Internet DRAFT - draft-dulaunoy-misp-taxonomy-format
draft-dulaunoy-misp-taxonomy-format
Network Working Group A. Dulaunoy
Internet-Draft A. Iklody
Intended status: Informational CIRCL
Expires: 24 August 2024 21 February 2024
MISP taxonomy format
draft-dulaunoy-misp-taxonomy-format-09
Abstract
This document describes the MISP taxonomy format, a simple JSON
format used to represent machine tags (also known as triple tags)
vocabularies. A public directory, known as MISP taxonomies, is
available and utilizes the MISP taxonomy format. These taxonomies
are employed to classify cybersecurity events, threats, suspicious
events, or indicators.
Status of This Memo
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This Internet-Draft will expire on 24 August 2024.
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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 . . . . . . . . . . . . . . . . . . . . . . . . . 22
6. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 25
7. Normative References . . . . . . . . . . . . . . . . . . . . 25
8. Informative References . . . . . . . . . . . . . . . . . . . 25
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 26
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.
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.
DFRLab-dichotomies-of-disinformation: DFRLab Dichotomies of
Disinformation.
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.
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).
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).
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.
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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.
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.
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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.
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.
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
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).
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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
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.
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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)
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)
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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-2810.09-02%20%5bNASA%20Information%20Securi
ty%20Incident%20Management%5d.pdf#page=9 (https://www.nasa.gov/
pdf/589502main_ITS-HBK-2810.09-02%20%5bNASA%20Information%20Securi
ty%20Incident%20Management%5d.pdf#page=9)
infoleak: A taxonomy describing information leaks and especially
information classified as being potentially leaked. The taxonomy
is based on the work by CIRCL on the AIL framework. The taxonomy
aim is to be used at 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.
maec-delivery-vectors: Vectors used to deliver malware based on MAEC
5.0
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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
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/
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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.
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)
osint: Open Source Intelligence - Classification (MISP taxonomies)
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pandemic: Pandemic
passivetotal: Tags from RiskIQ's PassiveTotal service
pentest: Penetration test (pentest) classification.
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.us-cert.gov/
NCCIC-Cyber-Incident-Scoring-System (https://www.us-cert.gov/
NCCIC-Cyber-Incident-Scoring-System).
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.
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
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engineering as described in 'A Taxonomy of Social Engineering
Defense Mechanisms' by Dalal Alharthi and others.
srbcert: SRB-CERT Taxonomy - Schemes of Classification in Incident
Response and Detection
state-responsibility: A spectrum of state responsibility to more
directly tie the goals of attribution to the needs of
policymakers.
stealth_malware: Classification based on malware stealth techniques.
Described in https://vxheaven.org/lib/pdf/
Introducing%20Stealth%20Malware%20Taxonomy.pdf
(https://vxheaven.org/lib/pdf/
Introducing%20Stealth%20Malware%20Taxonomy.pdf)
stix-ttp: TTPs are representations of the behavior or modus operandi
of cyber adversaries.
targeted-threat-index: The Targeted Threat Index is a metric for
assigning an overall threat ranking score to email messages that
deliver malware to a victim’s computer. The TTI metric was first
introduced at SecTor 2013 by Seth Hardy as part of the talk
“RATastrophe: Monitoring a Malware Menagerie” along with Katie
Kleemola and Greg Wiseman.
thales_group: Thales Group Taxonomy - was designed with the aim of
enabling desired sharing and preventing unwanted sharing between
Thales Group security communities.
threatmatch: The ThreatMatch Sectors, Incident types, Malware types
and Alert types are applicable for any ThreatMatch instances and
should be used for all CIISI and TIBER Projects.
threats-to-dns: An overview of some of the known attacks related to
DNS as described by Torabi, S., Boukhtouta, A., Assi, C., &
Debbabi, M. (2018) in Detecting Internet Abuse by Analyzing
Passive DNS Traffic: A Survey of 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
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discipline which can be described the origin of intelligence.
unified-kill-chain: The Unified Kill Chain is a refinement to the
Kill Chain.
use-case-applicability: The Use Case Applicability categories
reflect standard resolution categories, to clearly display
alerting rule configuration problems.
veris: Vocabulary for Event Recording and Incident Sharing (VERIS)
vmray: VMRay taxonomies to map VMRay Thread Identifier scores and
artifacts.
vocabulaire-des-probabilites-estimatives: Ce vocabulaire attribue
des valeurs en pourcentage à certains énoncés de probabilité
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"
},
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"exclusive": {
"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"
}
},
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"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
[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 - 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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[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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