MISP Taxonomies

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.

CERT-XLM

CERT-XLM : CERT-XLM Security Incident Classification. Overview

DFRLab-dichotomies-of-disinformation

DFRLab-dichotomies-of-disinformation : DFRLab Dichotomies of Disinformation. Overview

DML

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

PAP

PAP : The Permissible Actions Protocol - or short: PAP - was designed to indicate how the received information can be used. Overview

access-method

access-method : The access method used to remotely access a system. Overview

accessnow

accessnow : Access Now classification to classify an issue (such as security, human rights, youth rights). Overview

action-taken

action-taken : Action taken in the case of a security incident (CSIRT perspective). Overview

admiralty-scale

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

adversary

adversary : An overview and description of the adversary infrastructure Overview

ais-marking

ais-marking : The AIS Marking Schema implementation is maintained by the National Cybersecurity and Communication Integration Center (NCCIC) of the U.S. Department of Homeland Security (DHS) Overview

analyst-assessment

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

approved-category-of-action

approved-category-of-action : A pre-approved category of action for indicators being shared with partners (MIMIC). Overview

binary-class

binary-class : Custom taxonomy for types of binary file. Overview

cccs

cccs : Internal taxonomy for CCCS. Overview

circl

circl : CIRCL Taxonomy - Schemes of Classification in Incident Response and Detection Overview

coa

coa : Course of action taken within organization to discover, detect, deny, disrupt, degrade, deceive and/or destroy an attack. Overview

collaborative-intelligence

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

common-taxonomy

common-taxonomy : Common Taxonomy for Law enforcement and CSIRTs Overview

copine-scale

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

course-of-action

course-of-action : A Course Of Action analysis considers six potential courses of action for the development of a cyber security capability. Overview

cryptocurrency-threat

cryptocurrency-threat : Threats targetting cryptocurrency, based on CipherTrace report. Overview

csirt-americas

csirt-americas : Taxonomía CSIRT Américas. Overview

csirt_case_classification

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

cssa

cssa : The CSSA agreed sharing taxonomy. Overview

current-event

current-event : Current events - Schemes of Classification in Incident Response and Detection Overview

cyber-threat-framework

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

cycat

cycat : Taxonomy used by CyCAT, the Universal Cybersecurity Resource Catalogue, to categorize the namespaces it supports and uses. Overview

cytomic-orion

cytomic-orion : Taxonomy to describe desired actions for Cytomic Orion Overview

dark-web

dark-web : Criminal motivation on the dark web: A categorisation model for law enforcement. ref: Janis Dalins, Campbell Wilson, Mark Carman. Taxonomy updated by MISP Project Overview

data-classification

data-classification : Data classification for data potentially at risk of exfiltration based on table 2.1 of Solving Cyber Risk book. Overview

dcso-sharing

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

ddos

ddos : Distributed Denial of Service - or short: DDoS - taxonomy supports the description of Denial of Service attacks and especially the types they belong too. Overview

de-vs

de-vs : German (DE) Government classification markings (VS). Overview

dhs-ciip-sectors

dhs-ciip-sectors : DHS critical sectors as in https://www.dhs.gov/critical-infrastructure-sectors Overview

diamond-model

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

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

domain-abuse

domain-abuse : Domain Name Abuse - taxonomy to tag domain names used for cybercrime. Use europol-incident to tag abuse-activity Overview

drugs

drugs : A taxonomy based on the superclass and class of drugs. Based on https://www.drugbank.ca/releases/latest Overview

economical-impact

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

ecsirt

ecsirt : Incident Classification by the ecsirt.net version mkVI of 31 March 2015 enriched with IntelMQ taxonomy-type mapping. Overview

enisa

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 : 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

eu-marketop-and-publicadmin

eu-marketop-and-publicadmin : Market operators and public administrations that must comply to some notifications requirements under EU NIS directive Overview

eu-nis-sector-and-subsectors

eu-nis-sector-and-subsectors : Sectors, subsectors, and digital services as identified by the NIS Directive Overview

euci

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

europol-event

europol-event : This taxonomy was designed to describe the type of events Overview

europol-incident

europol-incident : This taxonomy was designed to describe the type of incidents by class. Overview

event-assessment

event-assessment : A series of assessment predicates describing the event assessment performed to make judgement(s) under a certain level of uncertainty. Overview

event-classification

event-classification : Classification of events as seen in tools such as RT/IR, MISP and other Overview

exercise

exercise : Exercise is a taxonomy to describe if the information is part of one or more cyber or crisis exercise. Overview

extended-event

extended-event : Reasons why an event has been extended. Overview

failure-mode-in-machine-learning

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

false-positive

false-positive : This taxonomy aims to ballpark the expected amount of false positives. Overview

file-type

file-type : List of known file types. Overview

flesch-reading-ease

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

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

fr-classif

fr-classif : French gov information classification system Overview

gdpr

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

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

gea-nz-entities

gea-nz-entities : Information relating to instances of entities or things. Overview

gea-nz-motivators

gea-nz-motivators : Information relating to authority or governance. Overview

gsma-attack-category

gsma-attack-category : Taxonomy used by GSMA for their information sharing program with telco describing the attack categories Overview

gsma-fraud

gsma-fraud : Taxonomy used by GSMA for their information sharing program with telco describing the various aspects of fraud Overview

gsma-network-technology

gsma-network-technology : Taxonomy used by GSMA for their information sharing program with telco describing the types of infrastructure. WiP Overview

honeypot-basic

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

ics

ics : FIRST.ORG CTI SIG - MISP Proposal for ICS/OT Threat Attribution (IOC) Project Overview

iep

iep : Forum of Incident Response and Security Teams (FIRST) Information Exchange Policy (IEP) framework Overview

iep2-policy

iep2-policy : Forum of Incident Response and Security Teams (FIRST) Information Exchange Policy (IEP) v2.0 Policy Overview

iep2-reference

iep2-reference : Forum of Incident Response and Security Teams (FIRST) Information Exchange Policy (IEP) v2.0 Reference Overview

ifx-vetting

ifx-vetting : The IFX taxonomy is used to categorise information (MISP events and attributes) to aid in the intelligence vetting process Overview

incident-disposition

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

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

information-security-data-source

information-security-data-source : Taxonomy to classify the information security data sources. Overview

information-security-indicators

information-security-indicators : A full set of operational indicators for organizations to use to benchmark their security posture. Overview

interception-method

interception-method : The interception method used to intercept traffic. Overview

iot

iot : Internet of Things taxonomy, based on IOT UK report https://iotuk.org.uk/wp-content/uploads/2017/01/IOT-Taxonomy-Report.pdf Overview

kill-chain

kill-chain : The Cyber Kill Chain, a phase-based model developed by Lockheed Martin, aims to help categorise and identify the stage of an attack. Overview

maec-delivery-vectors

maec-delivery-vectors : Vectors used to deliver malware based on MAEC 5.0 Overview

maec-malware-behavior

maec-malware-behavior : Malware behaviours based on MAEC 5.0 Overview

maec-malware-capabilities

maec-malware-capabilities : Malware Capabilities based on MAEC 5.0 Overview

maec-malware-obfuscation-methods

maec-malware-obfuscation-methods : Obfuscation methods used by malware based on MAEC 5.0 Overview

malware_classification

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

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

misp

misp : MISP taxonomy to infer with MISP behavior or operation. Overview

monarc-threat

monarc-threat : MONARC Threats Taxonomy Overview

ms-caro-malware

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

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

mwdb

mwdb : Malware Database (mwdb) Taxonomy - Tags used across the platform Overview

nato

nato : NATO classification markings. Overview

nis

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 : 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

osint

osint : Open Source Intelligence - Classification (MISP taxonomies) Overview

pandemic

pandemic : Pandemic Overview

passivetotal

passivetotal : Tags from RiskIQ's PassiveTotal service Overview

pentest

pentest : Penetration test (pentest) classification. Overview

phishing

phishing : Taxonomy to classify phishing attacks including techniques, collection mechanisms and analysis status. Overview

priority-level

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

ransomware

ransomware : Ransomware is used to define ransomware types and the elements that compose them. Overview

retention

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

rsit

rsit : Reference Security Incident Classification Taxonomy Overview

rt_event_status

rt_event_status : Status of events used in Request Tracker. Overview

runtime-packer

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

scrippsco2-fgc

scrippsco2-fgc : Flags describing the sample Overview

scrippsco2-fgi

scrippsco2-fgi : Flags describing the sample for isotopic data (C14, O18) Overview

scrippsco2-sampling-stations

scrippsco2-sampling-stations : Sampling stations of the Scripps CO2 Program Overview

smart-airports-threats

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

stealth_malware : Classification based on malware stealth techniques. Described in https://vxheaven.org/lib/pdf/Introducing%20Stealth%20Malware%20Taxonomy.pdf Overview

stix-ttp

stix-ttp : TTPs are representations of the behavior or modus operandi of cyber adversaries. Overview

targeted-threat-index

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

threatmatch-alert-types

threatmatch-alert-types : The ThreatMatch Alert types are applicable for any ThreatMatch instances and should be used for all CIISI and TIBER Projects. Overview

threatmatch-incident-types

threatmatch-incident-types : The ThreatMatch Incident types are applicable for any ThreatMatch instances and should be used for all CIISI and TIBER Projects. Overview

threatmatch-malware-types

threatmatch-malware-types : The ThreatMatch Malware types are applicable for any ThreatMatch instances and should be used for all CIISI and TIBER Projects. Overview

threatmatch-sectors

threatmatch-sectors : The ThreatMatch Sector types are applicable for any ThreatMatch instances and should be used for all CIISI and TIBER Projects. Overview

threats-to-dns

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

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

tor

tor : Taxonomy to describe Tor network infrastructure Overview

trust

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

type

type : Taxonomy to describe different types of intelligence gathering discipline which can be described the origin of intelligence. Overview

use-case-applicability

use-case-applicability : The Use Case Applicability categories reflect standard resolution categories, to clearly display alerting rule configuration problems. Overview

veris

veris : Vocabulary for Event Recording and Incident Sharing (VERIS) Overview

vocabulaire-des-probabilites-estimatives

vocabulaire-des-probabilites-estimatives : Ce vocabulaire attribue des valeurs en pourcentage à certains énoncés de probabilité Overview

workflow

workflow : Workflow support language is a common language to support intelligence analysts to perform their analysis on data and information. Overview

Reserved Taxonomy

The following taxonomy namespaces are reserved and used internally to MISP.

• galaxy mapping taxonomy with cluster:element:"value".

Documentation

A documentation of the taxonomies is generated automatically from the taxonomies description and available in PDF and HTML.

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.

MISP Taxonomies

Tools

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"
        ...

Library

• PyTaxonomies is a Python module to use easily the MISP Taxonomies.

License

The MISP taxonomies (JSON files) are dual-licensed under:

• CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication.

or

 Copyright (c) 2015-2021 Alexandre Dulaunoy - a@foo.be
 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.