Network Working Group                                        A. Dulaunoy
Internet-Draft                                                 A. Iklody
Intended status: Informational                                     CIRCL
Expires: 3 July 2025                                    30 December 2024


                          MISP taxonomy format
                 draft-dulaunoy-misp-taxonomy-format-10

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

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   provisions of BCP 78 and BCP 79.

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   This Internet-Draft will expire on 3 July 2025.

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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.  optional fields . . . . . . . . . . . . . . . . . . . . .   4
       2.4.1.  colour  . . . . . . . . . . . . . . . . . . . . . . .   4
       2.4.2.  description . . . . . . . . . . . . . . . . . . . . .   5
       2.4.3.  numerical_value . . . . . . . . . . . . . . . . . . .   5
   3.  Directory . . . . . . . . . . . . . . . . . . . . . . . . . .   6
     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 . . . . . . . .   9
     4.3.  Available taxonomies in the public repository . . . . . .  11
   5.  JSON Schema . . . . . . . . . . . . . . . . . . . . . . . . .  26
   6.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  29
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  29
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .  29
     7.2.  Informative References  . . . . . . . . . . . . . . . . .  29
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  30

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.  optional fields

2.4.1.  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.

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











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   "predicates": [
       {
         "colour": "#CC0033",
         "expanded": "(TLP:RED) Information exclusively and directly
                      given to (a group of) individual recipients.
                      Sharing outside is not legitimate.",
         "value": "red"
       },
       {
         "colour": "#FFC000",
         "expanded": "(TLP:AMBER) Information exclusively given
                      to an organization; sharing limited within
                      the organization to be effectively acted upon.",
         "value": "amber"
       }...]

2.4.2.  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.

2.4.3.  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
           },
           {
             "expanded": "Usually confident",
             "value": "usually-confident",
             "numerical_value": 75
           },
           {
             "expanded": "Fairly confident",
             "value": "fairly-confident",
             "numerical_value": 50
           },
           {
             "expanded": "Rarely confident",
             "value": "rarely-confident",
             "numerical_value": 25
           },
           {
             "expanded": "Unconfident",
             "value": "unconfident",
             "numerical_value": 0
           },
           {
             "expanded": "Confidence cannot be evaluated",
             "value": "confidence-cannot-be-evalued"
           }
        ]
        }

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.




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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 (also called the NATO System)
                     is used to rank the reliability of a source and
                     the credibility of an information.",
     "version": 1,
     "predicates": [
       {
         "value": "source-reliability",
         "expanded": "Source Reliability"
       },
       {
         "value": "information-credibility",
         "expanded": "Information Credibility"
       }
     ],
     "values": [
       {
         "predicate": "source-reliability",
         "entry": [
           {



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             "value": "a",
             "expanded": "Completely reliable"
           },
           {
             "value": "b",
             "expanded": "Usually reliable"
           },
           {
             "value": "c",
             "expanded": "Fairly reliable"
           },
           {
             "value": "d",
             "expanded": "Not usually reliable"
           },
           {
             "value": "e",
             "expanded": "Unreliable"
           },
           {
             "value": "f",
             "expanded": "Reliability cannot be judged"
           }
         ]
       },
       {
         "predicate": "information-credibility",
         "entry": [
           {
             "value": "1",
             "expanded": "Confirmed by other sources"
           },
           {
             "value": "2",
             "expanded": "Probably true"
           },
           {
             "value": "3",
             "expanded": "Possibly true"
           },
           {
             "value": "4",
             "expanded": "Doubtful"
           },
           {
             "value": "5",
             "expanded": "Improbable"
           },



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           {
             "value": "6",
             "expanded": "Truth cannot be judged"
           }
         ]
       }
     ]
   }

4.2.  Open Source Intelligence - Classification

 {
   "values": [
     {
       "entry": [
         {
           "expanded": "Blog post",
           "value": "blog-post"
         },
         {
           "expanded": "Technical or analysis report",
           "value": "technical-report"
         },
         {
           "expanded": "News report",
           "value": "news-report"
         },
         {
           "expanded": "Pastie-like website",
           "value": "pastie-website"
         },
         {
           "expanded": "Electronic forum",
           "value": "electronic-forum"
         },
         {
           "expanded": "Mailing-list",
           "value": "mailing-list"
         },
         {
           "expanded": "Block or Filter List",
           "value": "block-or-filter-list"
         },
         {
           "expanded": "Expansion",
           "value": "expansion"
         }
       ],



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       "predicate": "source-type"
     },
     {
       "predicate": "lifetime",
       "entry": [
         {
           "value": "perpetual",
           "expanded": "Perpetual",
           "description": "Information available publicly on long-term"
         },
         {
           "value": "ephemeral",
           "expanded": "Ephemeral",
           "description": "Information available publicly on short-term"
         }
       ]
     },
     {
       "predicate": "certainty",
       "entry": [
         {
           "numerical_value": 100,
           "value": "100",
           "expanded": "100% Certainty",
           "description": "100% Certainty"
         },
         {
           "numerical_value": 93,
           "value": "93",
           "expanded": "93% Almost certain",
           "description": "93% Almost certain"
         },
         {
           "numerical_value": 75,
           "value": "75",
           "expanded": "75% Probable",
           "description": "75% Probable"
         },
         {
           "numerical_value": 50,
           "value": "50",
           "expanded": "50% Chances about even",
           "description": "50% Chances about even"
         },
         {
           "numerical_value": 30,
           "value": "30",
           "expanded": "30% Probably not",



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           "description": "30% Probably not"
         },
         {
           "numerical_value": 7,
           "value": "7",
           "expanded": "7% Almost certainly not",
           "description": "7% Almost certainly not"
         },
         {
           "numerical_value": 0,
           "value": "0",
           "expanded": "0% Impossibility",
           "description": "0% Impossibility"
         }
       ]
     }
   ],
   "namespace": "osint",
   "description": "Open Source Intelligence - Classification",
   "version": 3,
   "predicates": [
     {
       "value": "source-type",
       "expanded": "Source Type"
     },
     {
       "value": "lifetime",
       "expanded": "Lifetime of the information
                    as Open Source Intelligence"
     },
     {
       "value": "certainty",
       "expanded": "Certainty of the elements mentioned
                    in this Open Source Intelligence"
     }
   ]
 }

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.




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   *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 response 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 or 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.

   *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 COLLECTOR 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).





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   *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 objective 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 Paedophile 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
   taxonomy.

   *cryptocurrency-threat* : Threats targetting cryptocurrency, based on
   CipherTrace report.

   *csirt-americas* : Taxonomía CSIRT Américas.






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   *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.  Consistent 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 adversaries.
   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 usefulness for receiving parties.



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   *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

   *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, technical, 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 Controlled 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




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   *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* : Economical impact is a taxonomy to describe the
   financial impact as positive or negative gain to the tagged
   information (e.g. data exfiltration loss, a positive gain 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
   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 of 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



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   *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.foo.be/docs/intelligence/
   PsychofIntelNew.pdf (http://www.foo.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 system 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 formally 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-identification (https://fpf.org/2016/04/25/a-visual-guide-to-
   practical-data-de-identification/) taxonomy is used to evaluate the
   degree of identifiability 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)




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   *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 point 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 Mathematical 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
   to be resolved.  The taxonomy is inspired from NASA Incident Response
   and Management Handbook. https://www.nasa.gov/pdf/589502main_ITS-HBK-



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   2810.09-02%20%5bNASA%20Information%20Security%20Incident%20Management
   %5d.pdf#page=9 (https://www.nasa.gov/pdf/589502main_ITS-HBK-2810.09-0
   2%20%5bNASA%20Information%20Security%20Incident%20Management%5d.pdf#p
   age=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 large 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.



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   *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
   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
























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   *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/en-us/security/portal/mmpc/shared/
   malwarenaming.aspx (https://www.microsoft.com/en-
   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 (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/security/portal/threat/threats.aspx
   (https://www.microsoft.com/en-us/security/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/en-us/security/portal/mmpc/shared/
   malwarenaming.aspx (https://www.microsoft.com/en-
   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 (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/security/portal/threat/threats.aspx
   (https://www.microsoft.com/en-us/security/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.



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   *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 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.

   *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/Using-Open-Tools-
   to-Convert-Threat-Intelligence-into-Practical-Defenses-James-Tarala-
   SANS-Institute.pdf (https://files.sans.org/summit/
   Threat_Hunting_Incident_Response_Summit_2016/PDFs/Using-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_upload/str-
   r04_using-an-open-source-threat-model-for-prioritized-defense-
   final.pdf
   (https://www.rsaconference.com/writable/presentations/file_upload/
   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.







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   *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 informations.  Il est classé selon des codes de couleurs.

   *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 assignment 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 retenion 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.








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   *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 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
   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 Hardy as part of the talk
   “RATastrophe: Monitoring a Malware Menagerie” along with Katie
   Kleemola and Greg Wiseman.




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   *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 Implemented 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 labels 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 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)




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   *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": {
       "entry": {
         "type": "array",
         "uniqueItems": true,
         "items": {
           "type": "object",
           "additionalProperties": false,
           "properties": {
             "numerical_value": {
               "type": "number"
             },
             "expanded": {
               "type": "string"
             },
             "description": {
               "type": "string"
             },
             "colour": {
               "type": "string"
             },
             "value": {
               "type": "string"
             },



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             "required": [
               "value"
             ]
           }
         }
       },
       "values": {
         "type": "array",
         "uniqueItems": true,
         "items": {
           "type": "object",
           "additionalProperties": false,
           "properties": {
             "entry": {
               "$ref": "#/defs/entry"
             },
             "predicate": {
               "type": "string"
             }
           },
           "required": [
             "predicate"
           ]
         }
       },
       "predicates": {
         "type": "array",
         "uniqueItems": true,
         "items": {
           "type": "object",
           "additionalProperties": false,
           "properties": {
             "numerical_value": {
               "type": "number"
             },
             "colour": {
               "type": "string"
             },
             "description": {
               "type": "string"
             },
             "expanded": {
               "type": "string"
             },
             "value": {
               "type": "string"
             },
             "exclusive": {



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               "type": "boolean"
             },
             "required": [
               "value"
             ]
           }
         }
       }
     },
     "type": "object",
     "additionalProperties": false,
     "properties": {
       "version": {
         "type": "integer"
       },
       "description": {
         "type": "string"
       },
       "expanded": {
         "type": "string"
       },
       "namespace": {
         "type": "string"
       },
       "exclusive": {
         "type": "boolean"
       },
       "type": {
         "type": "array",
         "uniqueItems": true,
         "items": {
           "type": "string",
           "enum": [
             "org",
             "user",
             "attribute",
             "event"
           ]
         }
       },
       "refs": {
         "type": "array",
         "uniqueItems": true,
         "items": {
           "type": "string"
         }
       },
       "predicates": {



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         "$ref": "#/defs/predicates"
       },
       "values": {
         "$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

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>.

   [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>.




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Internet-Draft            MISP taxonomy format             December 2024


   [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


   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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