MISP Taxonomies is a set of common classification libraries to tag, classify and organise information. Taxonomy allows to express the same vocabulary among a distributed set of users and organisations.
Taxonomies that can be used in MISP (2.4) and other information sharing tool and expressed in Machine Tags (Triple Tags). A machine tag is composed of a namespace (MUST), a predicate (MUST) and an (OPTIONAL) value. Machine tags are often called triple tag due to their format.
The following taxonomies can be used in MISP (as local or distributed tags) or in other tools and software willing to share common taxonomies among security information sharing tools.
List of available taxonomies
DML : 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. Overview
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. Overview
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. Overview
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. Overview
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. Overview
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. Overview
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 Overview
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. Overview
dhs-ciip-sectors : DHS critical sectors as in https://www.dhs.gov/critical-infrastructure-sectors Overview
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. Overview
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 Overview
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). Overview
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. Overview
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 Overview
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. Overview
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. Overview
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). Overview
fpf : The Future of Privacy Forum (FPF) visual guide to practical 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. Overview
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) Overview
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’. Overview
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 Overview
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%20Security%20Incident%20Management%5d.pdf#page=9 Overview
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. Overview
iot : Internet of Things taxonomy, based on IOT UK report https://iotuk.org.uk/wp-content/uploads/2017/01/IOT-Taxonomy-Report.pdf Overview
malware_classification : Classification based on different categories. Based on https://www.sans.org/reading-room/whitepapers/incident/malware-101-viruses-32848 Overview
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 Overview
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/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. Overview
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/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. Overview
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. Overview
open_threat : 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 Overview
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. Overview
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. Overview
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. Overview
smart-airports-threats : Threat taxonomy in the scope of securing smart airports by ENISA. https://www.enisa.europa.eu/publications/securing-smart-airports Overview
stealth_malware : Classification based on malware stealth techniques. Described in https://vxheaven.org/lib/pdf/Introducing%20Stealth%20Malware%20Taxonomy.pdf Overview
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. Overview
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 Overview
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. Overview
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. Overview
The following taxonomy namespaces are reserved and used internally to MISP.
- galaxy mapping taxonomy with cluster:element:"value".
How to contribute your taxonomy?
It is quite easy. Create a JSON file describing your taxonomy as triple tags (e.g. check an existing one like Admiralty Scale), create a directory matching your name space, put your machinetag file in the directory and pull your request. That's it. Everyone can benefit from your taxonomy and can be automatically enabled in information sharing tools like MISP.
For more information, "Information Sharing and Taxonomies Practical Classification of Threat Indicators using MISP" presentation given to the last MISP training in Luxembourg.
How to add your private taxonomy to MISP
$ cd /var/www/MISP/app/files/taxonomies/ $ mkdir privatetaxonomy $ cd privatetaxonomy $ vi machinetag.json
Create a JSON file describing your taxonomy as triple tags.
Once you are happy with your file go to MISP Web GUI taxonomies/index and update the taxonomies, the newly created taxonomy should be visible, now you need to activate the tags within your taxonomy.
machinetag.py is a parsing tool to dump taxonomies expressed in Machine Tags (Triple Tags) and list all valid tags from a specific taxonomy.
% cd tools % python machinetag.py admiralty-scale:source-reliability="a" admiralty-scale:source-reliability="b" admiralty-scale:source-reliability="c" admiralty-scale:source-reliability="d" admiralty-scale:source-reliability="e" admiralty-scale:source-reliability="f" admiralty-scale:information-credibility="1" admiralty-scale:information-credibility="2" admiralty-scale:information-credibility="3" admiralty-scale:information-credibility="4" admiralty-scale:information-credibility="5" admiralty-scale:information-credibility="6" ...
- PyTaxonomies is a Python module to use easily the MISP Taxonomies.
The MISP taxonomies (JSON files) are dual-licensed under:
- CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication.
Copyright (c) 2015-2021 Alexandre Dulaunoy - firstname.lastname@example.org Copyright (c) 2015-2021 CIRCL - Computer Incident Response Center Luxembourg Copyright (c) 2015-2021 Andras Iklody Copyright (c) 2015-2021 Raphael Vinot Copyright (c) 2016-2021 Various contributors to MISP Project Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
If a specific author of a taxonomy wants to license it under a different license, a pull request can be requested.