[MISC HS n°18] Références de l’article « Machine Learning en Python : clusterisation à la rescousse des hunters (de malwares) »

Retrouvez ci-dessous la liste des références qui accompagnent l’article « Machine Learning en Python : clusterisation à la rescousse des hunters (de malwares) », publié dans MISC HS n°18 :

[Corkami] https://github.com/corkami/pics/blob/master/binary/pe101/pe101.svg

[Pefile] https://github.com/erocarrera/pefile

[LIEF] https://github.com/lief-project/LIEF

[ssdeep] https://ssdeep-project.github.io/ssdeep/index.html

[ssdeep_for] http://dfrws.org/sites/default/files/session-files/paper-identifying_almost_identical_files_using_context_triggered_piecewise_hashing.pdf

[pehash] https://www.usenix.org/legacy/events/leet09/tech/full_papers/wicherski/wicherski_html/index.html

[imphash] https://www.fireeye.com/blog/threat-research/2014/01/tracking-malware-import-hashing.html

[tinynuke] https://github.com/rossja/TinyNuke

[impfuzzy] https://blog.jpcert.or.jp/2016/05/classifying-mal-a988.html

[impfuzzy_clust] https://blog.jpcert.or.jp/2017/03/malware-clustering-using-impfuzzy-and-network-analysis—impfuzzy-for-neo4j-.html

[polichombr] https://github.com/ANSSI-FR/polichombr

[machoke] https://github.com/conix-security/machoke

[Scikit] http://scikit-learn.org/stable/

[theZoo] https://github.com/ytisf/theZoo

[YaraGen] https://github.com/Xen0ph0n/YaraGenerator/

[Yar] http://virustotal.github.io/yara/

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