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misp-taxonomies
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new: Added Manifest and Markdown generators

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  1. 642
      MANIFEST.json
  2. 1156
      Summary.md
  3. 2
      ifx-vetting/machinetag.json
  4. 48
      tools/gen_manifest.py
  5. 49
      tools/gen_markdown.py

642
MANIFEST.json
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@ -1,539 +1,539 @@
{
"description": "Manifest file of MISP taxonomies available.",
"license": "CC-0",
"path": "machinetag.json",
"taxonomies": [
{
"version": 3,
"name": "accessnow",
"description": "Access Now classification to classify an issue (such as security, human rights, youth rights)."
"description": "The Targeted Threat Index is a metric for assigning an overall threat ranking score to email messages that deliver malware to a victim\u2019s computer. The TTI metric was first introduced at SecTor 2013 by Seth Hardy as part of the talk \u201cRATastrophe: Monitoring a Malware Menagerie\u201d along with Katie Kleemola and Greg Wiseman.",
"name": "targeted-threat-index",
"version": 3
},
{
"version": 1,
"name": "access-method",
"description": "The access method used to remotely access a system."
"description": "Classification based on different categories. Based on https://www.sans.org/reading-room/whitepapers/incident/malware-101-viruses-32848",
"name": "malware_classification",
"version": 2
},
{
"version": 2,
"name": "action-taken",
"description": "Action taken in the case of a security incident (CSIRT perspective)."
"description": "Custom taxonomy for types of binary file.",
"name": "binary-class",
"version": 2
},
{
"version": 2,
"name": "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."
"description": "TTPs are representations of the behavior or modus operandi of cyber adversaries.",
"name": "stix-ttp",
"version": 1
},
{
"version": 4,
"name": "adversary",
"description": "An overview and description of the adversary infrastructure."
"description": "This taxonomy was designed to describe the type of incidents by class.",
"name": "europol-incident",
"version": 1
},
{
"version": 1,
"name": "ais-marking",
"description": "AIS Marking Schema implementation is maintained by the National Cybersecurity and Communication Integration Center (NCCIC) of the U.S. Department of Homeland Security (DHS)"
"description": "Taxonomy to classify the information security data sources.",
"name": "information-security-data-source",
"version": 1
},
{
"version": 2,
"name": "analyst-assessment",
"description": "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."
"description": "The CSSA agreed sharing taxonomy.",
"name": "cssa",
"version": 7
},
{
"version": 1,
"name": "approved-category-of-action",
"description": "A pre-approved category of action for indicators being shared with partners (MIMIC)."
"description": "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",
"name": "dni-ism",
"version": 3
},
{
"version": 1,
"name": "binary-class",
"description": "Custom taxonomy for types of binary file."
"description": "A Course Of Action analysis considers six potential courses of action for the development of a cyber security capability.",
"name": "course-of-action",
"version": 1
},
{
"version": 2,
"name": "cccs",
"description": "Internal taxonomy for CCCS."
"description": "Taxonomy to describe different types of intelligence gathering discipline which can be described the origin of intelligence.",
"name": "type",
"version": 1
},
{
"version": 1,
"name": "CERT-XLM",
"description": "CERT-XLM Security Incident Classification."
"description": "The Use Case Applicability categories reflect standard resolution categories, to clearly display alerting rule configuration problems.",
"name": "use-case-applicability",
"version": 1
},
{
"version": 2,
"name": "circl",
"description": "CIRCL Taxonomy is a simple scheme for incident classification and area topic where the incident took place."
"description": "Obfuscation methods used by malware based on MAEC 5.0",
"name": "maec-malware-obfuscation-methods",
"version": 1
},
{
"version": 1,
"name": "coa",
"description": "Course of action taken within organization to discover, detect, deny, disrupt, degrade, deceive and/or destroy an attack."
"description": "MISP taxonomy to infer with MISP behavior or operation.",
"name": "misp",
"version": 10
},
{
"version": 3,
"name": "collaborative-intelligence",
"description": "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."
"description": "Vocabulary for Event Recording and Incident Sharing (VERIS)",
"name": "veris",
"version": 2
},
{
"version": 1,
"name": "csirt_case_classification",
"description": "FIRST CSIRT Case Classification."
"description": "Threats targetting cryptocurrency, based on CipherTrace report.",
"name": "cryptocurrency-threat",
"version": 1
},
{
"version": 4,
"name": "cssa",
"description": "The CSSA agreed sharing taxonomy."
"description": "Taxonomy to classify phishing attacks including techniques, collection mechanisms and analysis status.",
"name": "phishing",
"version": 4
},
{
"version": 1,
"name": "dcso-sharing",
"description": "DCSO Sharing Taxonomy to classify certain types of MISP events using the DCSO Event Guide"
"description": "Penetration test (pentest) classification.",
"name": "pentest",
"version": 3
},
{
"version": 2,
"name": "ddos",
"description": "Distributed Denial of Service - or short: DDoS - taxonomy supports the description of Denial of Service attacks and especially the types they belong too."
"description": "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.",
"name": "priority-level",
"version": 2
},
{
"version": 1,
"name": "de-vs",
"description": "Taxonomy for the handling of protectively marked information in MISP with German (DE) Government classification markings (VS)"
"description": "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\u20131. doi:10.1109/comst.2018.2849614",
"name": "threats-to-dns",
"version": 1
},
{
"version": 2,
"name": "dhs-ciip-sectors",
"description": "DHS critical sectors as described in https://www.dhs.gov/critical-infrastructure-sectors."
"description": "Exercise is a taxonomy to describe if the information is part of one or more cyber or crisis exercise.",
"name": "exercise",
"version": 5
},
{
"version": 1,
"name": "diamond-model",
"description": "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."
"description": "Workflow support language is a common language to support intelligence analysts to perform their analysis on data and information.",
"name": "workflow",
"version": 10
},
{
"version": 1,
"name": "DML",
"description": "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."
"description": "Classification based on malware stealth techniques. Described in https://vxheaven.org/lib/pdf/Introducing%20Stealth%20Malware%20Taxonomy.pdf",
"name": "stealth_malware",
"version": 1
},
{
"version": 3,
"name": "dni-ism",
"description": "ISM (Information Security Marking Metadata) V13 as described by DNI.gov (Director of National Intelligence - US)."
"description": "DHS critical sectors as in https://www.dhs.gov/critical-infrastructure-sectors",
"name": "dhs-ciip-sectors",
"version": 2
},
{
"version": 1,
"name": "domain-abuse",
"description": "Taxonomy to tag domain names used for cybercrime."
"description": "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.",
"name": "fpf",
"version": 0
},
{
"version": 1,
"name": "drugs",
"description": "A taxonomy based on the superclass and class of drugs, based on https://www.drugbank.ca/releases/latest"
"description": "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 \u2018The Business\u2019.",
"name": "gea-nz-activities",
"version": 1
},
{
"version": 1,
"name": "ecsirt",
"description": "eCSIRT incident classification Appendix C of the eCSIRT EU project including IntelMQ updates."
"description": "This taxonomy aims to ballpark the expected amount of false positives.",
"name": "false-positive",
"version": 4
},
{
"version": 201601,
"name": "enisa",
"description": "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"
"description": "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.",
"name": "euci",
"version": 3
},
{
"version": 3,
"name": "estimative-language",
"description": "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."
"description": "Tags from RiskIQ's PassiveTotal service",
"name": "passivetotal",
"version": 2
},
{
"version": 1,
"name": "euci",
"description": "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"
"description": "Vectors used to deliver malware based on MAEC 5.0",
"name": "maec-delivery-vectors",
"version": 1
},
{
"version": 2,
"name": "eu-marketop-and-publicadmin",
"description": "Market operators and public administrations that must comply to some notifications requirements under EU NIS directive."
"description": "A taxonomy based on the superclass and class of drugs. Based on https://www.drugbank.ca/releases/latest",
"name": "drugs",
"version": 2
},
{
"version": 1,
"name": "europol-event",
"description": "EUROPOL type of events taxonomy."
"description": "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",
"name": "estimative-language",
"version": 5
},
{
"version": 1,
"name": "europol-incident",
"description": "EUROPOL class of incident taxonomy."
"description": "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)",
"name": "ais-marking",
"version": 2
},
{
"version": 1,
"name": "event-assessment",
"description": "A series of assessment predicates describing the event assessment performed to make judgement(s) under a certain level of uncertainty."
"description": "MONARC Threats Taxonomy",
"name": "monarc-threat",
"version": 1
},
{
"version": 1,
"name": "fr-classif",
"description": "French gov information classification system."
"description": "Course of action taken within organization to discover, detect, deny, disrupt, degrade, deceive and/or destroy an attack.",
"name": "coa",
"version": 2
},
{
"version": 1,
"name": "iep",
"description": "Forum of Incident Response and Security Teams (FIRST) Information Exchange Policy (IEP) framework."
"description": "Ransomware is used to define ransomware types and the elements that compose them.",
"name": "ransomware",
"version": 4
},
{
"version": 1,
"name": "information-security-indicators",
"description": "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"
"description": "Internal taxonomy for CCCS.",
"name": "cccs",
"version": 2
},
{
"version": 1,
"name": "interception-method",
"description": "The interception method used to intercept traffic."
"description": "A series of assessment predicates describing the event assessment performed to make judgement(s) under a certain level of uncertainty.",
"name": "event-assessment",
"version": 2
},
{
"version": 1,
"name": "kill-chain",
"description": "Cyber Kill Chain from Lockheed Martin as described in Intelligence-Driven Computer Network Defense Informed by Analysis of Adversary Campaigns and Intrusion Kill Chains."
"description": "Threat taxonomy in the scope of securing smart airports by ENISA. https://www.enisa.europa.eu/publications/securing-smart-airports",
"name": "smart-airports-threats",
"version": 1
},
{
"version": 1,
"name": "malware_classification",
"description": "Malware classification based on a SANS whitepaper about malware."
"description": "Open Threat Taxonomy v1.1 base on James Tarala of SANS 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://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",
"name": "open_threat",
"version": 1
},
{
"version": 9,
"name": "misp",
"description": "Internal MISP taxonomy."
"description": "German (DE) Government classification markings (VS).",
"name": "de-vs",
"version": 1
},
{
"version": 1,
"name": "ms-caro-malware",
"description": "Malware Type and Platform classification based on Microsoft's implementation of the Computer Antivirus Research Organization (CARO) Naming Scheme and Malware Terminology."
"description": "Data classification for data potentially at risk of exfiltration based on table 2.1 of Solving Cyber Risk book.",
"name": "data-classification",
"version": 1
},
{
"version": 1,
"name": "ms-caro-malware-full",
"description": "Malware Type and Platform classification based on Microsoft's implementation of the Computer Antivirus Research Organization (CARO) Naming Scheme and Malware Terminology."
"description": "Flags describing the sample",
"name": "scrippsco2-fgc",
"version": 1
},
{
"version": 1,
"name": "nato",
"description": "Marking of Classified and Unclassified materials as described by the North Atlantic Treaty Organization, NATO."
"description": "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/security/portal/mmpc/shared/glossary.aspx, https://www.microsoft.com/security/portal/mmpc/shared/objectivecriteria.aspx, and 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 and https://www.microsoft.com/en-us/security/portal/threat/threats.aspx. Note that SIRs do NOT include all Microsoft malware families.",
"name": "ms-caro-malware-full",
"version": 2
},
{
"version": 1,
"name": "open_threat",
"description": "Open Threat Taxonomy v1.1 base on James Tarala of SANS ref. - http://www.auditscripts.com/resources/open_threat_taxonomy_v1.1a.pdf"
"description": "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.",
"name": "dcso-sharing",
"version": 1
},
{
"version": 9,
"name": "osint",
"description": "Open Source Intelligence - Classification (MISP taxonomies)."
"description": "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.",
"name": "retention",
"version": 3
},
{
"version": 1,
"name": "PAP",
"description": "The Permissible Actions Protocol - or short: PAP - was designed to indicate how the received information can be used."
"description": "CERT-XLM Security Incident Classification.",
"name": "CERT-XLM",
"version": 2
},
{
"version": 1,
"name": "passivetotal",
"description": "Tags for RiskIQ's passivetotal service"
"description": "Taxonom\u00eda CSIRT Am\u00e9ricas.",
"name": "csirt-americas",
"version": 1
},
{
"version": 1,
"name": "pentest",
"description": "Penetration test (pentest) classification."
"description": "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.",
"name": "enisa",
"version": 20170725
},
{
"version": 1,
"name": "rt_event_status",
"description": "Status of events used in Request Tracker."
"description": "List of known file types.",
"name": "file-type",
"version": 1
},
{
"version": 1,
"name": "stealth_malware",
"description": "Classification based on malware stealth techniques."
"description": "The access method used to remotely access a system.",
"name": "access-method",
"version": 1
},
{
"version": 1,
"name": "stix-ttp",
"description": "Representation of the behavior or modus operandi of cyber adversaries (a.k.a TTP) as normalized in STIX"
"description": "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/security/portal/mmpc/shared/glossary.aspx, https://www.microsoft.com/security/portal/mmpc/shared/objectivecriteria.aspx, and 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 and https://www.microsoft.com/en-us/security/portal/threat/threats.aspx. Note that SIRs do NOT include all Microsoft malware families.",
"name": "ms-caro-malware",
"version": 1
},
{
"version": 2,
"name": "targeted-threat-index",
"description": "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."
"description": "Sampling stations of the Scripps CO2 Program",
"name": "scrippsco2-sampling-stations",
"version": 1
},
{
"version": 3,
"name": "tlp",
"description": "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."
"description": "Taxonomy used by GSMA for their information sharing program with telco describing the attack categories",
"name": "gsma-attack-category",
"version": 1
},
{
"version": 1,
"name": "tor",
"description": "Taxonomy to describe Tor network infrastructure"
"description": "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",
"name": "cyber-threat-framework",
"version": 2
},
{
"version": 2,
"name": "veris",
"description": "Vocabulary for Event Recording and Incident Sharing (VERIS)."
"description": "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.",
"name": "copine-scale",
"version": 3
},
{
"version": 2,
"name": "vocabulaire-des-probabilites-estimatives",
"description": "Vocabulaire des probabilités estimatives"
"description": "Taxonomy used by GSMA for their information sharing program with telco describing the types of infrastructure. WiP",
"name": "gsma-network-technology",
"version": 3
},
{
"version": 2,
"name": "workflow",
"description": "Workflow support language is a common language to support intelligence analysts to perform their analysis on data and information."
"description": "Open Source Intelligence - Classification (MISP taxonomies)",
"name": "osint",
"version": 11
},
{
"version": 1,
"name": "runtime-packer",
"description": "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."
"description": "Action taken in the case of a security incident (CSIRT perspective).",
"name": "action-taken",
"version": 2
},
{
"version": 4,
"name": "honeypot-basic",
"description": "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"
"description": "Malware Capabilities based on MAEC 5.0",
"name": "maec-malware-capabilities",
"version": 2
},
{
"version": 1,
"name": "incident-disposition",
"description": "How an incident is classified in its process to be resolved. The taxonomy is inspired from NASA Incident Response and Management Handbook."
"description": "Market operators and public administrations that must comply to some notifications requirements under EU NIS directive",
"name": "eu-marketop-and-publicadmin",
"version": 1
},
{
"version": 1,
"name": "cyber-threat-framework",
"description": "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"
"description": "A pre-approved category of action for indicators being shared with partners (MIMIC).",
"name": "approved-category-of-action",
"version": 1
},
{
"version": 1,
"name": "priority-level",
"description": "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."
"description": "Reference Security Incident Classification Taxonomy",
"name": "rsit",
"version": 3
},
{
"version": 1,
"name": "eu-nis-sector-and-subsectors",
"description": "Sectors and sub sectors as identified by the NIS Directive."
"description": "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)",
"name": "gdpr",
"version": 0
},
{
"version": 3,
"name": "economical-impact",
"description": "Economical impact is a taxonomy to describe the financial impact as positive or negative gain to the tagged information."
"description": "Common Taxonomy for Law enforcement and CSIRTs",
"name": "common-taxonomy",
"version": 3
},
{
"version": 1,
"name": "fpf",
"description": "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."
"description": "FIRST.ORG CTI SIG - MISP Proposal for ICS/OT Threat Attribution (IOC) Project",
"name": "ics",
"version": 1
},
{
"version": 1,
"name": "gdpr",
"description": "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)"
"description": "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.",
"name": "collaborative-intelligence",
"version": 3
},
{
"version": 1,
"name": "infoleak",
"description": "A taxonomy describing information leaks and especially information classified as being potentially leaked."
"description": "Ce vocabulaire attribue des valeurs en pourcentage \u00e0 certains \u00e9nonc\u00e9s de probabilit\u00e9",
"name": "vocabulaire-des-probabilites-estimatives",
"version": 3
},
{
"version": 1,
"name": "copine-scale",
"description": "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."
"description": "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).",
"name": "economical-impact",
"version": 4
},
{
"name": "maec-delivery-vectors",
"description": "Vectors used to deliver malware based on MAEC 5.0",
"version": 1
"description": "Distributed Denial of Service - or short: DDoS - taxonomy supports the description of Denial of Service attacks and especially the types they belong too.",
"name": "ddos",
"version": 2
},
{
"name": "maec-malware-behavior",
"description": "Malware behaviours based on MAEC 5.0",
"version": 1
"description": "Incident Classification by the ecsirt.net version mkVI of 31 March 2015 enriched with IntelMQ taxonomy-type mapping.",
"name": "ecsirt",
"version": 2
},
{
"name": "maec-malware-obfuscation-methods",
"description": "Obfuscation methods used by malware based on MAEC 5.0",
"version": 1
"description": "Status of events used in Request Tracker.",
"name": "rt_event_status",
"version": 2
},
{
"name": "maec-malware-capabilities",
"description": "Malware Capabilities based on MAEC 5.0",
"description": "Information relating to authority or governance.",
"name": "gea-nz-motivators",
"version": 1
},
{
"name": "smart-airports-threats",
"description": "Threat taxonomy in the scope of securing smart airports by ENISA.",
"version": 1
"description": "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.",
"name": "admiralty-scale",
"version": 5
},
{
"version": 3,
"name": "false-positive",
"description": "This taxonomy aims to ballpark the expected amount of false positives."
"description": "The interception method used to intercept traffic.",
"name": "interception-method",
"version": 1
},
{
"version": 1,
"name": "rsit",
"description": "Reference Security Incident Classification Taxonomy."
"description": "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%20Security%20Incident%20Management%5d.pdf#page=9",
"name": "incident-disposition",
"version": 2
},
{
"version": 1,
"name": "nis",
"description": "NIS Cybersecurity Incident Taxonomy."
"description": "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.",
"name": "DML",
"version": 1
},
{
"version": 1,
"name": "ifx-vetting",
"description": "The IFX taxonomy is used to categorise information (MISP events and attributes) to aid in the intelligence vetting process"
"description": "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.",
"name": "infoleak",
"version": 7
},
{
"version": 1,
"name": "monarc-threat",
"description": "MONARC threat taxonomy."
"description": "The Cyber Kill Chain, a phase-based model developed by Lockheed Martin, aims to help categorise and identify the stage of an attack.",
"name": "kill-chain",
"version": 2
},
{
"version": 1,
"name": "file-type",
"description": "List of known file types."
"description": "NATO classification markings.",
"name": "nato",
"version": 2
},
{
"version": 1,
"name": "gsma-attack-category",
"description": "Taxonomy used by GSMA for their information sharing program with telco describing the attack categories"
"description": "Internet of Things taxonomy, based on IOT UK report https://iotuk.org.uk/wp-content/uploads/2017/01/IOT-Taxonomy-Report.pdf",
"name": "iot",
"version": 1
},
{
"version": 1,
"name": "gsma-fraud",
"description": "Taxonomy used by GSMA for their information sharing program with telco describing the various aspects of fraud"
"description": "An overview and description of the adversary infrastructure",
"name": "adversary",
"version": 4
},
{
"version": 1,
"name": "gsma-network-technology",
"description": "Taxonomy used by GSMA for their information sharing program with telco describing the types of infrastructure. WiP"
"description": "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.",
"name": "diamond-model",
"version": 1
},
{
"version": 1,
"name": "event-classification",
"description": "Event Classification."
"description": "The Permissible Actions Protocol - or short: PAP - was designed to indicate how the received information can be used.",
"name": "PAP",
"version": 2
},
{
"version": 1,
"name": "use-case-applicability",
"description": "The Use Case Applicability categories reflect standard resolution categories, to clearly display alerting rule configuration problems."
"description": "Malware behaviours based on MAEC 5.0",
"name": "maec-malware-behavior",
"version": 1
},
{
"version": 5,
"name": "exercise",
"description": "Exercise is a taxonomy to describe if the information is part of one or more cyber or crisis exercise."
"description": "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.",
"name": "nis",
"version": 2
},
{
"version": 1,
"name": "data-classification",
"description": "Data classification for data potentially at risk of exfiltration based on table 2.1 of Solving Cyber Risk book."
"description": "French gov information classification system",
"name": "fr-classif",
"version": 3
},
{
"version": 1,
"name": "type",
"description": "Taxonomy to describe different types of intelligence gathering discipline which can be described the origin of intelligence."
"description": "The IFX taxonomy is used to categorise information (MISP events and attributes) to aid in the intelligence vetting process",
"name": "ifx-vetting",
"version": 3
},
{
"version": 1,
"name": "information-security-data-source",
"description": "Taxonomy to classify the information security data sources"
"description": "Access Now classification to classify an issue (such as security, human rights, youth rights).",
"name": "accessnow",
"version": 3
},
{
"version": 1,
"name": "gea-nz-entities",
"description": "Information relating to instances of entities or things."
"description": "Taxonomy to describe Tor network infrastructure",
"name": "tor",
"version": 1
},
{
"version": 1,
"name": "gea-nz-activities",
"description": "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’."
"description": "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).",
"name": "flesch-reading-ease",
"version": 2
},
{
"version": 3,
"name": "gea-nz-motivators",
"description": "Information relating to authority or governance."
"description": "Taxonomy used by GSMA for their information sharing program with telco describing the various aspects of fraud",
"name": "gsma-fraud",
"version": 1
},
{
"version": 1,
"name": "cryptocurrency-threat",
"description": "Threats targetting cryptocurrency, based on CipherTrace report."
"description": "Domain Name Abuse - taxonomy to tag domain names used for cybercrime. Use europol-incident to tag abuse-activity",
"name": "domain-abuse",
"version": 1
},
{
"version": 1,
"name": "flesch-reading-ease",
"description": "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)."
"description": "Updated (CIRCL, Seamus Dowling and EURECOM) from Christian Seifert, Ian Welch, Peter Komisarczuk, \u2018Taxonomy of Honeypots\u2019, 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",
"name": "honeypot-basic",
"version": 4
},
{
"version": 3,
"name": "common-taxonomy",
"description": "The Common Taxonomy for Law Enforcement and The National Network of CSIRTs bridges the gap between the CSIRTs and international Law Enforcement communities by adding a legislative framework to facilitate the harmonisation of incident reporting to competent authorities, the development of useful statistics and sharing information within the entire cybercrime ecosystem."
"description": "Forum of Incident Response and Security Teams (FIRST) Information Exchange Policy (IEP) framework",
"name": "iep",
"version": 2
},
{
"version": 1,
"name": "ransomware",
"description": "Ransomware is used to define ransomware types and the elements that compose them."
"description": "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.",
"name": "analyst-assessment",
"version": 4
},
{
"version": 3,
"name": "dark-web",
"description": "Criminal motivation on the dark web: A categorisation model for law enforcement. ref: Janis Dalins, Campbell Wilson, Mark Carman. Taxonomy updated by MISP Project."
"description": "Sectors and sub sectors as identified by the NIS Directive",
"name": "eu-nis-sector-and-subsectors",
"version": 1
},
{
"version": 2,
"name": "retention",
"description": "Retention taxonomy to describe the retention period of the tagged information."
"description": "Flags describing the sample for isotopic data (C14, O18)",
"name": "scrippsco2-fgi",
"version": 1
},
{
"version": 1,
"name": "threats-to-dns",
"description": "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"
"description": "This taxonomy was designed to describe the type of events",
"name": "europol-event",
"version": 1
},
{
"version": 1,
"name": "csirt-americas",
"description": "Taxonomy from CSIRTAmericas.org."
"description": "Classification of events as seen in tools such as RT/IR, MISP and other",
"name": "event-classification",
"version": 1
},
{
"version": 1,
"name": "scrippsco2-fgc",
"description": "Flags describing the sample"
"description": "Criminal motivation on the dark web: A categorisation model for law enforcement. ref: Janis Dalins, Campbell Wilson, Mark Carman. Taxonomy updated by MISP Project",
"name": "dark-web",
"version": 3
},
{
"version": 1,
"name": "scrippsco2-fgi",
"description": "Flags describing the sample for isotopic data (C14, O18)"
"description": "CIRCL Taxonomy - Schemes of Classification in Incident Response and Detection",
"name": "circl",
"version": 3
},
{
"version": 1,
"name": "scrippsco2-sampling-stations",
"description": "Sampling stations of the Scripps CO2 Program"
"description": "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.",
"name": "runtime-packer",
"version": 1
},
{
"version": 4,
"name": "phishing",
"description": "Taxonomy to classify phishing attacks including techniques, collection mechanisms and analysis status."
"description": "A full set of operational indicators for organizations to use to benchmark their security posture.",
"name": "information-security-indicators",
"version": 1
},
{
"description": "FIRST.ORG CTI SIG - MISP Proposal for ICS/OT Threat Attribution (IOC) Project",
"version": 1,
"name": "ics"
"description": "Information relating to instances of entities or things.",
"name": "gea-nz-entities",
"version": 1
},
{
"name": "course-of-action",
"description": "A Course Of Action analysis considers six potential courses of action for the development of a cyber security capability.",
"version": 2
"description": "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.",
"name": "tlp",
"version": 5
},
{
"name": "iot",
"description": "Internet of Things taxonomy, based on IOT UK report https://iotuk.org.uk/wp-content/uploads/2017/01/IOT-Taxonomy-Report.pdf",
"description": "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\u2019s with proper case handling procedures and will form the basis of SLA\u2019s between the CSIRT and other Company departments.",
"name": "csirt_case_classification",
"version": 1
}
],
"path": "machinetag.json",
"url": "https://raw.githubusercontent.com/MISP/misp-taxonomies/master/",
"description": "Manifest file of MISP taxonomies available.",
"license": "CC-0",
"version": "20191023"
}
"version": "20191105"
}

1156
Summary.md
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파일 크기가 너무 크기때문에 변경 상태를 표시하지 않습니다.

2
ifx-vetting/machinetag.json
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@ -567,4 +567,4 @@
]
}
]
}
}

48
tools/gen_manifest.py
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@ -0,0 +1,48 @@
#!/usr/bin/env python3
import json
from pathlib import Path
from datetime import datetime
TAXONOMY_ROOT_PATH = Path(__file__).resolve().parent.parent
def fetchTaxonomies():
taxonomiesFolder = TAXONOMY_ROOT_PATH
taxonomies = []
for taxonomyFile in taxonomiesFolder.glob('./*/machinetag.json'):
with open(taxonomyFile) as f:
taxonomy = json.load(f)
taxonomies.append(taxonomy)
return taxonomies
def generateManifest(taxonomies):
manifest = {}
manifest['taxonomies'] = []
manifest['path'] = 'machinetag.json'
manifest['url'] = 'https://raw.githubusercontent.com/MISP/misp-taxonomies/master/'
manifest['description'] = 'Manifest file of MISP taxonomies available.'
manifest['license'] = 'CC-0'
now = datetime.now()
manifest['version'] = '{}{:02}{:02}'.format(now.year, now.month, now.day)
for taxonomy in taxonomies:
taxObj = {
'name': taxonomy['namespace'],
'description': taxonomy['description'],
'version': taxonomy['version']
}
manifest['taxonomies'].append(taxObj)
return manifest
def saveManifest(manifest):
with open(TAXONOMY_ROOT_PATH / 'MANIFEST.json', 'w') as f:
json.dump(manifest, f, indent=2, sort_keys=True)
def awesomePrint(text):
print('\033[1;32m{}\033[0;39m'.format(text))
if __name__ == "__main__":
taxonomies = fetchTaxonomies()
manifest = generateManifest(taxonomies)
saveManifest(manifest)
awesomePrint('> Manifest saved!')

49
tools/gen_markdown.py
파일 보기

@ -0,0 +1,49 @@
#!/usr/bin/env python3
import json
from pathlib import Path
from datetime import datetime
TAXONOMY_ROOT_PATH = Path(__file__).resolve().parent.parent
def fetchTaxonomies():
taxonomiesFolder = TAXONOMY_ROOT_PATH
taxonomies = []
for taxonomyFile in taxonomiesFolder.glob('./*/machinetag.json'):
with open(taxonomyFile) as f:
taxonomy = json.load(f)
taxonomies.append(taxonomy)
return taxonomies
def generateMarkdown(taxonomies):
markdown_line_array = []
markdown_line_array.append("# Taxonomies")
markdown_line_array.append("- Generation date: %s" % datetime.now().isoformat().split('T')[0])
markdown_line_array.append("- license: %s" % 'CC-0')
markdown_line_array.append("- description: %s" % 'Manifest file of MISP taxonomies available.')
markdown_line_array.append("")
markdown_line_array.append("## Taxonomies")
markdown_line_array.append("")
for taxonomy in taxonomies:
markdown_line_array.append("### %s" % taxonomy['namespace'])
markdown_line_array.append("- description: %s" % taxonomy['description'])
markdown_line_array.append("- version: %s" % taxonomy['version'])
markdown_line_array.append("- Predicates")
markdown_line_array = markdown_line_array + [' - '+p['value'] for p in taxonomy['predicates']]
markdown = '\n'.join(markdown_line_array)
return markdown
def saveMarkdown(markdown):
with open(TAXONOMY_ROOT_PATH / 'Summary.md', 'w') as f:
f.write(markdown)
def awesomePrint(text):
print('\033[1;32m{}\033[0;39m'.format(text))
if __name__ == "__main__":
taxonomies = fetchTaxonomies()
markdown = generateMarkdown(taxonomies)
saveMarkdown(markdown)
awesomePrint('> Markdown saved!')
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