MISP taxonomy format
draft-dulaunoy-misp-taxonomy-format-06
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draft-dulaunoy-misp-taxonomy-format-06
Network Working Group A. Dulaunoy
Internet-Draft A. Iklody
Intended status: Informational CIRCL
Expires: June 3, 2019 November 30, 2018
MISP taxonomy format
draft-dulaunoy-misp-taxonomy-format-06
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
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provisions of BCP 78 and BCP 79.
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Copyright Notice
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document authors. All rights reserved.
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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 . . . . . . . . . . . . . . . . . . . . . . . . . 19
6. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 22
7. References . . . . . . . . . . . . . . . . . . . . . . . . . 22
7.1. Normative References . . . . . . . . . . . . . . . . . . 22
7.2. Informative References . . . . . . . . . . . . . . . . . 22
7.3. URIs . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 23
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 [RFC4627] 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 decimal and 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.
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.
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>
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>.
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.
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:
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 CELEX 32013D0488
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:
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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.
fpf:
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
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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.
infoleak:
A taxonomy describing information leaks and especially information
classified as being potentially leaked.
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:
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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:
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
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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:
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
veris:
Vocabulary for Event Recording and Incident Sharing (VERIS).
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vocabulaire-des-probabilites-estimatives:
Vocabulaire des probabilites 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.
{
"$schema": "http://json-schema.org/schema#",
"title": "Validator for misp-taxonomies",
"id": "https://www.github.com/MISP/misp-taxonomies/schema.json",
"defs": {
"entry": {
"type": "array",
"uniqueItems": true,
"items": {
"type": "object",
"additionalProperties": false,
"properties": {
"numerical_value": {
"type": "number"
},
"expanded": {
"type": "string"
},
"description": {
"type": "string"
},
"colour": {
"type": "string"
},
"value": {
"type": "string"
},
"required": [
"value"
]
}
}
},
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"values": {
"type": "array",
"uniqueItems": true,
"items": {
"type": "object",
"additionalProperties": false,
"properties": {
"entry": {
"$ref": "#/defs/entry"
},
"predicate": {
"type": "string"
}
},
"required": [
"predicate"
]
}
},
"predicates": {
"type": "array",
"uniqueItems": true,
"items": {
"type": "object",
"additionalProperties": false,
"properties": {
"numerical_value": {
"type": "number"
},
"colour": {
"type": "string"
},
"description": {
"type": "string"
},
"expanded": {
"type": "string"
},
"value": {
"type": "string"
},
"exclusive": {
"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"
}
},
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"required": [
"namespace",
"description",
"version",
"predicates"
]
}
6. Acknowledgements
The authors wish to thank all the MISP community who are supporting
the creation of open standards in threat intelligence sharing.
7. References
7.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC4627] Crockford, D., "The application/json Media Type for
JavaScript Object Notation (JSON)", RFC 4627,
DOI 10.17487/RFC4627, July 2006,
<https://www.rfc-editor.org/info/rfc4627>.
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>.
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7.3. URIs
[1] https://fpf.org/2016/04/25/a-visual-guide-to-practical-data-de-
identification/
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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