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Network Working Group A. Dulaunoy
Internet-Draft A. Iklody
Intended status: Informational CIRCL
Expires: 25 May 2022 21 November 2021
MISP taxonomy format
draft-00
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
Task Force (IETF). Note that other groups may also distribute
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
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 25 May 2022.
Copyright Notice
Copyright (c) 2021 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. 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 . . . . . . . . . . . . . . . . . . . . . . . . . 18
6. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 21
7. Normative References . . . . . . . . . . . . . . . . . . . . 21
8. Informative References . . . . . . . . . . . . . . . . . . . 22
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 22
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.
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].
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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.
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.
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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: The Detection Maturity Level (DML) model is a capability
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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.
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
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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
(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: 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
(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).
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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
(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 (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: 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.
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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: 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)
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,
(U+2018)Taxonomy of Honeypots (U+2019), 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)
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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.
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.
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monarc-threat: MONARC threat 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.
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
(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 (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: 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.
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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 (U+2019)s computer. The TTI metric
was first introduced at SecTor 2013 by Seth Hardy as part of the
talk “ (U+201C)RATastrophe: Monitoring a Malware Menagerie”
(U+201D) 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
probabilité (U+00E9)s estimatives
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.
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{
"$schema": "http://json-schema.org/schema#",
"title": "Validator for misp-taxonomies",
"id": "https://www.github.com/MISP/misp-taxonomies/schema.json",
"defs": {
"entry": {
"type": "array",
"uniqueItems": true,
"items": {
"type": "object",
"additionalProperties": false,
"properties": {
"numerical_value": {
"type": "number"
},
"expanded": {
"type": "string"
},
"description": {
"type": "string"
},
"colour": {
"type": "string"
},
"value": {
"type": "string"
},
"required": [
"value"
]
}
}
},
"values": {
"type": "array",
"uniqueItems": true,
"items": {
"type": "object",
"additionalProperties": false,
"properties": {
"entry": {
"$ref": "#/defs/entry"
},
"predicate": {
"type": "string"
}
},
"required": [
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"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"
]
}
}
}
},
"type": "object",
"additionalProperties": false,
"properties": {
"version": {
"type": "integer"
},
"description": {
"type": "string"
},
"expanded": {
"type": "string"
},
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"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",
"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. Normative References
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[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>.
8. 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 - Malware Information Sharing
Platform and Threat 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
16, bd d'Avranches
L-L-1611 Luxembourg
Luxembourg
Phone: +352 247 88444
Email: alexandre.dulaunoy@circl.lu
Andras Iklody
Computer Incident Response Center Luxembourg
16, bd d'Avranches
L-L-1611 Luxembourg
Luxembourg
Phone: +352 247 88444
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Email: andras.iklody@circl.lu
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