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Specifications used in the MISP project including MISP core format
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  1. 

  2. 

  3. 

  4. 

  5. Network Working Group                                        A. Dulaunoy

  6. Internet-Draft                                                 A. Iklody

  7. Intended status: Informational                                     CIRCL

  8. Expires: June 2, 2018                                  November 29, 2017

  9. 

  10. 

  11.                           MISP taxonomy format

  12.                   draft-dulaunoy-misp-taxonomy-format

  13. 

  14. Abstract

  15. 

  16.    This document describes the MISP taxonomy format which describes a

  17.    simple JSON format to represent machine tags (also called triple

  18.    tags) vocabularies.  A public directory of common vocabularies called

  19.    MISP taxonomies is available and relies on the MISP taxonomy format.

  20.    MISP taxonomies are used to classify cyber security events, threats,

  21.    suspicious events, or indicators.

  22. 

  23. Status of This Memo

  24. 

  25.    This Internet-Draft is submitted in full conformance with the

  26.    provisions of BCP 78 and BCP 79.

  27. 

  28.    Internet-Drafts are working documents of the Internet Engineering

  29.    Task Force (IETF).  Note that other groups may also distribute

  30.    working documents as Internet-Drafts.  The list of current Internet-

  31.    Drafts is at https://datatracker.ietf.org/drafts/current/.

  32. 

  33.    Internet-Drafts are draft documents valid for a maximum of six months

  34.    and may be updated, replaced, or obsoleted by other documents at any

  35.    time.  It is inappropriate to use Internet-Drafts as reference

  36.    material or to cite them other than as "work in progress."

  37. 

  38.    This Internet-Draft will expire on June 2, 2018.

  39. 

  40. Copyright Notice

  41. 

  42.    Copyright (c) 2017 IETF Trust and the persons identified as the

  43.    document authors.  All rights reserved.

  44. 

  45.    This document is subject to BCP 78 and the IETF Trust's Legal

  46.    Provisions Relating to IETF Documents

  47.    (https://trustee.ietf.org/license-info) in effect on the date of

  48.    publication of this document.  Please review these documents

  49.    carefully, as they describe your rights and restrictions with respect

  50.    to this document.  Code Components extracted from this document must

  51.    include Simplified BSD License text as described in Section 4.e of

  52. 

  53. 

  54. 

  55. 

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  57. 

  58. Internet-Draft            MISP taxonomy format             November 2017

  59. 

  60. 

  61.    the Trust Legal Provisions and are provided without warranty as

  62.    described in the Simplified BSD License.

  63. 

  64. Table of Contents

  65. 

  66.    1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2

  67.      1.1.  Conventions and Terminology . . . . . . . . . . . . . . .   3

  68.    2.  Format  . . . . . . . . . . . . . . . . . . . . . . . . . . .   3

  69.      2.1.  Overview  . . . . . . . . . . . . . . . . . . . . . . . .   3

  70.      2.2.  predicates  . . . . . . . . . . . . . . . . . . . . . . .   4

  71.      2.3.  values  . . . . . . . . . . . . . . . . . . . . . . . . .   4

  72.      2.4.  optional fields . . . . . . . . . . . . . . . . . . . . .   4

  73.        2.4.1.  colour  . . . . . . . . . . . . . . . . . . . . . . .   4

  74.        2.4.2.  description . . . . . . . . . . . . . . . . . . . . .   5

  75.        2.4.3.  numerical_value . . . . . . . . . . . . . . . . . . .   5

  76.    3.  Directory . . . . . . . . . . . . . . . . . . . . . . . . . .   6

  77.      3.1.  Sample Manifest . . . . . . . . . . . . . . . . . . . . .   7

  78.    4.  Sample Taxonomy in MISP taxonomy format . . . . . . . . . . .   7

  79.      4.1.  Admiralty Scale Taxonomy  . . . . . . . . . . . . . . . .   7

  80.      4.2.  Open Source Intelligence - Classification . . . . . . . .   9

  81.      4.3.  Available taxonomies in the public directory  . . . . . .  11

  82.    5.  JSON Schema . . . . . . . . . . . . . . . . . . . . . . . . .  20

  83.    6.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  23

  84.    7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  23

  85.      7.1.  Normative References  . . . . . . . . . . . . . . . . . .  23

  86.      7.2.  Informative References  . . . . . . . . . . . . . . . . .  23

  87.      7.3.  URIs  . . . . . . . . . . . . . . . . . . . . . . . . . .  23

  88.    Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  24

  89. 

  90. 1.  Introduction

  91. 

  92.    Sharing threat information became a fundamental requirements on the

  93.    Internet, security and intelligence community at large.  Threat

  94.    information can include indicators of compromise, malicious file

  95.    indicators, financial fraud indicators or even detailed information

  96.    about a threat actor.  While sharing such indicators or information,

  97.    classification plays an important role to ensure adequate

  98.    distribution, understanding, validation or action of the shared

  99.    information.  MISP taxonomies is a public repository of known

  100.    vocabularies that can be used in threat information sharing.

  101. 

  102.    Machine tags were introduced in 2007 [machine-tags] to allow users to

  103.    be more precise when tagging their pictures with geolocation.  So a

  104.    machine tag is a tag which uses a special syntax to provide more

  105.    information to users and machines.  Machine tags are also known as

  106.    triple tags due to their format.

  107. 

  108. 

  109. 

  110. 

  111. 

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  115. 

  116. 

  117.    In the MISP taxonomy context, machine tags help analysts to classify

  118.    their cybersecurity events, indicators or threats.  MISP taxonomies

  119.    can be used for classification, filtering, triggering actions or

  120.    visualisation depending on their use in threat intelligence platforms

  121.    such as MISP [MISP-P].

  122. 

  123. 1.1.  Conventions and Terminology

  124. 

  125.    The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",

  126.    "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this

  127.    document are to be interpreted as described in RFC 2119 [RFC2119].

  128. 

  129. 2.  Format

  130. 

  131.    A machine tag is composed of a namespace (MUST), a predicate (MUST)

  132.    and an optional value (OPTIONAL).

  133. 

  134.    Machine tags are represented as a string.  Below listed are a set of

  135.    sample machine tags for different namespaces such as tlp, admiralty-

  136.    scale and osint.

  137. 

  138.                 tlp:amber

  139.                 admiralty-scale:information-credibility="1"

  140.                 osint:source-type="blog-post"

  141. 

  142.    The MISP taxonomy format describes how to define a machine tag

  143.    namespace in a parseable format.  The objective is to provide a

  144.    simple format to describe machine tag (aka triple tag) vocabularies.

  145. 

  146. 2.1.  Overview

  147. 

  148.    The MISP taxonomy format uses the JSON [RFC8259] format.  Each

  149.    namespace is represented as a JSON object with meta information

  150.    including the following fields: namespace, description, version,

  151.    type.

  152. 

  153.    namespace defines the overall namespace of the machine tag.  The

  154.    namespace is represented as a string and MUST be present.  The

  155.    description is represented as a string and MUST be present.  A

  156.    version is represented as a unsigned integer MUST be present.  A type

  157.    defines where a specific taxonomy is applicable and a type can be

  158.    applicable at event, user or org level.  The type is represented as

  159.    an array containing one or more type and SHOULD be present.  If a

  160.    type is not mentioned, by default, the taxonomy is applicable at

  161.    event level only.  An exclusive boolean property MAY be present and

  162.    defines at namespace level if the predicates are mutually exclusive.

  163. 

  164. 

  165. 

  166. 

  167. 

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  171. 

  172. 

  173.    predicates defines all the predicates available in the namespace

  174.    defined. predicates is represented as an array of JSON objects.

  175.    predicates MUST be present and MUST at least content one element.

  176. 

  177.    values defines all the values for each predicate in the namespace

  178.    defined. values SHOULD be present.

  179. 

  180. 2.2.  predicates

  181. 

  182.    The predicates array contains one or more JSON objects which lists

  183.    all the possible predicates.  The JSON object contains two fields:

  184.    value and expanded. value MUST be present. expanded SHOULD be

  185.    present. value is represented as a string and describes the predicate

  186.    value.  The predicate value MUST not contain spaces or colons.

  187.    expanded is represented as a string and describes the human-readable

  188.    version of the predicate value.  An exclusive property MAY be present

  189.    and defines at namespace level if the values are mutually exclusive.

  190. 

  191. 2.3.  values

  192. 

  193.    The values array contain one or more JSON objects which lists all the

  194.    possible values of a predicate.  The JSON object contains two fields:

  195.    predicate and entry. predicate is represented as a string and

  196.    describes the predicate value. entry is an array with one or more

  197.    JSON objects.  The JSON object contains two fields: value and

  198.    expanded. value MUST be present. expanded SHOULD be present. value is

  199.    represented as a string and describes the machine parsable value.

  200.    expanded is represented as a string and describes the human-readable

  201.    version of the value.

  202. 

  203. 2.4.  optional fields

  204. 

  205. 2.4.1.  colour

  206. 

  207.    colour fields MAY be used at predicates or values level to set a

  208.    specify colour that MAY be used by the implementation.  The colour

  209.    field is described as an RGB colour fill in hexadecimal

  210.    representation.

  211. 

  212.    Example use of the colour field in the Traffic Light Protocol (TLP):

  213. 

  214. 

  215. 

  216. 

  217. 

  218. 

  219. 

  220. 

  221. 

  222. 

  223. 

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  227. 

  228. 

  229.     "predicates": [

  230.         {

  231.           "colour": "#CC0033",

  232.           "expanded": "(TLP:RED) Information exclusively and directly

  233.                        given to (a group of) individual recipients.

  234.                        Sharing outside is not legitimate.",

  235.           "value": "red"

  236.         },

  237.         {

  238.           "colour": "#FFC000",

  239.           "expanded": "(TLP:AMBER) Information exclusively given

  240.                        to an organization; sharing limited within

  241.                        the organization to be effectively acted upon.",

  242.           "value": "amber"

  243.         }...]

  244. 

  245. 2.4.2.  description

  246. 

  247.    description fields MAY be used at predicates or values level to add a

  248.    descriptive and human-readable information about the specific

  249.    predicate or value.  The field is represented as a string.

  250.    Implementations MAY use the description field to improve more

  251.    contextual information.  The description at the namespace level is a

  252.    MUST as described above.

  253. 

  254. 2.4.3.  numerical_value

  255. 

  256.    numerical_value fields MAY be used at a predicate or value level to

  257.    add a machine-readable numeric value to a specific predicate or

  258.    value.  The field is represented as a JSON number.  Implementations

  259.    SHOULD use the decimal value provided to support scoring or

  260.    filtering.

  261. 

  262.    The decimal range for numerical_value SHOULD use a range from 0 up to

  263.    100.  The range is recommended to support common mathematical

  264.    properties among taxonomies.

  265. 

  266.    Example use of the numerical_value in the MISP confidence level:

  267. 

  268. 

  269. 

  270. 

  271. 

  272. 

  273. 

  274. 

  275. 

  276. 

  277. 

  278. 

  279. 

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  283. 

  284. 

  285.               {

  286.                "predicate": "confidence-level",

  287.                "entry": [

  288.                   {

  289.                     "expanded": "Completely confident",

  290.                     "value": "completely-confident",

  291.                     "numerical_value": 100

  292.                   },

  293.                   {

  294.                     "expanded": "Usually confident",

  295.                     "value": "usually-confident",

  296.                     "numerical_value": 75

  297.                   },

  298.                   {

  299.                     "expanded": "Fairly confident",

  300.                     "value": "fairly-confident",

  301.                     "numerical_value": 50

  302.                   },

  303.                   {

  304.                     "expanded": "Rarely confident",

  305.                     "value": "rarely-confident",

  306.                     "numerical_value": 25

  307.                   },

  308.                   {

  309.                     "expanded": "Unconfident",

  310.                     "value": "unconfident",

  311.                     "numerical_value": 0

  312.                   },

  313.                   {

  314.                     "expanded": "Confidence cannot be evaluated",

  315.                     "value": "confidence-cannot-be-evalued"

  316.                   }

  317.                ]

  318.                }

  319. 

  320. 3.  Directory

  321. 

  322.    The MISP taxonomies directory is publicly available [MISP-T] in a git

  323.    repository.  The repository contains a directory per namespace then a

  324.    file machinetag.json which contains the taxonomy as described in the

  325.    format above.  In the root of the repository, a MANIFEST.json exists

  326.    containing a list of all the taxonomies.

  327. 

  328.    The MANIFEST.json file is composed of an JSON object with metadata

  329.    like version, license, description, url and path.  A taxonomies array

  330.    describes the taxonomy available with the description, name and

  331.    version field.

  332. 

  333. 

  334. 

  335. 

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  339. 

  340. 

  341. 3.1.  Sample Manifest

  342. 

  343.  {

  344.    "version": "20161009",

  345.    "license": "CC-0",

  346.    "description": "Manifest file of MISP taxonomies available.",

  347.    "url":

  348.      "https://raw.githubusercontent.com/MISP/misp-taxonomies/master/",

  349.    "path": "machinetag.json",

  350.    "taxonomies": [

  351.      {

  352.        "description": "The Admiralty Scale (also called the NATO System)

  353.                        is used to rank the reliability of a source and

  354.                        the credibility of an information.",

  355.        "name": "admiralty-scale",

  356.        "version": 1

  357.      },

  358.      {

  359.        "description": "Open Source Intelligence - Classification.",

  360.        "name": "osint",

  361.        "version": 2

  362.      }]

  363.  }

  364. 

  365. 4.  Sample Taxonomy in MISP taxonomy format

  366. 

  367. 4.1.  Admiralty Scale Taxonomy

  368. 

  369.       "namespace": "admiralty-scale",

  370.       "description": "The Admiralty Scale (also called the NATO System)

  371.                       is used to rank the reliability of a source and

  372.                       the credibility of an information.",

  373.       "version": 1,

  374.       "predicates": [

  375.         {

  376.           "value": "source-reliability",

  377.           "expanded": "Source Reliability"

  378.         },

  379.         {

  380.           "value": "information-credibility",

  381.           "expanded": "Information Credibility"

  382.         }

  383.       ],

  384.       "values": [

  385.         {

  386.           "predicate": "source-reliability",

  387.           "entry": [

  388.             {

  389. 

  390. 

  391. 

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  395. 

  396. 

  397.               "value": "a",

  398.               "expanded": "Completely reliable"

  399.             },

  400.             {

  401.               "value": "b",

  402.               "expanded": "Usually reliable"

  403.             },

  404.             {

  405.               "value": "c",

  406.               "expanded": "Fairly reliable"

  407.             },

  408.             {

  409.               "value": "d",

  410.               "expanded": "Not usually reliable"

  411.             },

  412.             {

  413.               "value": "e",

  414.               "expanded": "Unreliable"

  415.             },

  416.             {

  417.               "value": "f",

  418.               "expanded": "Reliability cannot be judged"

  419.             }

  420.           ]

  421.         },

  422.         {

  423.           "predicate": "information-credibility",

  424.           "entry": [

  425.             {

  426.               "value": "1",

  427.               "expanded": "Confirmed by other sources"

  428.             },

  429.             {

  430.               "value": "2",

  431.               "expanded": "Probably true"

  432.             },

  433.             {

  434.               "value": "3",

  435.               "expanded": "Possibly true"

  436.             },

  437.             {

  438.               "value": "4",

  439.               "expanded": "Doubtful"

  440.             },

  441.             {

  442.               "value": "5",

  443.               "expanded": "Improbable"

  444.             },

  445. 

  446. 

  447. 

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  451. 

  452. 

  453.             {

  454.               "value": "6",

  455.               "expanded": "Truth cannot be judged"

  456.             }

  457.           ]

  458.         }

  459.       ]

  460.     }

  461. 

  462. 4.2.  Open Source Intelligence - Classification

  463. 

  464.  {

  465.    "values": [

  466.      {

  467.        "entry": [

  468.          {

  469.            "expanded": "Blog post",

  470.            "value": "blog-post"

  471.          },

  472.          {

  473.            "expanded": "Technical or analysis report",

  474.            "value": "technical-report"

  475.          },

  476.          {

  477.            "expanded": "News report",

  478.            "value": "news-report"

  479.          },

  480.          {

  481.            "expanded": "Pastie-like website",

  482.            "value": "pastie-website"

  483.          },

  484.          {

  485.            "expanded": "Electronic forum",

  486.            "value": "electronic-forum"

  487.          },

  488.          {

  489.            "expanded": "Mailing-list",

  490.            "value": "mailing-list"

  491.          },

  492.          {

  493.            "expanded": "Block or Filter List",

  494.            "value": "block-or-filter-list"

  495.          },

  496.          {

  497.            "expanded": "Expansion",

  498.            "value": "expansion"

  499.          }

  500.        ],

  501. 

  502. 

  503. 

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  507. 

  508. 

  509.        "predicate": "source-type"

  510.      },

  511.      {

  512.        "predicate": "lifetime",

  513.        "entry": [

  514.          {

  515.            "value": "perpetual",

  516.            "expanded": "Perpetual",

  517.            "description": "Information available publicly on long-term"

  518.          },

  519.          {

  520.            "value": "ephemeral",

  521.            "expanded": "Ephemeral",

  522.            "description": "Information available publicly on short-term"

  523.          }

  524.        ]

  525.      },

  526.      {

  527.        "predicate": "certainty",

  528.        "entry": [

  529.          {

  530.            "numerical_value": 100,

  531.            "value": "100",

  532.            "expanded": "100% Certainty",

  533.            "description": "100% Certainty"

  534.          },

  535.          {

  536.            "numerical_value": 93,

  537.            "value": "93",

  538.            "expanded": "93% Almost certain",

  539.            "description": "93% Almost certain"

  540.          },

  541.          {

  542.            "numerical_value": 75,

  543.            "value": "75",

  544.            "expanded": "75% Probable",

  545.            "description": "75% Probable"

  546.          },

  547.          {

  548.            "numerical_value": 50,

  549.            "value": "50",

  550.            "expanded": "50% Chances about even",

  551.            "description": "50% Chances about even"

  552.          },

  553.          {

  554.            "numerical_value": 30,

  555.            "value": "30",

  556.            "expanded": "30% Probably not",

  557. 

  558. 

  559. 

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  563. 

  564. 

  565.            "description": "30% Probably not"

  566.          },

  567.          {

  568.            "numerical_value": 7,

  569.            "value": "7",

  570.            "expanded": "7% Almost certainly not",

  571.            "description": "7% Almost certainly not"

  572.          },

  573.          {

  574.            "numerical_value": 0,

  575.            "value": "0",

  576.            "expanded": "0% Impossibility",

  577.            "description": "0% Impossibility"

  578.          }

  579.        ]

  580.      }

  581.    ],

  582.    "namespace": "osint",

  583.    "description": "Open Source Intelligence - Classification",

  584.    "version": 3,

  585.    "predicates": [

  586.      {

  587.        "value": "source-type",

  588.        "expanded": "Source Type"

  589.      },

  590.      {

  591.        "value": "lifetime",

  592.        "expanded": "Lifetime of the information

  593.                     as Open Source Intelligence"

  594.      },

  595.      {

  596.        "value": "certainty",

  597.        "expanded": "Certainty of the elements mentioned

  598.                     in this Open Source Intelligence"

  599.      }

  600.    ]

  601.  }

  602. 

  603. 

  604. 4.3.  Available taxonomies in the public directory

  605. 

  606.    The public directory of MISP taxonomies [MISP-T] contains a variety

  607.    of taxonomy in various fields such as:

  608. 

  609.    CERT-XLM:

  610.       CERT-XLM Security Incident Classification.

  611. 

  612.    DML:

  613. 

  614. 

  615. 

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  619. 

  620. 

  621.       The Detection Maturity Level (DML) model is a capability maturity

  622.       model for referencing ones maturity in detecting cyber attacks.

  623.       It's designed for organizations who perform intel-driven detection

  624.       and response and who put an emphasis on having a mature detection

  625.       program.

  626. 

  627.    PAP:

  628.       The Permissible Actions Protocol - or short: PAP - was designed to

  629.       indicate how the received information can be used.

  630. 

  631.    access-method:

  632.       The access method used to remotely access a system.

  633. 

  634.    accessnow:

  635.       Access Now classification to classify an issue (such as security,

  636.       human rights, youth rights).

  637. 

  638.    action-taken:

  639.       Action taken in the case of a security incident (CSIRT

  640.       perspective).

  641. 

  642.    admiralty-scale:

  643.       The Admiralty Scale (also called the NATO System) is used to rank

  644.       the reliability of a source and the credibility of an information.

  645. 

  646.    adversary:

  647.       An overview and description of the adversary infrastructure.

  648. 

  649.    ais-marking:

  650.       AIS Marking Schema implementation is maintained by the National

  651.       Cybersecurity and Communication Integration Center (NCCIC) of the

  652.       U.S.  Department of Homeland Security (DHS)

  653. 

  654.    analyst-assessment:

  655.       A series of assessment predicates describing the analyst

  656.       capabilities to perform analysis.  These assessment can be

  657.       assigned by the analyst him/herself or by another party evaluating

  658.       the analyst.

  659. 

  660.    approved-category-of-action:

  661.       A pre-approved category of action for indicators being shared with

  662.       partners (MIMIC).

  663. 

  664.    binary-class:

  665.       Custom taxonomy for types of binary file.

  666. 

  667.    cccs:

  668.       Internal taxonomy for CCCS.

  669. 

  670. 

  671. 

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  674. Internet-Draft            MISP taxonomy format             November 2017

  675. 

  676. 

  677.    circl:

  678.       CIRCL Taxonomy is a simple scheme for incident classification and

  679.       area topic where the incident took place.

  680. 

  681.    collaborative-intelligence:

  682.       Collaborative intelligence support language is a common language

  683.       to support analysts to perform their analysis to get crowdsourced

  684.       support when using threat intelligence sharing platform like MISP.

  685. 

  686.    common-taxonomy:

  687.       The Common Taxonomy for Law Enforcement and The National Network

  688.       of CSIRTs bridges the gap between the CSIRTs and international Law

  689.       Enforcement communities by adding a legislative framework to

  690.       facilitate the harmonisation of incident reporting to competent

  691.       authorities, the development of useful statistics and sharing

  692.       information within the entire cybercrime ecosystem.

  693. 

  694.    copine-scale:

  695.       The COPINE Scale is a rating system created in Ireland and used in

  696.       the United Kingdom to categorise the severity of images of child

  697.       sex abuse.

  698. 

  699.    cryptocurrency-threat:

  700.       Threats targetting cryptocurrency, based on CipherTrace report.

  701. 

  702.    csirt_case_classification:

  703.       FIRST CSIRT Case Classification.

  704. 

  705.    cssa:

  706.       The CSSA agreed sharing taxonomy.

  707. 

  708.    cyber-threat-framework:

  709.       Cyber Threat Framework was developed by the US Government to

  710.       enable consistent characterization and categorization of cyber

  711.       threat events, and to identify trends or changes in the activities

  712.       of cyber adversaries. <https://www.dni.gov/index.php/cyber-threat-

  713.       framework>

  714. 

  715.    data-classification:

  716.       Data classification for data potentially at risk of exfiltration

  717.       based on table 2.1 of Solving Cyber Risk book.

  718. 

  719.    dcso-sharing:

  720.       DCSO Sharing Taxonomy to classify certain types of MISP events

  721.       using the DCSO Event Guide

  722. 

  723.    ddos:

  724. 

  725. 

  726. 

  727. 

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  730. Internet-Draft            MISP taxonomy format             November 2017

  731. 

  732. 

  733.       Distributed Denial of Service - or short: DDoS - taxonomy supports

  734.       the description of Denial of Service attacks and especially the

  735.       types they belong too.

  736. 

  737.    de-vs:

  738.       Taxonomy for the handling of protectively marked information in

  739.       MISP with German (DE) Government classification markings (VS)

  740. 

  741.    dhs-ciip-sectors:

  742.       DHS critical sectors as described in <https://www.dhs.gov/

  743.       critical-infrastructure-sectors>.

  744. 

  745.    diamond-model:

  746.       The Diamond Model for Intrusion Analysis, a phase-based model

  747.       developed by Lockheed Martin, aims to help categorise and identify

  748.       the stage of an attack.

  749. 

  750.    dni-ism:

  751.       ISM (Information Security Marking Metadata) V13 as described by

  752.       DNI.gov (Director of National Intelligence - US).

  753. 

  754.    domain-abuse:

  755.       Taxonomy to tag domain names used for cybercrime.

  756. 

  757.    drugs:

  758.       A taxonomy based on the superclass and class of drugs, based on

  759.       <https://www.drugbank.ca/releases/latest>

  760. 

  761.    economical-impact:

  762.       Economical impact is a taxonomy to describe the financial impact

  763.       as positive or negative gain to the tagged information.

  764. 

  765.    ecsirt:

  766.       eCSIRT incident classification Appendix C of the eCSIRT EU project

  767.       including IntelMQ updates.

  768. 

  769.    enisa:

  770.       ENISA Threat Taxonomy - A tool for structuring threat information

  771.       as published in <https://www.enisa.europa.eu/topics/threat-risk-

  772.       management/threats-and-trends/enisa-threat-landscape/etl2015/

  773.       enisa-threat-taxonomy-a-tool-for-structuring-threat-information>

  774. 

  775.    estimative-language:

  776.       Estimative language - including likelihood or probability of event

  777.       based on the Intelligence Community Directive 203 (ICD 203)

  778.       (6.2.(a)) and JP 2-0, Joint Intelligence.

  779. 

  780.    eu-marketop-and-publicadmin:

  781. 

  782. 

  783. 

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  786. Internet-Draft            MISP taxonomy format             November 2017

  787. 

  788. 

  789.       Market operators and public administrations that must comply to

  790.       some notifications requirements under EU NIS directive.

  791. 

  792.    eu-nis-sector-and-subsectors:

  793.       Sectors and sub sectors as identified by the NIS Directive.

  794. 

  795.    euci:

  796.       EU classified information (EUCI) means any information or material

  797.       designated by a EU security classification, the unauthorised

  798.       disclosure of which could cause varying degrees of prejudice to

  799.       the interests of the European Union or of one or more of the

  800.       Member States as described in COUNCIL DECISION of 23 September

  801.       2013 on the security rules for protecting EU classified

  802.       information

  803. 

  804.    europol-event:

  805.       EUROPOL type of events taxonomy.

  806. 

  807.    europol-incident:

  808.       EUROPOL class of incident taxonomy.

  809. 

  810.    event-assessment:

  811.       A series of assessment predicates describing the event assessment

  812.       performed to make judgement(s) under a certain level of

  813.       uncertainty.

  814. 

  815.    event-classification:

  816.       Event Classification.

  817. 

  818.    exercise:

  819.       Exercise is a taxonomy to describe if the information is part of

  820.       one or more cyber or crisis exercise.

  821. 

  822.    false-positive:

  823.       This taxonomy aims to ballpark the expected amount of false

  824.       positives.

  825. 

  826.    file-type:

  827.       List of known file types.

  828. 

  829.    flesch-reading-ease:

  830.       Flesch Reading Ease is a revised system for determining the

  831.       comprehension difficulty of written material.  The scoring of the

  832.       flesh score can have a maximum of 121.22 and there is no limit on

  833.       how low a score can be (negative score are valid).

  834. 

  835.    fpf:

  836. 

  837. 

  838. 

  839. 

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  842. Internet-Draft            MISP taxonomy format             November 2017

  843. 

  844. 

  845.       The Future of Privacy Forum (FPF) visual guide to practical de-

  846.       identification [1] taxonomy is used to evaluate the degree of

  847.       identifiability of personal data and the types of pseudonymous

  848.       data, de-identified data and anonymous data.  The work of FPF is

  849.       licensed under a creative commons attribution 4.0 international

  850.       license.

  851. 

  852.    fr-classif:

  853.       French gov information classification system.

  854. 

  855.    gdpr:

  856.       Taxonomy related to the REGULATION (EU) 2016/679 OF THE EUROPEAN

  857.       PARLIAMENT AND OF THE COUNCIL on the protection of natural persons

  858.       with regard to the processing of personal data and on the free

  859.       movement of such data, and repealing Directive 95/46/EC (General

  860.       Data Protection Regulation)

  861. 

  862.    gsma-attack-category:

  863.       Taxonomy used by GSMA for their information sharing program with

  864.       telco describing the attack categories

  865. 

  866.    gsma-fraud:

  867.       Taxonomy used by GSMA for their information sharing program with

  868.       telco describing the various aspects of fraud

  869. 

  870.    gsma-network-technology:

  871.       Taxonomy used by GSMA for their information sharing program with

  872.       telco describing the types of infrastructure.  WiP

  873. 

  874.    honeypot-basic:

  875.       Christian Seifert, Ian Welch, Peter Komisarczuk, 'Taxonomy of

  876.       Honeypots', Technical Report CS-TR-06/12, VICTORIA UNIVERSITY OF

  877.       WELLINGTON, School of Mathematical and Computing Sciences, June

  878.       2006, <http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-

  879.       06/CS-TR-06-12.pdf>

  880. 

  881.    iep:

  882.       Forum of Incident Response and Security Teams (FIRST) Information

  883.       Exchange Policy (IEP) framework.

  884. 

  885.    ifx-vetting:

  886.       The IFX taxonomy is used to categorise information (MISP events

  887.       and attributes) to aid in the intelligence vetting process

  888. 

  889.    incident-disposition:

  890.       How an incident is classified in its process to be resolved.  The

  891.       taxonomy is inspired from NASA Incident Response and Management

  892.       Handbook.

  893. 

  894. 

  895. 

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  898. Internet-Draft            MISP taxonomy format             November 2017

  899. 

  900. 

  901.    infoleak:

  902.       A taxonomy describing information leaks and especially information

  903.       classified as being potentially leaked.

  904. 

  905.    information-security-data-source:

  906.       Taxonomy to classify the information security data sources

  907. 

  908.    information-security-indicators:

  909.       Information security indicators have been standardized by the ETSI

  910.       Industrial Specification Group (ISG) ISI.  These indicators

  911.       provide the basis to switch from a qualitative to a quantitative

  912.       culture in IT Security Scope of measurements: External and

  913.       internal threats (attempt and success), user's deviant behaviours,

  914.       nonconformities and/or vulnerabilities (software, configuration,

  915.       behavioural, general security framework).  ETSI GS ISI 001-1

  916.       (V1.1.2): ISI Indicators

  917. 

  918.    interception-method:

  919.       The interception method used to intercept traffic.

  920. 

  921.    kill-chain:

  922.       Cyber Kill Chain from Lockheed Martin as described in

  923.       Intelligence-Driven Computer Network Defense Informed by Analysis

  924.       of Adversary Campaigns and Intrusion Kill Chains.

  925. 

  926.    maec-delivery-vectors:

  927.       Vectors used to deliver malware based on MAEC 5.0

  928. 

  929.    maec-malware-behavior:

  930.       Malware behaviours based on MAEC 5.0

  931. 

  932.    maec-malware-capabilities:

  933.       Malware Capabilities based on MAEC 5.0

  934. 

  935.    maec-malware-obfuscation-methods:

  936.       Obfuscation methods used by malware based on MAEC 5.0

  937. 

  938.    malware_classification:

  939.       Malware classification based on a SANS whitepaper about malware.

  940. 

  941.    misp:

  942.       Internal MISP taxonomy.

  943. 

  944.    monarc-threat:

  945.       MONARC threat taxonomy.

  946. 

  947.    ms-caro-malware:

  948. 

  949. 

  950. 

  951. 

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  954. Internet-Draft            MISP taxonomy format             November 2017

  955. 

  956. 

  957.       Malware Type and Platform classification based on Microsoft's

  958.       implementation of the Computer Antivirus Research Organization

  959.       (CARO) Naming Scheme and Malware Terminology.

  960. 

  961.    ms-caro-malware-full:

  962.       Malware Type and Platform classification based on Microsoft's

  963.       implementation of the Computer Antivirus Research Organization

  964.       (CARO) Naming Scheme and Malware Terminology.

  965. 

  966.    nato:

  967.       Marking of Classified and Unclassified materials as described by

  968.       the North Atlantic Treaty Organization, NATO.

  969. 

  970.    nis:

  971.       NIS Cybersecurity Incident Taxonomy.

  972. 

  973.    open_threat:

  974.       Open Threat Taxonomy v1.1 base on James Tarala of SANS ref. -

  975.       <http://www.auditscripts.com/resources/

  976.       open_threat_taxonomy_v1.1a.pdf>

  977. 

  978.    osint:

  979.       Open Source Intelligence - Classification (MISP taxonomies).

  980. 

  981.    passivetotal:

  982.       Tags for RiskIQ's passivetotal service

  983. 

  984.    pentest:

  985.       Penetration test (pentest) classification.

  986. 

  987.    priority-level:

  988.       After an incident is scored, it is assigned a priority level.  The

  989.       six levels listed below are aligned with NCCIC, DHS, and the CISS

  990.       to help provide a common lexicon when discussing incidents.  This

  991.       priority assignment drives NCCIC urgency, pre-approved incident

  992.       response offerings, reporting requirements, and recommendations

  993.       for leadership escalation.  Generally, incident priority

  994.       distribution should follow a similar pattern to the graph below.

  995.       Based on <https://www.us-cert.gov/NCCIC-Cyber-Incident-Scoring-

  996.       System>.

  997. 

  998.    rsit:

  999.       Reference Security Incident Classification Taxonomy.

  1000. 

  1001.    rt_event_status:

  1002.       Status of events used in Request Tracker.

  1003. 

  1004.    runtime-packer:

  1005. 

  1006. 

  1007. 

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  1010. Internet-Draft            MISP taxonomy format             November 2017

  1011. 

  1012. 

  1013.       Runtime or software packer used to combine compressed data with

  1014.       the decompression code.  The decompression code can add additional

  1015.       obfuscations mechanisms including polymorphic-packer or other

  1016.       obfuscation techniques.  This taxonomy lists all the known or

  1017.       official packer used for legitimate use or for packing malicious

  1018.       binaries.

  1019. 

  1020.    smart-airports-threats:

  1021.       Threat taxonomy in the scope of securing smart airports by ENISA.

  1022. 

  1023.    stealth_malware:

  1024.       Classification based on malware stealth techniques.

  1025. 

  1026.    stix-ttp:

  1027.       Representation of the behavior or modus operandi of cyber

  1028.       adversaries (a.k.a TTP) as normalized in STIX

  1029. 

  1030.    targeted-threat-index:

  1031.       The Targeted Threat Index is a metric for assigning an overall

  1032.       threat ranking score to email messages that deliver malware to a

  1033.       victim's computer.  The TTI metric was first introduced at SecTor

  1034.       2013 by Seth Hardy as part of the talk "RATastrophe: Monitoring a

  1035.       Malware Menagerie" along with Katie Kleemola and Greg Wiseman.

  1036. 

  1037.    tlp:

  1038.       The Traffic Light Protocol - or short: TLP - was designed with the

  1039.       objective to create a favorable classification scheme for sharing

  1040.       sensitive information while keeping the control over its

  1041.       distribution at the same time.  Extended with TLP:EX:CHR.

  1042. 

  1043.    tor:

  1044.       Taxonomy to describe Tor network infrastructure

  1045. 

  1046.    type:

  1047.       Taxonomy to describe different types of intelligence gathering

  1048.       discipline which can be described the origin of intelligence.

  1049. 

  1050.    use-case-applicability:

  1051.       The Use Case Applicability categories reflect standard resolution

  1052.       categories, to clearly display alerting rule configuration

  1053.       problems.

  1054. 

  1055.    veris:

  1056.       Vocabulary for Event Recording and Incident Sharing (VERIS).

  1057. 

  1058.    vocabulaire-des-probabilites-estimatives:

  1059.       Vocabulaire des probabilites estimatives

  1060. 

  1061. 

  1062. 

  1063. 

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  1066. Internet-Draft            MISP taxonomy format             November 2017

  1067. 

  1068. 

  1069.    workflow:

  1070.       Workflow support language is a common language to support

  1071.       intelligence analysts to perform their analysis on data and

  1072.       information.

  1073. 

  1074. 5.  JSON Schema

  1075. 

  1076.    The JSON Schema [JSON-SCHEMA] below defines the structure of the MISP

  1077.    taxonomy document as literally described before.  The JSON Schema is

  1078.    used validating a MISP taxonomy.  The validation is a _MUST_ if the

  1079.    taxonomy is included in the MISP taxonomies directory.

  1080. 

  1081.     {

  1082.       "$schema": "http://json-schema.org/schema#",

  1083.       "title": "Validator for misp-taxonomies",

  1084.       "id": "https://www.github.com/MISP/misp-taxonomies/schema.json",

  1085.       "defs": {

  1086.         "entry": {

  1087.           "type": "array",

  1088.           "uniqueItems": true,

  1089.           "items": {

  1090.             "type": "object",

  1091.             "additionalProperties": false,

  1092.             "properties": {

  1093.               "numerical_value": {

  1094.                 "type": "number"

  1095.               },

  1096.               "expanded": {

  1097.                 "type": "string"

  1098.               },

  1099.               "description": {

  1100.                 "type": "string"

  1101.               },

  1102.               "colour": {

  1103.                 "type": "string"

  1104.               },

  1105.               "value": {

  1106.                 "type": "string"

  1107.               },

  1108.               "required": [

  1109.                 "value"

  1110.               ]

  1111.             }

  1112.           }

  1113.         },

  1114.         "values": {

  1115.           "type": "array",

  1116.           "uniqueItems": true,

  1117. 

  1118. 

  1119. 

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  1122. Internet-Draft            MISP taxonomy format             November 2017

  1123. 

  1124. 

  1125.           "items": {

  1126.             "type": "object",

  1127.             "additionalProperties": false,

  1128.             "properties": {

  1129.               "entry": {

  1130.                 "$ref": "#/defs/entry"

  1131.               },

  1132.               "predicate": {

  1133.                 "type": "string"

  1134.               }

  1135.             },

  1136.             "required": [

  1137.               "predicate"

  1138.             ]

  1139.           }

  1140.         },

  1141.         "predicates": {

  1142.           "type": "array",

  1143.           "uniqueItems": true,

  1144.           "items": {

  1145.             "type": "object",

  1146.             "additionalProperties": false,

  1147.             "properties": {

  1148.               "numerical_value": {

  1149.                 "type": "number"

  1150.               },

  1151.               "colour": {

  1152.                 "type": "string"

  1153.               },

  1154.               "description": {

  1155.                 "type": "string"

  1156.               },

  1157.               "expanded": {

  1158.                 "type": "string"

  1159.               },

  1160.               "value": {

  1161.                 "type": "string"

  1162.               },

  1163.               "exclusive": {

  1164.                 "type": "boolean"

  1165.               },

  1166.               "required": [

  1167.                 "value"

  1168.               ]

  1169.             }

  1170.           }

  1171.         }

  1172.       },

  1173. 

  1174. 

  1175. 

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  1178. Internet-Draft            MISP taxonomy format             November 2017

  1179. 

  1180. 

  1181.       "type": "object",

  1182.       "additionalProperties": false,

  1183.       "properties": {

  1184.         "version": {

  1185.           "type": "integer"

  1186.         },

  1187.         "description": {

  1188.           "type": "string"

  1189.         },

  1190.         "expanded": {

  1191.           "type": "string"

  1192.         },

  1193.         "namespace": {

  1194.           "type": "string"

  1195.         },

  1196.         "exclusive": {

  1197.           "type": "boolean"

  1198.         },

  1199.         "type": {

  1200.           "type": "array",

  1201.           "uniqueItems": true,

  1202.           "items": {

  1203.             "type": "string",

  1204.             "enum": [

  1205.               "org",

  1206.               "user",

  1207.               "attribute",

  1208.               "event"

  1209.             ]

  1210.           }

  1211.         },

  1212.         "refs": {

  1213.           "type": "array",

  1214.           "uniqueItems": true,

  1215.           "items": {

  1216.             "type": "string"

  1217.           }

  1218.         },

  1219.         "predicates": {

  1220.           "$ref": "#/defs/predicates"

  1221.         },

  1222.         "values": {

  1223.           "$ref": "#/defs/values"

  1224.         }

  1225.       },

  1226.       "required": [

  1227.         "namespace",

  1228.         "description",

  1229. 

  1230. 

  1231. 

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  1234. Internet-Draft            MISP taxonomy format             November 2017

  1235. 

  1236. 

  1237.         "version",

  1238.         "predicates"

  1239.       ]

  1240.     }

  1241. 

  1242. 6.  Acknowledgements

  1243. 

  1244.    The authors wish to thank all the MISP community who are supporting

  1245.    the creation of open standards in threat intelligence sharing.

  1246. 

  1247. 7.  References

  1248. 

  1249. 7.1.  Normative References

  1250. 

  1251.    [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate

  1252.               Requirement Levels", BCP 14, RFC 2119,

  1253.               DOI 10.17487/RFC2119, March 1997,

  1254.               <https://www.rfc-editor.org/info/rfc2119>.

  1255. 

  1256.    [RFC8259]  Bray, T., Ed., "The JavaScript Object Notation (JSON) Data

  1257.               Interchange Format", STD 90, RFC 8259,

  1258.               DOI 10.17487/RFC8259, December 2017,

  1259.               <https://www.rfc-editor.org/info/rfc8259>.

  1260. 

  1261. 7.2.  Informative References

  1262. 

  1263.    [JSON-SCHEMA]

  1264.               "JSON Schema: A Media Type for Describing JSON Documents",

  1265.               2016,

  1266.               <https://tools.ietf.org/html/draft-wright-json-schema>.

  1267. 

  1268.    [machine-tags]

  1269.               "Machine tags", 2007,

  1270.               <https://www.flickr.com/groups/51035612836@N01/

  1271.               discuss/72157594497877875/>.

  1272. 

  1273.    [MISP-P]   MISP, "MISP Project - Malware Information Sharing Platform

  1274.               and Threat Sharing", <https://github.com/MISP>.

  1275. 

  1276.    [MISP-T]   MISP, "MISP Taxonomies - shared and common vocabularies of

  1277.               tags", <https://github.com/MISP/misp-taxonomies>.

  1278. 

  1279. 7.3.  URIs

  1280. 

  1281.    [1] https://fpf.org/2016/04/25/a-visual-guide-to-practical-data-de-

  1282.        identification/

  1283. 

  1284. 

  1285. 

  1286. 

  1287. 

  1288. Dulaunoy & Iklody         Expires June 2, 2018                 [Page 23]

  1289. 

  1290. Internet-Draft            MISP taxonomy format             November 2017

  1291. 

  1292. 

  1293. Authors' Addresses

  1294. 

  1295.    Alexandre Dulaunoy

  1296.    Computer Incident Response Center Luxembourg

  1297.    16, bd d'Avranches

  1298.    Luxembourg  L-1611

  1299.    Luxembourg

  1300. 

  1301.    Phone: +352 247 88444

  1302.    Email: alexandre.dulaunoy@circl.lu

  1303. 

  1304. 

  1305.    Andras Iklody

  1306.    Computer Incident Response Center Luxembourg

  1307.    16, bd d'Avranches

  1308.    Luxembourg  L-1611

  1309.    Luxembourg

  1310. 

  1311.    Phone: +352 247 88444

  1312.    Email: andras.iklody@circl.lu

  1313. 

  1314. 

  1315. 

  1316. 

  1317. 

  1318. 

  1319. 

  1320. 

  1321. 

  1322. 

  1323. 

  1324. 

  1325. 

  1326. 

  1327. 

  1328. 

  1329. 

  1330. 

  1331. 

  1332. 

  1333. 

  1334. 

  1335. 

  1336. 

  1337. 

  1338. 

  1339. 

  1340. 

  1341. 

  1342. 

  1343. 

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