NDLI: Le Machine Learning confronté aux contraintes opérationnelles des systèmes de détection

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Le Machine Learning confronté aux contraintes opérationnelles des systèmes de détection

Content Provider	Semantic Scholar
Author	Beaugnon, Anaël Husson, Antoine
Copyright Year	2017
Abstract	Les systemes de detection d’intrusion, reposant traditionnellement sur des signatures, n’ont pas echappe a l’attrait recent des techniques de Machine Learning. Si les resultats presentes dans les articles de recherche academique sont souvent excellents, les experts en securite ont cependant encore de nombreuses reticences concernant l’utilisation du Machine Learning dans les systemes de detection d’intrusion. Ils redoutent generalement une inadequation de ces techniques aux contraintes operationnelles, notamment a cause d’un niveau d’expertise requis important, ou d’un grand nombre de faux positifs. Dans cet article, nous montrons que le Machine Learning peut etre compatible avec les contraintes operationnelles des systemes de detection. Nous expliquons comment construire un modele de detection et presentons de bonnes pratiques pour le valider avant sa mise en production. La methodologie est illustree par un cas d’etude sur la detection de fichiers PDF malveillants et nous proposons un outil libre, SecuML, pour la mettre en oeuvre.
Starting Page	317
Ending Page	346
Page Count	30
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://hal.archives-ouvertes.fr/hal-01636303/file/SSTIC2017-Article-le_machine_learning_confront_aux_contraintes_oprationnelles_des_systmes_de_dtection-bonneton_husson.pdf
Alternate Webpage(s)	https://www.sstic.org/media/SSTIC2017/SSTIC-actes/le_machine_learning_confront_aux_contraintes_oprat/SSTIC2017-Article-le_machine_learning_confront_aux_contraintes_oprationnelles_des_systmes_de_dtection-bonneton_husson.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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