mirror of https://github.com/MISP/PyTaxonomies
188 lines
4.9 KiB
Markdown
188 lines
4.9 KiB
Markdown
# PyTaxonomies
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[![Build Status](https://travis-ci.org/MISP/PyTaxonomies.svg?branch=master)](https://travis-ci.org/MISP/PyTaxonomies)
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[![Coverage Status](https://coveralls.io/repos/github/MISP/PyTaxonomies/badge.svg?branch=master)](https://coveralls.io/github/MISP/PyTaxonomies?branch=master)
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[![codecov.io](https://codecov.io/github/MISP/PyTaxonomies/coverage.svg?branch=master)](https://codecov.io/github/MISP/PyTaxonomies?branch=master)
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Pythonic way to work with the taxonomies defined there: https://github.com/MISP/misp-taxonomies
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# Usage
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Taxonomies and predicates are represented as immutable Python dictionaries.
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# Installation
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```
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pip3 install git+https://github.com/MISP/PyTaxonomies
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```
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or
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```
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git clone https://github.com/MISP/PyTaxonomies
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cd PyTaxonomies
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git submodule init && git submodule update
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python3 setup.py install
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```
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## Basics
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```
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In [1]: from pytaxonomies import Taxonomies
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In [2]: taxonomies = Taxonomies()
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In [3]: taxonomies.version
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Out[3]: '20160725'
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In [4]: taxonomies.license
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Out[4]: 'CC-BY'
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In [5]: taxonomies.description
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Out[5]: 'Manifest file of MISP taxonomies available.'
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# How many taxonomies have been imported
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In [6]: len(taxonomies)
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Out[6]: 27
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# Names of the taxonomies
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In [7]: list(taxonomies.keys())
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Out[7]:
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['tlp',
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'eu-critical-sectors',
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'dni-ism',
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'de-vs',
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'osint',
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'ms-caro-malware',
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'open-threat',
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'circl',
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'iep',
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'euci',
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'kill-chain',
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'europol-events',
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'veris',
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'information-security-indicators',
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'estimative-language',
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'adversary',
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'europol-incident',
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'malware_classification',
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'ecsirt',
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'dhs-ciip-sectors',
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'csirt_case_classification',
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'nato',
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'fr-classif',
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'enisa',
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'misp',
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'admiralty-scale',
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'ms-caro-malware-full']
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In [8]: taxonomies.get('enisa').description
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Out[8]: '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.'
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In [9]: taxonomies.get('enisa').version
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Out[9]: 201601
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In [10]: taxonomies.get('enisa').name
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Out[10]: 'enisa'
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In [11]: list(taxonomies.get('enisa').keys())
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Out[11]:
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['legal',
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'outages',
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'eavesdropping-interception-hijacking',
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'nefarious-activity-abuse',
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'physical-attack',
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'failures-malfunction',
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'disaster',
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'unintentional-damage']
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In [12]: list(taxonomies.get('enisa').get('physical-attack'))
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Out[12]:
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['fraud-by-employees',
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'theft',
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'unauthorised-physical-access-or-unauthorised-entry-to-premises',
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'theft-of-documents',
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'information-leak-or-unauthorised-sharing',
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'vandalism',
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'damage-from-the-wafare',
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'sabotage',
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'coercion-or-extortion-or-corruption',
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'theft-of-mobile-devices',
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'theft-of-fixed-hardware',
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'terrorist-attack',
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'theft-of-backups',
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'fraud']
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In [13]: taxonomies.get('enisa').get('physical-attack').get('vandalism').value
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Out[13]: 'vandalism'
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In [14]: taxonomies.get('enisa').get('physical-attack').get('vandalism').expanded
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Out[14]: 'Vandalism'
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In [15]: taxonomies.get('enisa').get('physical-attack').get('vandalism').description
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Out[15]: 'Act of physically damaging IT assets.'
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```
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## Get machine tags
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```
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In [1]: print(taxonomies) # or taxonomies.all_machinetags()
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<display the machine tags for all the taxonomies>
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In [2]: print(taxonomies.get('circl')) # or taxonomies.get('circl').machinetags()
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circl:incident-classification="vulnerability"
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circl:incident-classification="malware"
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circl:incident-classification="fastflux"
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circl:incident-classification="system-compromise"
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circl:incident-classification="sql-injection"
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circl:incident-classification="scan"
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circl:incident-classification="XSS"
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circl:incident-classification="information-leak"
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circl:incident-classification="scam"
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circl:incident-classification="copyright-issue"
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circl:incident-classification="denial-of-service"
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circl:incident-classification="phishing"
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circl:incident-classification="spam"
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circl:topic="undefined"
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circl:topic="industry"
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circl:topic="ict"
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circl:topic="finance"
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circl:topic="services"
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circl:topic="individual"
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circl:topic="medical"
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# All entries
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In [3]: taxonomies.get('circl').amount_entries()
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Out[3]: 28
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# Amount predicates
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In [3]: len(taxonomies.get('circl'))
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Out[3]: 2
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```
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## Expanded machine tag
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```
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In [10]: print(taxonomies.get('circl').machinetags_expanded())
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circl:topic="Individual"
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circl:topic="Services"
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circl:topic="Finance"
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circl:topic="Medical"
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circl:topic="Industry"
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circl:topic="Undefined"
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circl:topic="ICT"
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circl:incident-classification="Phishing"
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circl:incident-classification="Malware"
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circl:incident-classification="XSS"
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circl:incident-classification="Copyright issue"
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circl:incident-classification="Spam"
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circl:incident-classification="SQL Injection"
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circl:incident-classification="Scan"
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circl:incident-classification="Scam"
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circl:incident-classification="Vulnerability"
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circl:incident-classification="Denial of Service"
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circl:incident-classification="Information leak"
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circl:incident-classification="Fastflux"
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circl:incident-classification="System compromise"
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```
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