



Network Working Group                                        A. Dulaunoy
Internet-Draft                                                 A. Iklody
Intended status: Informational                                     CIRCL
Expires: 4 January 2026                                      3 July 2025


                          MISP taxonomy format
                 draft-dulaunoy-misp-taxonomy-format-11

Abstract

   This document outlines the MISP taxonomy format, a straightforward
   JSON structure designed to represent machine tags (also known as
   triple tags) vocabularies.  A public directory, referred to as MISP
   taxonomies, is available and leverages this format.  These taxonomies
   are used to classify cybersecurity events, threats, suspicious
   activities, and indicators.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
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   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on 4 January 2026.

Copyright Notice

   Copyright (c) 2025 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
   license-info) in effect on the date of publication of this document.
   Please review these documents carefully, as they describe your rights
   and restrictions with respect to this document.






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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Conventions and Terminology . . . . . . . . . . . . . . .   3
   2.  Format  . . . . . . . . . . . . . . . . . . . . . . . . . . .   3
     2.1.  Overview  . . . . . . . . . . . . . . . . . . . . . . . .   3
     2.2.  predicates  . . . . . . . . . . . . . . . . . . . . . . .   4
     2.3.  values  . . . . . . . . . . . . . . . . . . . . . . . . .   4
     2.4.  uuid  . . . . . . . . . . . . . . . . . . . . . . . . . .   4
     2.5.  optional fields . . . . . . . . . . . . . . . . . . . . .   4
       2.5.1.  colour  . . . . . . . . . . . . . . . . . . . . . . .   4
       2.5.2.  description . . . . . . . . . . . . . . . . . . . . .   5
       2.5.3.  numerical_value . . . . . . . . . . . . . . . . . . .   5
   3.  Directory . . . . . . . . . . . . . . . . . . . . . . . . . .   7
     3.1.  Sample Manifest . . . . . . . . . . . . . . . . . . . . .   7
   4.  Sample Taxonomy in MISP taxonomy format . . . . . . . . . . .   7
     4.1.  Admiralty Scale Taxonomy  . . . . . . . . . . . . . . . .   7
     4.2.  Open Source Intelligence - Classification . . . . . . . .  10
     4.3.  Available taxonomies in the public repository . . . . . .  14
   5.  JSON Schema . . . . . . . . . . . . . . . . . . . . . . . . .  25
   6.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  28
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  28
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .  29
     7.2.  Informative References  . . . . . . . . . . . . . . . . .  29
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  29

1.  Introduction

   Sharing threat information has become a fundamental requirement in
   the Internet security and intelligence community at large.  This
   information can include indicators of compromise, malicious file
   indicators, financial fraud indicators, or even detailed information
   about a threat actor.  Classification plays a crucial role while
   sharing such indicators or information, ensuring adequate
   distribution, understanding, validation, or action regarding the
   shared information.  The MISP taxonomies are a public repository of
   known vocabularies that can be utilized in threat information
   sharing.

   Machine tags were introduced in 2007 [machine-tags] to allow users to
   be more precise when tagging their pictures with geolocation.  So a
   machine tag is a tag which uses a special syntax to provide more
   information to users and machines.  Machine tags are also known as
   triple tags due to their format.







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   In the MISP taxonomy context, machine tags help analysts to classify
   their cybersecurity events, indicators or threats.  MISP taxonomies
   can be used for classification, filtering, triggering actions or
   visualisation depending on their use in threat intelligence platforms
   such as MISP [MISP-P].

1.1.  Conventions and Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

2.  Format

   A machine tag is composed of a namespace (MUST), a predicate (MUST)
   and an optional value (OPTIONAL).

   Machine tags are represented as a string.  Below listed are a set of
   sample machine tags for different namespaces such as tlp, admiralty-
   scale and osint.

   tlp:amber
   admiralty-scale:information-credibility="1"
   osint:source-type="blog-post"

   The MISP taxonomy format describes how to define a machine tag
   namespace in a parseable format.  The objective is to provide a
   simple format to describe machine tag (aka triple tag) vocabularies.

2.1.  Overview

   The MISP taxonomy format uses the JSON [RFC8259] format.  Each
   namespace is represented as a JSON object with meta information
   including the following fields: namespace, description, version,
   type.

   namespace defines the overall namespace of the machine tag.  The
   namespace is represented as a string and MUST be present.  The
   description is represented as a string and MUST be present.  A
   version is represented as a unsigned integer MUST be present.  A type
   defines where a specific taxonomy is applicable and a type can be
   applicable at event, user or org level.  The type is represented as
   an array containing one or more type and SHOULD be present.  If a
   type is not mentioned, by default, the taxonomy is applicable at
   event level only.  An exclusive boolean property MAY be present and
   defines at namespace level if the predicates are mutually exclusive.





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   predicates defines all the predicates available in the namespace
   defined. predicates is represented as an array of JSON objects.
   predicates MUST be present and MUST at least content one element.

   values defines all the values for each predicate in the namespace
   defined. values SHOULD be present.

2.2.  predicates

   The predicates array contains one or more JSON objects which lists
   all the possible predicates.  The JSON object contains two fields:
   value and expanded. value MUST be present. expanded SHOULD be
   present. value is represented as a string and describes the predicate
   value.  The predicate value MUST not contain spaces or colons.
   expanded is represented as a string and describes the human-readable
   version of the predicate value.  An exclusive property MAY be present
   and defines at namespace level if the values are mutually exclusive.

2.3.  values

   The values array contain one or more JSON objects which lists all the
   possible values of a predicate.  The JSON object contains two fields:
   predicate and entry. predicate is represented as a string and
   describes the predicate value. entry is an array with one or more
   JSON objects.  The JSON object contains two fields: value and
   expanded. value MUST be present. expanded SHOULD be present. value is
   represented as a string and describes the machine parsable value.
   expanded is represented as a string and describes the human-readable
   version of the value.

2.4.  uuid

   uuid represents the Universally Unique IDentifier (UUID) [RFC4122] of
   the taxonomy.  The uuid MUST be preserved for any updates of the same
   taxonomy.  UUID version 4 or version 5 is RECOMMENDED when assigning
   it to a new taxonomy. uuid MUST be present at predicate and value
   level.

   uuid is represented as a JSON string. uuid MUST be present.

2.5.  optional fields

2.5.1.  colour

   colour fields MAY be used at predicates or values level to set a
   specify colour that MAY be used by the implementation.  The colour
   field is described as an RGB colour fill in hexadecimal
   representation.



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

"predicates": [
    {
      "colour": "#FF2B2B",
      "description": "For the eyes and ears of individual recipients only, no further disclosure. Sources may use TLP:RED when information cannot be effectively acted upon without significant risk for the privacy, reputation, or operations of the organizations involved. Recipients may therefore not share TLP:RED information with anyone else. In the context of a meeting, for example, TLP:RED information is limited to those present at the meeting.",
      "expanded": "(TLP:RED) For the eyes and ears of individual recipients only, no further disclosure.",
      "value": "red",
      "uuid": "845de186-eefa-57a6-be53-a0d36b7c6d2e"
    },
    {
      "colour": "#FFC000",
      "description": "Limited disclosure, recipients can only spread this on a need-to-know basis within their organization and its clients. Sources may use TLP:AMBER when information requires support to be effectively acted upon, yet carries risk to privacy, reputation, or operations if shared outside of the organizations involved. Recipients may share TLP:AMBER information with members of their own organization and its clients, but only on a need-to-know basis to protect their organization and its clients and prevent further harm. Note that TLP:AMBER+STRICT restricts sharing to the organization only.",
      "expanded": "(TLP:AMBER) Limited disclosure, recipients can only spread this on a need-to-know basis within their organization and its clients.",
      "value": "amber",
      "uuid": "8906fac2-2d9c-55fd-af11-0f35e91a6347"
    }...]

2.5.2.  description

   description fields MAY be used at predicates or values level to add a
   descriptive and human-readable information about the specific
   predicate or value.  The field is represented as a string.
   Implementations MAY use the description field to improve more
   contextual information.  The description at the namespace level is a
   MUST as described above.

2.5.3.  numerical_value

   numerical_value fields MAY be used at a predicate or value level to
   add a machine-readable numeric value to a specific predicate or
   value.  The field is represented as a JSON number.  Implementations
   SHOULD use the decimal value provided to support scoring or
   filtering.

   The decimal range for numerical_value SHOULD use a range from 0 up to
   100.  The range is recommended to support common mathematical
   properties among taxonomies.

   Example use of the numerical_value in the MISP confidence level:











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     {
         "predicate": "confidence-level",
         "entry": [
           {
             "expanded": "Completely confident",
             "value": "completely-confident",
             "numerical_value": 100,
             "uuid": "34ef60ef-bb46-5810-9046-4add93559164"
           },
           {
             "expanded": "Usually confident",
             "value": "usually-confident",
             "numerical_value": 75,
             "uuid": "0df28ca0-7237-58e5-ba64-8f1fb6706571"
           },
           {
             "expanded": "Fairly confident",
             "value": "fairly-confident",
             "numerical_value": 50,
             "uuid": "ae8c1689-f9b5-54f9-a267-e87f545bb7af"
           },
           {
             "expanded": "Rarely confident",
             "value": "rarely-confident",
             "numerical_value": 25,
             "uuid": "173014d7-f408-5eb5-b9f6-ac530c04fc2e"
           },
           {
             "expanded": "Unconfident",
             "value": "unconfident",
             "numerical_value": 0,
             "uuid": "3e9826a6-535a-555b-8ee0-54668d6af6ff"
           },
           {
             "expanded": "Confidence cannot be evaluated",
             "value": "confidence-cannot-be-evaluated",
             "numerical_value": 50,
             "uuid": "021873dd-14cd-5c9b-bc2f-f4a5f7a6c1bc"
           }
         ]
       }










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3.  Directory

   The MISP taxonomies directory is publicly available [MISP-T] in a git
   repository.  The repository contains a directory per namespace then a
   file machinetag.json which contains the taxonomy as described in the
   format above.  In the root of the repository, a MANIFEST.json exists
   containing a list of all the taxonomies.

   The MANIFEST.json file is composed of an JSON object with metadata
   like version, license, description, url and path.  A taxonomies array
   describes the taxonomy available with the description, name and
   version field.

3.1.  Sample Manifest

 {
   "version": "20161009",
   "license": "CC-0",
   "description": "Manifest file of MISP taxonomies available.",
   "url":
     "https://raw.githubusercontent.com/MISP/misp-taxonomies/master/",
   "path": "machinetag.json",
   "taxonomies": [
     {
       "description": "The Admiralty Scale (also called the NATO System)
                       is used to rank the reliability of a source and
                       the credibility of an information.",
       "name": "admiralty-scale",
       "version": 1
     },
     {
       "description": "Open Source Intelligence - Classification.",
       "name": "osint",
       "version": 2
     }]
 }

4.  Sample Taxonomy in MISP taxonomy format

4.1.  Admiralty Scale Taxonomy

{
  "namespace": "admiralty-scale",
  "description": "The Admiralty Scale or Ranking (also called the NATO System) is used to rank the reliability of a source and the credibility of an information. Reference based on FM 2-22.3 (FM 34-52) HUMAN INTELLIGENCE COLLECTOR OPERATIONS and NATO documents.",
  "version": 5,
  "predicates": [
    {
      "value": "source-reliability",



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      "expanded": "Source Reliability",
      "exclusive": true,
      "uuid": "f7b68aff-8b01-517c-a272-13dc713d01e5"
    },
    {
      "value": "information-credibility",
      "expanded": "Information Credibility",
      "exclusive": true,
      "uuid": "78ceeb42-699a-5882-882a-5b8cdebb4565"
    }
  ],
  "values": [
    {
      "predicate": "source-reliability",
      "entry": [
        {
          "value": "a",
          "expanded": "Completely reliable",
          "description": "No doubt of authenticity, trustworthiness, or competency; has a history of complete reliability",
          "numerical_value": 100,
          "uuid": "bcb65b93-062a-5b9f-8d69-9a1f23d25827"
        },
        {
          "value": "b",
          "expanded": "Usually reliable",
          "description": "Minor doubt about authenticity, trustworthiness, or competency; has a history of valid information most of the time",
          "numerical_value": 75,
          "uuid": "61cbed64-0bdf-5467-9b28-e94aa3ee87c5"
        },
        {
          "value": "c",
          "expanded": "Fairly reliable",
          "description": "Doubt of authenticity, trustworthiness, or competency but has provided valid information in the past",
          "numerical_value": 50,
          "uuid": "046949ad-cdc0-53a6-b3ae-762b7befbd77"
        },
        {
          "value": "d",
          "expanded": "Not usually reliable",
          "description": "Significant doubt about authenticity, trustworthiness, or co mpetency but has provided valid information in the past",
          "numerical_value": 25,
          "uuid": "3125e574-7adc-5e08-b5ca-86d7d1afb2a3"
        },
        {
          "value": "e",
          "expanded": "Unreliable",
          "description": "Lacking in authenticity, trustworthiness, and competency; history of invalid information",
          "numerical_value": 0,



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          "uuid": "6615786b-22e5-58d1-9bf5-4a790975e461"
        },
        {
          "value": "f",
          "expanded": "Reliability cannot be judged",
          "description": "No basis exists for evaluating the reliability of the source",
          "numerical_value": 50,
          "uuid": "5c877b09-bc69-5b92-bd34-099b4789d6ab"
        },
        {
          "value": "g",
          "expanded": "Deliberatly deceptive",
          "numerical_value": 0,
          "uuid": "47487617-8fe2-518a-9472-178e88a45e6f"
        }
      ]
    },
    {
      "predicate": "information-credibility",
      "entry": [
        {
          "value": "1",
          "expanded": "Confirmed by other sources",
          "description": "Confirmed by other independent sources; logical in itself; Consistent with other information on the subject",
          "numerical_value": 100,
          "uuid": "7bc7c468-23c2-5ecc-9e4b-709dc89a7763"
        },
        {
          "value": "2",
          "expanded": "Probably true",
          "description": "Not confirmed; logical in itself; consistent with other information on the subject",
          "numerical_value": 75,
          "uuid": "802dafc1-6d38-58d0-923b-569655cd0b93"
        },
        {
          "value": "3",
          "expanded": "Possibly true",
          "description": "Not confirmed; reasonably logical in itself; agrees with some other information on the subject",
          "numerical_value": 50,
          "uuid": "c1553166-625b-5d93-9b9a-816f06663e28"
        },
        {
          "value": "4",
          "expanded": "Doubtful",
          "description": "Not confirmed; possible but not logical ; no other information on the subject",
          "numerical_value": 25,
          "uuid": "ec23824e-c6ef-59d4-9949-643974ad32c1"
        },



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        {
          "value": "5",
          "expanded": "Improbable",
          "description": "Not confirmed; not logical in itself; contradicted by other information on the subject",
          "numerical_value": 0,
          "uuid": "5d6bc630-004a-54d9-b285-566361eea3bd"
        },
        {
          "value": "6",
          "expanded": "Truth cannot be judged",
          "description": "No basis exists for evaluating the validity of the information",
          "numerical_value": 50,
          "uuid": "ac88352a-20bb-5ba2-89b4-d5f0dcb78658"
        }
      ]
    }
  ],
  "uuid": "97c896f5-df57-517f-be3b-46f7c2dfcaf4"
}

4.2.  Open Source Intelligence - Classification

{
  "predicates": [
    {
      "expanded": "Source Type",
      "value": "source-type",
      "uuid": "53268e72-c3c7-58c0-afa8-ab00a85b46e3"
    },
    {
      "expanded": "Lifetime of the information as Open Source Intelligence",
      "value": "lifetime",
      "uuid": "d14cfc15-c9bc-598b-829d-b757f4cb99cd"
    },
    {
      "expanded": "Certainty of the elements mentioned in this Open Source Intelligence",
      "value": "certainty",
      "uuid": "50358d08-be83-50ea-86c1-c305722c9f13"
    }
  ],
  "version": 11,
  "description": "Open Source Intelligence - Classification (MISP taxonomies)",
  "namespace": "osint",
  "values": [
    {
      "predicate": "source-type",
      "entry": [
        {



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          "value": "blog-post",
          "expanded": "Blog post",
          "uuid": "6110e86a-1752-5b8a-a51b-9899367370d6"
        },
        {
          "value": "microblog-post",
          "expanded": "Microblog post like Twitter",
          "uuid": "a9bb1865-9680-586b-8e97-2ab133668df6"
        },
        {
          "value": "technical-report",
          "expanded": "Technical or analysis report",
          "uuid": "ec22778f-e7d6-5a2a-9a0d-5c402bf22921"
        },
        {
          "value": "presentation",
          "expanded": "Presentation or slidedeck",
          "uuid": "27c02224-598c-5817-8a63-813f659c7aa3"
        },
        {
          "value": "news-report",
          "expanded": "News report",
          "uuid": "61a0a580-f713-5eee-b6a1-bf130d52a92d"
        },
        {
          "value": "pastie-website",
          "expanded": "Pastie-like website",
          "uuid": "b2e6b620-eb8a-502d-91e3-d29f169c3cf9"
        },
        {
          "value": "electronic-forum",
          "expanded": "Electronic forum",
          "uuid": "201c07ce-d9a8-572f-8510-6fc1bb8a379d"
        },
        {
          "value": "mailing-list",
          "expanded": "Mailing-list",
          "uuid": "e4211c17-51a6-5c0b-9bad-8e325e1e8ed3"
        },
        {
          "value": "block-or-filter-list",
          "expanded": "Block or Filter List",
          "uuid": "e5586337-617e-5b78-a9c0-788eeee284d8"
        },
        {
          "value": "source-code-repository",
          "expanded": "Source code repository",
          "uuid": "f447bdf8-b074-5f52-a8e8-21ad064efeaa"



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        },
        {
          "value": "accessible-evidence",
          "expanded": "Infrastructure allowing the gathering of the evidences such as open directories, public web services or left over on public services",
          "uuid": "cd56c2e3-d1fb-5cae-bc63-2fd65f4c59ec"
        },
        {
          "value": "expansion",
          "expanded": "Expansion",
          "uuid": "66cd8968-ffb7-53bb-96ed-02f9e7b79225"
        },
        {
          "value": "automatic-analysis",
          "expanded": "Automatic analysis including dynamic analysis or sandboxes output",
          "uuid": "74b1ec26-7ef6-56a4-95fd-2a3eef1ec863"
        },
        {
          "value": "automatic-collection",
          "expanded": "Automatic collection including honeypots, spamtraps or equivalent technologies",
          "uuid": "371a3857-c667-5a3a-8cdc-b0d661e6b116"
        },
        {
          "value": "manual-analysis",
          "expanded": "Manual analysis or investigation",
          "uuid": "c05f0a98-ae08-5eaf-8155-c95e67fce2d9"
        },
        {
          "value": "manual-collection",
          "expanded": "Manual collection from crawlers, honeypots, spamtraps, gathering tools or equivalent technologies",
          "uuid": "e02da304-217e-5745-88cd-6c7a8024af38"
        },
        {
          "value": "unknown",
          "expanded": "Unknown",
          "uuid": "c0ee6b06-cc26-5dfe-ad74-fe5acae742b7"
        },
        {
          "value": "other",
          "expanded": "Other source not specified in this list",
          "uuid": "8054767e-2eb7-53e4-9be6-8950236a192e"
        }
      ]
    },
    {
      "entry": [
        {
          "description": "Information available publicly on long-term",
          "expanded": "Perpetual",



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          "value": "perpetual",
          "uuid": "cafad670-0b27-5818-b86a-757529e56eee"
        },
        {
          "description": "Information available publicly on short-term",
          "expanded": "Ephemeral",
          "value": "ephemeral",
          "uuid": "97adb94b-133d-5f5f-af8f-bf2387bd62ca"
        }
      ],
      "predicate": "lifetime"
    },
    {
      "entry": [
        {
          "description": "Certainty",
          "expanded": "Certainty (probability equals 1 - 100%)",
          "value": "100",
          "numerical_value": 100,
          "uuid": "d97bd9d3-5718-5cb3-890a-6f44c313877c"
        },
        {
          "description": "Almost certain",
          "expanded": "Almost certain (probability equals 0.93 - 93%)",
          "value": "93",
          "numerical_value": 93,
          "uuid": "dfc3f3c5-fb77-58e9-a6c8-5b2ea9414db8"
        },
        {
          "description": "Probable",
          "expanded": "Probable (probability equals 0.75 - 75%)",
          "value": "75",
          "numerical_value": 75,
          "uuid": "a386a237-ed7d-5e8d-9cb3-27cc71dabd0e"
        },
        {
          "description": "Chances about even",
          "expanded": "Chances about even (probability equals 0.50 - 50%)",
          "value": "50",
          "numerical_value": 50,
          "uuid": "f530059e-558f-5919-aa7e-c0dabc163fe4"
        },
        {
          "description": "Probably not",
          "expanded": "Probably not (probability equals 0.30 - 30%)",
          "value": "30",
          "numerical_value": 30,
          "uuid": "66f8af84-735a-5ef2-8dac-b07f3b0df498"



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        },
        {
          "description": "Almost certainly not",
          "expanded": "Almost certainly not (probability equals 0.07 - 7%)",
          "value": "7",
          "numerical_value": 7,
          "uuid": "6e2f8efc-bac3-55ae-b7ee-b9a80a7dce73"
        },
        {
          "description": "Impossibility",
          "expanded": "Impossibility (probability equals 0 - 0%)",
          "value": "0",
          "numerical_value": 0,
          "uuid": "1479effb-9249-5503-8c63-a812d3bcb8b1"
        }
      ],
      "predicate": "certainty"
    }
  ],
  "uuid": "a86e35e6-b0cb-5b30-99df-013b40040cbc"
}

4.3.  Available taxonomies in the public repository

   The public directory of MISP taxonomies [MISP-T] contains more than
   150 taxonomies spanning various fields, including:

   CERT-XLM:  CERT-XLM Security Incident Classification.
   DFRLab-dichotomies-of-disinformation:  DFRLab Dichotomies of
      Disinformation.
   DML:  The Detection Maturity Level (DML) model is a capability
      maturity model for referencing ones maturity in detecting cyber
      attacks.  It's designed for organizations who perform intel-driven
      detection and respo nse and who put an emphasis on having a mature
      detection program.
   GrayZone:  Gray Zone of Active defense includes all elements which
      lay between reactive defense elements and offensive operations.
      It does fill the gray spot between them.  Taxo may be used for
      active defense planning o r modeling.
   PAP:  The Permissible Actions Protocol - or short: PAP - was designed
      to indicate how the received information can be used.
   access-method:  The access method used to remotely access a system.
   accessnow:  Access Now classification to classify an issue (such as
      security, human rights, youth rights).
   acs-marking:  The Access Control Specification (ACS) marking type
      defines the object types required to implement automated access
      control systems based on the relevant policies governing sharing
      between participants.



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   action-taken:  Action taken in the case of a security incident (CSIRT
      perspective).
   admiralty-scale:  The Admiralty Scale or Ranking (also called the
      NATO System) is used to rank the reliability of a source and the
      credibility of an information.  Reference based on FM 2-22.3 (FM
      34-52) HUMAN INTELLIGENCE COLL ECTOR OPERATIONS and NATO
      documents.
   adversary:  An overview and description of the adversary
      infrastructure
   ais-marking:  The AIS Marking Schema implementation is maintained by
      the National Cybersecurity and Communication Integration Center
      (NCCIC) of the U.S.  Department of Homeland Security (DHS)
   analyst-assessment:  A series of assessment predicates describing the
      analyst capabilities to perform analysis.  These assessment can be
      assigned by the analyst him/herself or by another party evaluating
      the analyst.
   approved-category-of-action:  A pre-approved category of action for
      indicators being shared with partners (MIMIC).
   artificial-satellites:  This taxonomy was designed to describe
      artificial satellites
   aviation:  A taxonomy describing security threats or incidents
      against the aviation sector.
   binary-class:  Custom taxonomy for types of binary file.
   cccs:  Internal taxonomy for CCCS.
   circl:  CIRCL Taxonomy - Schemes of Classification in Incident
      Response and Detection.
   cnsd:  La presente taxonomia es la primera versión disponible para el
      Centro Nacional de Seguridad Digital del Perú.
   coa:  Course of action taken within organization to discover, detect,
      deny, disrupt, degrade, deceive and/or destroy an attack.
   collaborative-intelligence:  Collaborative intelligence support
      language is a common language to support analysts to perform their
      analysis to get crowdsourced support when using threat
      intelligence sharing platform like MISP.  The objec tive of this
      language is to advance collaborative analysis and to share earlier
      than later.
   common-taxonomy:  Common Taxonomy for Law enforcement and CSIRTs
   copine-scale:  The COPINE Scale is a rating system created in Ireland
      and used in the United Kingdom to categorise the severity of
      images of child sex abuse.  The scale was developed by staff at
      the COPINE (Combating Paedop hile Information Networks in Europe)
      project.  The COPINE Project was founded in 1997, and is based in
      the Department of Applied Psychology, University College Cork,
      Ireland.
   course-of-action:  A Course Of Action analysis considers six
      potential courses of action for the development of a cyber
      security capability.
   crowdsec:  Crowdsec IP address classifications and behaviors



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      taxonomy.
   cryptocurrency-threat:  Threats targeting cryptocurrency, based on
      CipherTrace report.
   csirt-americas:  Taxonomía CSIRT Américas.
   csirt_case_classification:  It is critical that the CSIRT provide
      consistent and timely response to the customer, and that sensitive
      information is handled appropriately.  This document provides the
      guidelines needed for CSIRT Incident Managers (IM) to classify the
      case category, criticality level, and sensitivity level for each
      CSIRT case.  This information will be entered into the Incident
      Tracking System (ITS) when a case is created.  Cons istent case
      classification is required for the CSIRT to provide accurate
      reporting to management on a regular basis.  In addition, the
      classifications will provide CSIRT IM’s with proper case handling
      procedures and will form the basis of SLA’s between the CSIRT and
      other Company departments.
   cssa:  The CSSA agreed sharing taxonomy.
   cti:  Cyber Threat Intelligence cycle to control workflow state of
      your process.
   current-event:  Current events - Schemes of Classification in
      Incident Response and Detection
   cyber-threat-framework:  Cyber Threat Framework was developed by the
      US Government to enable consistent characterization and
      categorization of cyber threat events, and to identify trends or
      changes in the activities of cyber adversa ries.
      https://www.dni.gov/index.php/cyber-threat-framework
      (https://www.dni.gov/index.php/cyber-threat-framework)
   cycat:  Taxonomy used by CyCAT, the Universal Cybersecurity Resource
      Catalogue, to categorize the namespaces it supports and uses.
   cytomic-orion:  Taxonomy to describe desired actions for Cytomic
      Orion
   dark-web:  Criminal motivation and content detection the dark web: A
      categorisation model for law enforcement. ref: Janis Dalins,
      Campbell Wilson, Mark Carman.  Taxonomy updated by MISP Project
      and extended by the JRC ( Joint Research Centre) of the European
      Commission.
   data-classification:  Data classification for data potentially at
      risk of exfiltration based on table 2.1 of Solving Cyber Risk
      book.
   dcso-sharing:  Taxonomy defined in the DCSO MISP Event Guide.  It
      provides guidance for the creation and consumption of MISP events
      in a way that minimises the extra effort for the sending party,
      while enhancing the usefuln ess for receiving parties.
   ddos:  Distributed Denial of Service - or short: DDoS - taxonomy
      supports the description of Denial of Service attacks and
      especially the types they belong too.
   de-vs:  German (DE) Government classification markings (VS).
   death-possibilities:  Taxonomy of Death Possibilities



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   deception:  Deception is an important component of information
      operations, valuable for both offense and defense.
   detection-engineering:  Taxonomy related to detection engineering
      techniques
   dga:  A taxonomy to describe domain-generation algorithms often
      called DGA.  Ref: A Comprehensive Measurement Study of Domain
      Generating Malware Daniel Plohmann and others.
   dhs-ciip-sectors:  DHS critical sectors as in https://www.dhs.gov/
      critical-infrastructure-sectors (https://www.dhs.gov/critical-
      infrastructure-sectors)
   diamond-model:  The Diamond Model for Intrusion Analysis establishes
      the basic atomic element of any intrusion activity, the event,
      composed of four core features: adversary, infrastructure,
      capability, and victim.
   diamond-model-for-influence-operations:  The diamond model for
      influence operations analysis is a framework that leads analysts
      and researchers toward a comprehensive understanding of a malign
      influence campaign by addressing the socio-political, t echnical,
      and psychological aspects of the campaign.  The diamond model for
      influence operations analysis consists of 5 components: 4 corners
      and a core element.  The 4 corners are divided into 2 axes:
      influencer and audience on the socio-political axis, capabilities
      and infrastructure on the technical axis.  Narrative makes up the
      core of the diamond.
   dni-ism:  A subset of Information Security Marking Metadata ISM as
      required by Executive Order (EO) 13526.  As described by DNI.gov
      as Data Encoding Specifications for Information Security Marking
      Metadata in Controlle d Vocabulary Enumeration Values for ISM
   domain-abuse:  Domain Name Abuse - taxonomy to tag domain names used
      for cybercrime.
   doping-substances:  This taxonomy aims to list doping substances
   drugs:  A taxonomy based on the superclass and class of drugs.  Based
      on https://www.drugbank.ca/releases/latest
      (https://www.drugbank.ca/releases/latest)
   economical-impact:  Economic impact refers to a taxonomy used to
      describe whether financial effects are positive or negative
      outcomes related to tagged information.  For instance, data
      exfiltration loss represents a positive out come for an adversary.
   ecsirt:  Incident Classification by the ecsirt.net version mkVI of 31
      March 2015 enriched with IntelMQ taxonomy-type mapping.
   enisa:  The present threat taxonomy is an initial version that has
      been developed on the basis of available ENISA material.  This
      material has been used as an ENISA-internal structuring aid for
      information collection and threat consolidation purposes.  It
      emerged in the time period 2012-2015.
   estimative-language:  Estimative language to describe quality and





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      credibility of underlying sources, data, and methodologies based
      Intelligence Community Directive 203 (ICD 203) and JP 2-0, Joint
      Intelligence
   eu-marketop-and-publicadmin:  Market operators and public
      administrations that must comply to some notifications
      requirements under EU NIS directive
   eu-nis-sector-and-subsectors:  Sectors, subsectors, and digital
      services as identified by the NIS Directive
   euci:  EU classified information (EUCI) means any information or
      material designated by a EU security classification, the
      unauthorised disclosure of which could cause varying degrees of
      prejudice to the interests o f the European Union or of one or
      more of the Member States.
   europol-event:  This taxonomy was designed to describe the type of
      events
   europol-incident:  This taxonomy was designed to describe the type of
      incidents by class.
   event-assessment:  A series of assessment predicates describing the
      event assessment performed to make judgement(s) under a certain
      level of uncertainty.
   event-classification:  Classification of events as seen in tools such
      as RT/IR, MISP and other
   exercise:  Exercise is a taxonomy to describe if the information is
      part of one or more cyber or crisis exercise.
   extended-event:  Reasons why an event has been extended.  This
      taxonomy must be used on the extended event.  The competitive
      analysis aspect is from Psychology of Intelligence Analysis by
      Richard J.  Heuer, Jr. ref:http://www.f (http://www.f)
      oo.be/docs/intelligence/PsychofIntelNew.pdf
   failure-mode-in-machine-learning:  The purpose of this taxonomy is to
      jointly tabulate both the of these failure modes in a single
      place.  Intentional failures wherein the failure is caused by an
      active adversary attempting to subvert the syst em to attain her
      goals – either to misclassify the result, infer private training
      data, or to steal the underlying algorithm.  Unintentional
      failures wherein the failure is because an ML system produces a
      formall y correct but completely unsafe outcome.
   false-positive:  This taxonomy aims to ballpark the expected amount
      of false positives.
   file-type:  List of known file types.
   financial:  Financial taxonomy to describe financial services,
      infrastructure and financial scope.
   flesch-reading-ease:  Flesch Reading Ease is a revised system for
      determining the comprehension difficulty of written material.  The
      scoring of the flesh score can have a maximum of 121.22 and there
      is no limit on how low a score can be (negative score are valid).
   fpf:  The Future of Privacy Forum (FPF) visual guide to practical de-




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      identification (https://fpf.org/2016/04/25/a-visual-guide-to-
      practical-data-de-identification/) taxonomy is used to evaluate
      the degree of iden tifiability of personal data and the types of
      pseudonymous data, de-identified data and anonymous data.  The
      work of FPF is licensed under a creative commons attribution 4.0
      international license.
   fr-classif:  French gov information classification system
   gdpr:  Taxonomy related to the REGULATION (EU) 2016/679 OF THE
      EUROPEAN PARLIAMENT AND OF THE COUNCIL on the protection of
      natural persons with regard to the processing of personal data and
      on the free movement of such data, and repealing Directive 95/46/
      EC (General Data Protection Regulation)
   gea-nz-activities:  Information needed to track or monitor moments,
      periods or events that occur over time.  This type of information
      is focused on occurrences that must be tracked for business
      reasons or represent a specific po int in the evolution of ‘The
      Business’.
   gea-nz-entities:  Information relating to instances of entities or
      things.
   gea-nz-motivators:  Information relating to authority or governance.
   gsma-attack-category:  Taxonomy used by GSMA for their information
      sharing program with telco describing the attack categories
   gsma-fraud:  Taxonomy used by GSMA for their information sharing
      program with telco describing the various aspects of fraud
   gsma-network-technology:  Taxonomy used by GSMA for their information
      sharing program with telco describing the types of infrastructure.
      WiP
   honeypot-basic:  Updated (CIRCL, Seamus Dowling and EURECOM) from
      Christian Seifert, Ian Welch, Peter Komisarczuk, ‘Taxonomy of
      Honeypots’, Technical Report CS-TR-06/12, VICTORIA UNIVERSITY OF
      WELLINGTON, School of Mathemati cal and Computing Sciences, June
      2006, http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/
      CS-TR-06-12.pdf
      (http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-
      TR-06-12.pdf)
   ics:  FIRST.ORG CTI SIG - MISP Proposal for ICS/OT Threat Attribution
      (IOC) Project
   iep:  Forum of Incident Response and Security Teams (FIRST)
      Information Exchange Policy (IEP) framework
   iep2-policy:  Forum of Incident Response and Security Teams (FIRST)
      Information Exchange Policy (IEP) v2.0 Policy
   iep2-reference:  Forum of Incident Response and Security Teams
      (FIRST) Information Exchange Policy (IEP) v2.0 Reference
   ifx-vetting:  The IFX taxonomy is used to categorise information
      (MISP events and attributes) to aid in the intelligence vetting
      process
   incident-disposition:  How an incident is classified in its process




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      to be resolved.  The taxonomy is inspired from NASA Incident
      Response and Management Handbook. https://www.nasa.gov/
      pdf/589502main_ITS-HBK-2810.09-02%20%5bNASA%20I
      (https://www.nasa.gov/pdf/589502main_ITS-HBK-
      2810.09-02%20%5bNASA%20I)
      nformation%20Security%20Incident%20Management%5d.pdf#page=9
   infoleak:  A taxonomy describing information leaks and especially
      information classified as being potentially leaked.  The taxonomy
      is based on the work by CIRCL on the AIL framework.  The taxonomy
      aim is to be used at l arge to improve classification of leaked
      information.
   information-origin:  Taxonomy for tagging information by its origin:
      human-generated or AI-generated.
   information-security-data-source:  Taxonomy to classify the
      information security data sources.
   information-security-indicators:  A full set of operational
      indicators for organizations to use to benchmark their security
      posture.
   interactive-cyber-training-audience:  Describes the target of cyber
      training and education.
   interactive-cyber-training-technical-setup:  The technical setup
      consists of environment structure, deployment, and orchestration.
   interactive-cyber-training-training-environment:  The training
      environment details the environment around the training,
      consisting of training type and scenario.
   interactive-cyber-training-training-setup:  The training setup
      further describes the training itself with the scoring, roles, the
      training mode as well as the customization level.
   interception-method:  The interception method used to intercept
      traffic.
   ioc:  An IOC classification to facilitate automation of malicious and
      non malicious artifacts
   iot:  Internet of Things taxonomy, based on IOT UK report
      https://iotuk.org.uk/wp-content/uploads/2017/01/IOT-Taxonomy-
      Report.pdf (https://iotuk.org.uk/wp-content/uploads/2017/01/IOT-
      Taxonomy-Report.pdf)
   kill-chain:  The Cyber Kill Chain, a phase-based model developed by
      Lockheed Martin, aims to help categorise and identify the stage of
      an attack.
   maec-delivery-vectors:  Vectors used to deliver malware based on MAEC
      5.0
   maec-malware-behavior:  Malware behaviours based on MAEC 5.0
   maec-malware-capabilities:  Malware Capabilities based on MAEC 5.0
   maec-malware-obfuscation-methods:  Obfuscation methods used by
      malware based on MAEC 5.0
   malware_classification:  Classification based on different





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      categories.  Based on https://www.sans.org/reading-
      room/whitepapers/incident/malware-101-viruses-32848
      (https://www.sans.org/reading-room/whitepapers/incident/malware-
      101-viruses-32848)
   misinformation-website-label:  classification for the identification
      of type of misinformation among websites.  Source:False,
      Misleading, Clickbait-y, and/or Satirical News Sources by Melissa
      Zimdars 2019
   misp:  MISP taxonomy to infer with MISP behavior or operation.
   misp-workflow:  MISP workflow taxonomy to support result of workflow
      execution.
   monarc-threat:  MONARC Threats Taxonomy
   ms-caro-malware:  Malware Type and Platform classification based on
      Microsoft's implementation of the Computer Antivirus Research
      Organization (CARO) Naming Scheme and Malware Terminology.  Based
      on https://www.microsoft.com/e (https://www.microsoft.com/e) n-
      us/security/portal/mmpc/shared/malwarenaming.aspx,
      https://www.microsoft.com/security/portal/mmpc/shared/
      glossary.aspx
      (https://www.microsoft.com/security/portal/mmpc/shared/
      glossary.aspx),
      https://www.microsoft.com/security/portal/mmpc/shared/
      objectivecriteria.aspx
      (https://www.microsoft.com/security/portal/mmpc/shared/
      objectivecriteria.aspx), and http:// www.caro.org/definitions/
      index.html.  Malware families are extracted from Microsoft SIRs
      since 2008 based on
      https://www.microsoft.com/security/sir/archive/default.aspx
      (https://www.microsoft.com/security/sir/archive/default.aspx) and
      https://www.microsoft.com/en-us/securit
      (https://www.microsoft.com/en-us/securit) y/portal/threat/
      threats.aspx.  Note that SIRs do NOT include all Microsoft malware
      families.
   ms-caro-malware-full:  Malware Type and Platform classification based
      on Microsoft's implementation of the Computer Antivirus Research
      Organization (CARO) Naming Scheme and Malware Terminology.  Based
      on https://www.microsoft.com/e (https://www.microsoft.com/e) n-
      us/security/portal/mmpc/shared/malwarenaming.aspx,
      https://www.microsoft.com/security/portal/mmpc/shared/
      glossary.aspx
      (https://www.microsoft.com/security/portal/mmpc/shared/
      glossary.aspx),
      https://www.microsoft.com/security/portal/mmpc/shared/
      objectivecriteria.aspx
      (https://www.microsoft.com/security/portal/mmpc/shared/
      objectivecriteria.aspx), and http:// www.caro.org/definitions/
      index.html.  Malware families are extracted from Microsoft SIRs
      since 2008 based on



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      https://www.microsoft.com/security/sir/archive/default.aspx
      (https://www.microsoft.com/security/sir/archive/default.aspx) and
      https://www.microsoft.com/en-us/securit
      (https://www.microsoft.com/en-us/securit) y/portal/threat/
      threats.aspx.  Note that SIRs do NOT include all Microsoft malware
      families.
   mwdb:  Malware Database (mwdb) Taxonomy - Tags used across the
      platform
   nato:  NATO classification markings.
   nis:  The taxonomy is meant for large scale cybersecurity incidents,
      as mentioned in the Commission Recommendation of 13 September
      2017, also known as the blueprint.  It has two core parts: The
      nature of the incide nt, i.e. the underlying cause, that triggered
      the incident, and the impact of the incident, i.e. the impact on
      services, in which sector(s) of economy and society.
   nis2:  The taxonomy is meant for large scale cybersecurity incidents,
      as mentioned in the Commission Recommendation of 13 May 2022, also
      known as the provisional agreement.  It has two core parts: The
      nature of the incident, i.e. the underlying cause, that triggered
      the incident, and the impact of the incident, i.e. the impact on
      services, in which sector(s) of economy and society.
   open_threat:  Open Threat Taxonomy v1.1 base on James Tarala of SANS
      http://www.auditscripts.com/resources/
      open_threat_taxonomy_v1.1a.pdf
      (http://www.auditscripts.com/resources/
      open_threat_taxonomy_v1.1a.pdf), https://files.sans.org/summit/
      Threat_Hunting_Incident_Response_Summit_2016/PDFs/Us
      (https://files.sans.org/summit/
      Threat_Hunting_Incident_Response_Summit_2016/PDFs/Us) ing-Open-
      Tools-to-Convert-Threat-Intelligence-into-Practical-Defenses-
      James-Tarala-SANS-Institute.pdf, https://www.youtube.com/
      watch?v=5rdGOOFC_yE (https://www.youtube.com/watch?v=5rdGOOFC_yE),
      and https://www.rsaconference.com/writable/presentations/file_u
      (https://www.rsaconference.com/writable/presentations/file_u)
      pload/str-r04_using-an-open-source-threat-model-for-prioritized-
      defense-final.pdf
   organizational-cyber-harm:  A taxonomy to classify organizational
      cyber harms based on categories like physical, economic,
      psychological, reputational, and social/societal impacts.
   osint:  Open Source Intelligence - Classification (MISP taxonomies)
   pandemic:  Pandemic
   passivetotal:  Tags from RiskIQ's PassiveTotal service
   pentest:  Penetration test (pentest) classification.
   pfc:  Le Protocole des feux de circulation (PFC) est basé sur le
      standard « Traffic Light Protocol (TLP) » conçu par le FIRST.  Il
      a pour objectif d’informer sur les limites autorisées pour la
      diffusion des informa tions.  Il est classé selon des codes de
      couleurs.



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   phishing:  Taxonomy to classify phishing attacks including
      techniques, collection mechanisms and analysis status.
   poison-taxonomy:  Non-exhaustive taxonomy of natural poison
   political-spectrum:  A political spectrum is a system to characterize
      and classify different political positions in relation to one
      another.
   priority-level:  After an incident is scored, it is assigned a
      priority level.  The six levels listed below are aligned with
      NCCIC, DHS, and the CISS to help provide a common lexicon when
      discussing incidents.  This priority a ssignment drives NCCIC
      urgency, pre-approved incident response offerings, reporting
      requirements, and recommendations for leadership escalation.
      Generally, incident priority distribution should follow a similar
      pattern to the graph below.  Based on https://www.cisa.gov/news-
      events/news/cisa-national-cyber-incident-scoring-system-nciss
      (https://www.cisa.gov/news-events/news/cisa-national-cyber-
      incident-scoring-system-nciss).
   pyoti:  PyOTI automated enrichment schemes for point in time
      classification of indicators.
   ransomware:  Ransomware is used to define ransomware types and the
      elements that compose them.
   ransomware-roles:  The seven roles seen in most ransomware incidents.
   retention:  Add a retention time to events to automatically remove
      the IDS-flag on ip-dst or ip-src attributes.  We calculate the
      time elapsed based on the date of the event.  Supported time units
      are: d(ays), w(eeks), m( onths), y(ears).  The numerical_value is
      just for sorting in the web-interface and is not used for
      calculations.
   rsit:  Reference Security Incident Classification Taxonomy
   rt_event_status:  Status of events used in Request Tracker.
   runtime-packer:  Runtime or software packer used to combine
      compressed or encrypted data with the decompression or decryption
      code.  This code can add additional obfuscations mechanisms
      including polymorphic-packer, virtualiz ation or other obfuscation
      techniques.  This taxonomy lists all the known or official packer
      used for legitimate use or for packing malicious binaries.
   scrippsco2-fgc:  Flags describing the sample
   scrippsco2-fgi:  Flags describing the sample for isotopic data (C14,
      O18)
   scrippsco2-sampling-stations:  Sampling stations of the Scripps CO2
      Program
   sentinel-threattype:  Sentinel indicator threat types.
   smart-airports-threats:  Threat taxonomy in the scope of securing
      smart airports by ENISA. https://www.enisa.europa.eu/publications/
      securing-smart-airports (https://www.enisa.europa.eu/publications/
      securing-smart-airports)
   social-engineering-attack-vectors:  Attack vectors used in social




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      engineering as described in 'A Taxonomy of Social Engineering
      Defense Mechanisms' by Dalal Alharthi and others.
   srbcert:  SRB-CERT Taxonomy - Schemes of Classification in Incident
      Response and Detection
   state-responsibility:  A spectrum of state responsibility to more
      directly tie the goals of attribution to the needs of
      policymakers.
   stealth_malware:  Classification based on malware stealth techniques.
      Described in https://vxheaven.org/lib/pdf/
      Introducing%20Stealth%20Malware%20Taxonomy.pdf
      (https://vxheaven.org/lib/pdf/
      Introducing%20Stealth%20Malware%20Taxonomy.pdf)
   stix-ttp:  TTPs are representations of the behavior or modus operandi
      of cyber adversaries.
   targeted-threat-index:  The Targeted Threat Index is a metric for
      assigning an overall threat ranking score to email messages that
      deliver malware to a victim’s computer.  The TTI metric was first
      introduced at SecTor 2013 by Seth H ardy as part of the talk
      “RATastrophe: Monitoring a Malware Menagerie” along with Katie
      Kleemola and Greg Wiseman.
   thales_group:  Thales Group Taxonomy - was designed with the aim of
      enabling desired sharing and preventing unwanted sharing between
      Thales Group security communities.
   threatmatch:  The ThreatMatch Sectors, Incident types, Malware types
      and Alert types are applicable for any ThreatMatch instances and
      should be used for all CIISI and TIBER Projects.
   threats-to-dns:  An overview of some of the known attacks related to
      DNS as described by Torabi, S., Boukhtouta, A., Assi, C., &
      Debbabi, M. (2018) in Detecting Internet Abuse by Analyzing
      Passive DNS Traffic: A Survey of Im plemented Systems.  IEEE
      Communications Surveys & Tutorials, 1–1. doi:10.1109/
      comst.2018.2849614
   tlp:  The Traffic Light Protocol (TLP) (v2.0) was created to
      facilitate greater sharing of potentially sensitive information
      and more effective collaboration.  Information sharing happens
      from an information source , towards one or more recipients.  TLP
      is a set of four standard labels (a fifth label is included in
      amber to limit the diffusion) used to indicate the sharing
      boundaries to be applied by the recipients.  Only la bels listed
      in this standard are considered valid by FIRST.  This taxonomy
      includes additional labels for backward compatibility which are no
      more validated by FIRST SIG.
   tor:  Taxonomy to describe Tor network infrastructure
   trust:  The Indicator of Trust provides insight about data on what
      can be trusted and known as a good actor.  Similar to a whitelist
      but on steroids, reusing features one would use with Indicators of
      Compromise, but to filter out what is known to be good.
   type:  Taxonomy to describe different types of intelligence gathering



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      discipline which can be described the origin of intelligence.
   unified-kill-chain:  The Unified Kill Chain is a refinement to the
      Kill Chain.
   unified-ransomware-kill-chain:  The Unified Ransomware Kill Chain, a
      intelligence driven model developed by Oleg Skulkin, aims to track
      every single phase of a ransomware attack.
   use-case-applicability:  The Use Case Applicability categories
      reflect standard resolution categories, to clearly display
      alerting rule configuration problems.
   veris:  Vocabulary for Event Recording and Incident Sharing (VERIS)
   vmray:  VMRay taxonomies to map VMRay Thread Identifier scores and
      artifacts.
   vocabulaire-des-probabilites-estimatives:  Ce vocabulaire attribue
      des valeurs en pourcentage à certains énoncés de probabilité
   vulnerability:  A taxonomy for describing vulnerabilities (software,
      hardware, or social) on different scales or with additional
      available information.
   workflow:  Workflow support language is a common language to support
      intelligence analysts to perform their analysis on data and
      information.

5.  JSON Schema

   The JSON Schema [JSON-SCHEMA] below defines the structure of the MISP
   taxonomy document as literally described before.  The JSON Schema is
   used validating a MISP taxonomy.  The validation is a _MUST_ if the
   taxonomy is included in the MISP taxonomies directory.

   {
     "$schema": "http://json-schema.org/schema#",
     "title": "Validator for misp-taxonomies",
     "id": "https://www.github.com/MISP/misp-taxonomies/schema.json",
     "defs": {
       "non-empty-string": {
         "type": "string",
         "minLength": 1
       },
       "entry": {
         "type": "array",
         "minItems": 1,
         "uniqueItems": true,
         "items": {
           "type": "object",
           "additionalProperties": false,
           "properties": {
             "numerical_value": {
               "type": "number"
             },



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             "expanded": {
               "$ref": "#/defs/non-empty-string"
             },
             "description": {
               "$ref": "#/defs/non-empty-string"
             },
             "colour": {
               "$ref": "#/defs/non-empty-string"
             },
             "value": {
               "$ref": "#/defs/non-empty-string"
             },
             "uuid": {
               "$ref": "#/defs/non-empty-string"
             },
             "required": [
               "value"
             ]
           }
         }
       },
       "values": {
         "type": "array",
         "minItems": 1,
         "uniqueItems": true,
         "items": {
           "type": "object",
           "additionalProperties": false,
           "properties": {
             "entry": {
               "$ref": "#/defs/entry"
             },
             "predicate": {
               "$ref": "#/defs/non-empty-string"
             },
             "uuid": {
               "$ref": "#/defs/non-empty-string"
             }
           },
           "required": [
             "predicate"
           ]
         }
       },
       "predicates": {
         "type": "array",
         "minItems": 1,
         "uniqueItems": true,



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         "items": {
           "type": "object",
           "additionalProperties": false,
           "properties": {
             "numerical_value": {
               "type": "number"
             },
             "colour": {
               "$ref": "#/defs/non-empty-string"
             },
             "description": {
               "$ref": "#/defs/non-empty-string"
             },
             "expanded": {
               "$ref": "#/defs/non-empty-string"
             },
             "value": {
               "$ref": "#/defs/non-empty-string"
             },
             "exclusive": {
               "type": "boolean"
             },
             "uuid": {
               "$ref": "#/defs/non-empty-string"
             },
             "required": [
               "value"
             ]
           }
         }
       }
     },
     "type": "object",
     "additionalProperties": false,
     "properties": {
       "version": {
         "type": "integer"
       },
       "description": {
         "$ref": "#/defs/non-empty-string"
       },
       "expanded": {
         "$ref": "#/defs/non-empty-string"
       },
       "namespace": {
         "$ref": "#/defs/non-empty-string"
       },
       "uuid": {



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         "$ref": "#/defs/non-empty-string"
       },
       "exclusive": {
         "type": "boolean"
       },
       "type": {
         "type": "array",
         "minItems": 1,
         "uniqueItems": true,
         "items": {
           "type": "string",
           "enum": [
             "org",
             "user",
             "attribute",
             "event"
           ]
         }
       },
       "refs": {
         "type": "array",
         "minItems": 1,
         "uniqueItems": true,
         "items": {
           "$ref": "#/defs/non-empty-string"
         }
       },
       "predicates": {
         "$ref": "#/defs/predicates"
       },
       "values": {
         "$ref": "#/defs/values"
       }
     },
     "required": [
       "namespace",
       "description",
       "version",
       "predicates"
     ]
   }

6.  Acknowledgements

   The authors wish to thank all the MISP community who are supporting
   the creation of open standards in threat intelligence sharing.

7.  References



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7.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC4122]  Leach, P., Mealling, M., and R. Salz, "A Universally
              Unique IDentifier (UUID) URN Namespace", RFC 4122,
              DOI 10.17487/RFC4122, July 2005,
              <https://www.rfc-editor.org/info/rfc4122>.

   [RFC8259]  Bray, T., Ed., "The JavaScript Object Notation (JSON) Data
              Interchange Format", STD 90, RFC 8259,
              DOI 10.17487/RFC8259, December 2017,
              <https://www.rfc-editor.org/info/rfc8259>.

7.2.  Informative References

   [JSON-SCHEMA]
              Wright, A., "JSON Schema: A Media Type for Describing JSON
              Documents", 2016,
              <https://tools.ietf.org/html/draft-wright-json-schema>.

   [MISP-P]   Community, M., "MISP Project - Open Source Threat
              Intelligence Platform and Open Standards For Threat
              Information Sharing", <https://github.com/MISP>.

   [MISP-T]   Community, M., "MISP Taxonomies - shared and common
              vocabularies of tags",
              <https://github.com/MISP/misp-taxonomies>.

   [machine-tags]
              Cope, A. S., "Machine tags", 2007,
              <https://www.flickr.com/groups/51035612836@N01/
              discuss/72157594497877875/>.

Authors' Addresses

   Alexandre Dulaunoy
   Computer Incident Response Center Luxembourg
   122, rue Adolphe Fischer
   L-L-1521 Luxembourg
   Luxembourg
   Phone: +352 247 88444
   Email: alexandre.dulaunoy@circl.lu





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   Andras Iklody
   Computer Incident Response Center Luxembourg
   122, rue Adolphe Fischer
   L-L-1521 Luxembourg
   Luxembourg
   Phone: +352 247 88444
   Email: andras.iklody@circl.lu












































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