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Communication Dans Un Congrès Année : 2010

0day anomaly detection made possible thanks to machine learning

Résumé

This paper proposes new cognitive algorithms and mechanisms for detecting 0day attacks targeting the Internet and its communication performances and behavior. For this purpose, this work relies on the use of machine learning techniques able to issue autonomously traffic models and new attack signatures when new attacks are detected, characterized and classified as such. The ultimate goal deals with being able to instantaneously deploy new defense strategies when a new 0day attack is encountered, thanks to an autonomous cognitive system. The algorithms and mechanisms are validated through extensive experiments taking advantage of real traffic traces captured on the Renater network as well as on a WIDE transpacific link between Japan and the USA.
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Dates et versions

hal-00466317 , version 1 (23-03-2010)

Identifiants

  • HAL Id : hal-00466317 , version 1

Citer

Philippe Owezarski, Johan Mazel, Yann Labit. 0day anomaly detection made possible thanks to machine learning. International Conference on Wired/Wireless Internet Communications (WWIC 2010), Jun 2010, Lulea, Sweden. p. 327-338. ⟨hal-00466317⟩
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