly the Information Elements that are named. No "right-hand-side" is given for these definitions, because the name of the field is the same as the name of the Information Element.

  Others have simple expressions. For example, packetCount is defined as "packetTotalCount|packetDeltaCount". This expressions means that the value should be found from the packetTotalCount IE, or if that is not set from the packetDeltaCount IE. This allows this field to be used regardless of which Information Element contains the data.

  Some others are derived in more complex ways from basic IPFIX fields. For example, the startTime field is produced using "func:startTime", which runs the "gauntlet of time" to determine the start time for a flow by whatever means possible. Other time fields are similarly defined.

  Some of the "func:..." fields are actually quite simple. For example, "func:sourceIPAddress", practically speaking, is the same as "sourceIPv4Address|sourceIPv6Address". However, these fields are defined using the func: extension mechanism so that partitioning on them is possible. (This restriction may be lifted in a future Mothra version.)

  Field Types

  The mappings between IPFIX types and Spark types are:

  • octetArray → Array[Byte]
  • unsigned8 → Short
  • unsigned16 → Int
  • unsigned32 → Long
  • unsigned64 → Long
  • signed8 → Byte
  • signed16 → Short
  • signed32 → Int
  • signed64 → Long
  • float32 → Float
  • float64 → Double
  • boolean → Boolean
  • macAddress → String
  • string → String
  • dateTimeSeconds → Timestamp
  • dateTimeMilliseconds → Timestamp
  • dateTimeMicroseconds → Timestamp
  • dateTimeNanoseconds → Timestamp
  • ipv4Address → String
  • ipv6Address → String

  IPFIX's basicList, subTemplateList, and subTemplateMultiList data types are handled differently.

  Field Expressions

  As noted above, field expressions may contain simple IPFIX Information Element names, or collections of names separated by pipe characters to indicate taking the first matching choice. This language has a number of other capabilities which are documented for now in the IPFIX field parser object.

  Functional Fields

  A number of pre-defined "functional fields" are available. Some of these combine other information elements in ways that the expression language cannot (applying the so-called "gauntlet of time", for example). Others provide support for the Mothra repository partitioning system. And finally, a few are for debugging purposes and provide high-level overviews of IPFIX records or point to file locations on disk.

  Function fields are all defined and described in the org.cert.netsa.mothra.datasources.ipfix.fields.func package.

  Definition Classes

  datasources

•  package silk

  Definition Classes

  datasources

• CERTDataFrameReader
• FilterResult

org.cert.netsa.mothra.datasources

FilterResult

Companion trait FilterResult