misp-rfc/misp-taxonomy-format/raw.md.txt

Network Working Group                                        A. Dulaunoy
Internet-Draft                                                 A. Iklody
Intended status: Informational                                     CIRCL
Expires: June 2, 2018                                  November 29, 2017


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

Abstract

   This document describes the MISP taxonomy format which describes a
   simple JSON format to represent machine tags (also called triple
   tags) vocabularies.  A public directory of common vocabularies called
   MISP taxonomies is available and relies on the MISP taxonomy format.
   MISP taxonomies are used to classify cyber security events, threats,
   suspicious events, or 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
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   This Internet-Draft will expire on June 2, 2018.

Copyright Notice

   Copyright (c) 2017 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
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   include Simplified BSD License text as described in Section 4.e of




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   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

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 directory  . . . . . .  11
   5.  JSON Schema . . . . . . . . . . . . . . . . . . . . . . . . .  20
   6.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  23
   7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  23
     7.1.  Normative References  . . . . . . . . . . . . . . . . . .  23
     7.2.  Informative References  . . . . . . . . . . . . . . . . .  23
     7.3.  URIs  . . . . . . . . . . . . . . . . . . . . . . . . . .  23
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  24

1.  Introduction

   Sharing threat information became a fundamental requirements on the
   Internet, security and intelligence community at large.  Threat
   information can include indicators of compromise, malicious file
   indicators, financial fraud indicators or even detailed information
   about a threat actor.  While sharing such indicators or information,
   classification plays an important role to ensure adequate
   distribution, understanding, validation or action of the shared
   information.  MISP taxonomies is a public repository of known
   vocabularies that can be used 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 directory

   The public directory of MISP taxonomies [MISP-T] contains a variety
   of taxonomy in various fields such as:

   CERT-XLM:
      CERT-XLM Security Incident Classification.

   DML:



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      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.

   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).

   action-taken:
      Action taken in the case of a security incident (CSIRT
      perspective).

   admiralty-scale:
      The Admiralty Scale (also called the NATO System) is used to rank
      the reliability of a source and the credibility of an information.

   adversary:
      An overview and description of the adversary infrastructure.

   ais-marking:
      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).

   binary-class:
      Custom taxonomy for types of binary file.

   cccs:
      Internal taxonomy for CCCS.



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   circl:
      CIRCL Taxonomy is a simple scheme for incident classification and
      area topic where the incident took place.

   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.

   common-taxonomy:
      The Common Taxonomy for Law Enforcement and The National Network
      of CSIRTs bridges the gap between the CSIRTs and international Law
      Enforcement communities by adding a legislative framework to
      facilitate the harmonisation of incident reporting to competent
      authorities, the development of useful statistics and sharing
      information within the entire cybercrime ecosystem.

   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.

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

   csirt_case_classification:
      FIRST CSIRT Case Classification.

   cssa:
      The CSSA agreed sharing taxonomy.

   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>

   data-classification:
      Data classification for data potentially at risk of exfiltration
      based on table 2.1 of Solving Cyber Risk book.

   dcso-sharing:
      DCSO Sharing Taxonomy to classify certain types of MISP events
      using the DCSO Event Guide

   ddos:




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      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:
      Taxonomy for the handling of protectively marked information in
      MISP with German (DE) Government classification markings (VS)

   dhs-ciip-sectors:
      DHS critical sectors as described in <https://www.dhs.gov/
      critical-infrastructure-sectors>.

   diamond-model:
      The Diamond Model for Intrusion Analysis, a phase-based model
      developed by Lockheed Martin, aims to help categorise and identify
      the stage of an attack.

   dni-ism:
      ISM (Information Security Marking Metadata) V13 as described by
      DNI.gov (Director of National Intelligence - US).

   domain-abuse:
      Taxonomy to tag domain names used for cybercrime.

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

   ecsirt:
      eCSIRT incident classification Appendix C of the eCSIRT EU project
      including IntelMQ updates.

   enisa:
      ENISA Threat Taxonomy - A tool for structuring threat information
      as published in <https://www.enisa.europa.eu/topics/threat-risk-
      management/threats-and-trends/enisa-threat-landscape/etl2015/
      enisa-threat-taxonomy-a-tool-for-structuring-threat-information>

   estimative-language:
      Estimative language - including likelihood or probability of event
      based on the Intelligence Community Directive 203 (ICD 203)
      (6.2.(a)) and JP 2-0, Joint Intelligence.

   eu-marketop-and-publicadmin:



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      Market operators and public administrations that must comply to
      some notifications requirements under EU NIS directive.

   eu-nis-sector-and-subsectors:
      Sectors and sub sectors 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 as described in COUNCIL DECISION of 23 September
      2013 on the security rules for protecting EU classified
      information

   europol-event:
      EUROPOL type of events taxonomy.

   europol-incident:
      EUROPOL class of incident taxonomy.

   event-assessment:
      A series of assessment predicates describing the event assessment
      performed to make judgement(s) under a certain level of
      uncertainty.

   event-classification:
      Event Classification.

   exercise:
      Exercise is a taxonomy to describe if the information is part of
      one or more cyber or crisis exercise.

   false-positive:
      This taxonomy aims to ballpark the expected amount of false
      positives.

   file-type:
      List of known file types.

   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:




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      The Future of Privacy Forum (FPF) visual guide to practical de-
      identification [1] 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)

   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:
      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>

   iep:
      Forum of Incident Response and Security Teams (FIRST) Information
      Exchange Policy (IEP) framework.

   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.



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   infoleak:
      A taxonomy describing information leaks and especially information
      classified as being potentially leaked.

   information-security-data-source:
      Taxonomy to classify the information security data sources

   information-security-indicators:
      Information security indicators have been standardized by the ETSI
      Industrial Specification Group (ISG) ISI.  These indicators
      provide the basis to switch from a qualitative to a quantitative
      culture in IT Security Scope of measurements: External and
      internal threats (attempt and success), user's deviant behaviours,
      nonconformities and/or vulnerabilities (software, configuration,
      behavioural, general security framework).  ETSI GS ISI 001-1
      (V1.1.2): ISI Indicators

   interception-method:
      The interception method used to intercept traffic.

   kill-chain:
      Cyber Kill Chain from Lockheed Martin as described in
      Intelligence-Driven Computer Network Defense Informed by Analysis
      of Adversary Campaigns and Intrusion Kill Chains.

   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:
      Malware classification based on a SANS whitepaper about malware.

   misp:
      Internal MISP taxonomy.

   monarc-threat:
      MONARC threat taxonomy.

   ms-caro-malware:




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      Malware Type and Platform classification based on Microsoft's
      implementation of the Computer Antivirus Research Organization
      (CARO) Naming Scheme and Malware Terminology.

   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.

   nato:
      Marking of Classified and Unclassified materials as described by
      the North Atlantic Treaty Organization, NATO.

   nis:
      NIS Cybersecurity Incident Taxonomy.

   open_threat:
      Open Threat Taxonomy v1.1 base on James Tarala of SANS ref. -
      <http://www.auditscripts.com/resources/
      open_threat_taxonomy_v1.1a.pdf>

   osint:
      Open Source Intelligence - Classification (MISP taxonomies).

   passivetotal:
      Tags for RiskIQ's passivetotal service

   pentest:
      Penetration test (pentest) classification.

   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.us-cert.gov/NCCIC-Cyber-Incident-Scoring-
      System>.

   rsit:
      Reference Security Incident Classification Taxonomy.

   rt_event_status:
      Status of events used in Request Tracker.

   runtime-packer:



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      Runtime or software packer used to combine compressed data with
      the decompression code.  The decompression 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.

   smart-airports-threats:
      Threat taxonomy in the scope of securing smart airports by ENISA.

   stealth_malware:
      Classification based on malware stealth techniques.

   stix-ttp:
      Representation of the behavior or modus operandi of cyber
      adversaries (a.k.a TTP) as normalized in STIX

   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.

   tlp:
      The Traffic Light Protocol - or short: TLP - was designed with the
      objective to create a favorable classification scheme for sharing
      sensitive information while keeping the control over its
      distribution at the same time.  Extended with TLP:EX:CHR.

   tor:
      Taxonomy to describe Tor network infrastructure

   type:
      Taxonomy to describe different types of intelligence gathering
      discipline which can be described the origin of intelligence.

   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).

   vocabulaire-des-probabilites-estimatives:
      Vocabulaire des probabilites estimatives




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   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"
              },
              "required": [
                "value"
              ]
            }
          }
        },
        "values": {
          "type": "array",
          "uniqueItems": true,



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          "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": {
                "type": "boolean"
              },
              "required": [
                "value"
              ]
            }
          }
        }
      },



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      "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": {
          "$ref": "#/defs/predicates"
        },
        "values": {
          "$ref": "#/defs/values"
        }
      },
      "required": [
        "namespace",
        "description",



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

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

   [MISP-P]   MISP, "MISP Project - Malware Information Sharing Platform
              and Threat Sharing", <https://github.com/MISP>.

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

7.3.  URIs

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





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Authors' Addresses

   Alexandre Dulaunoy
   Computer Incident Response Center Luxembourg
   16, bd d'Avranches
   Luxembourg  L-1611
   Luxembourg

   Phone: +352 247 88444
   Email: alexandre.dulaunoy@circl.lu


   Andras Iklody
   Computer Incident Response Center Luxembourg
   16, bd d'Avranches
   Luxembourg  L-1611
   Luxembourg

   Phone: +352 247 88444
   Email: andras.iklody@circl.lu































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