0day Anomaly Detection Made Possible Thanks to Machine Learning

Abstract. 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. Key Words: 0day anomaly detection, machine learning Acknowledgment — This work is achieved in the framework of the European ECODE project, granted and funded by the European Commission’s ICT program under reference FP7-ICT-2007-2...

• Type: Conference
• Year: 2010
• Where: WWIC
• Authors: Philippe Owezarski, Johan Mazel, Yann Labit
• Keywords: Autonomous Cognitive System, Communications, Machine Learning, Machine Learning Techniques, WWIC 2010