Proactive intrusion detection

Benjamin Liebald, Dan Roth, Neelay Shah, Vivek Srikumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine learning systems are deployed in many adversarial conditions like intrusion detection, where a classifier has to decide whether a sequence of actions come from a legitimate user or not. However, the attacker, being an adversarial agent, could reverse engineer the classifier and successfully masquerade as a legitimate user. In this paper, we propose the notion of a Proactive Intrusion Detection System (IDS) that can counter such attacks by incorporating feedback into the process. A proactive IDS influences the user's actions and observes them in different situations to decide whether the user is an intruder. We present a formal analysis of proactive intrusion detection and extend the adversarial relationship between the IDS and the attacker to present a game theoretic analysis. Finally, we present experimental results on real and synthetic data that confirm the predictions of the analysis.

Original languageEnglish (US)
Title of host publicationAAAI-08/IAAI-08 Proceedings - 23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference
Pages772-777
Number of pages6
StatePublished - Dec 24 2008
Event23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08 - Chicago, IL, United States
Duration: Jul 13 2008Jul 17 2008

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Other

Other23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08
CountryUnited States
CityChicago, IL
Period7/13/087/17/08

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Liebald, B., Roth, D., Shah, N., & Srikumar, V. (2008). Proactive intrusion detection. In AAAI-08/IAAI-08 Proceedings - 23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference (pp. 772-777). (Proceedings of the National Conference on Artificial Intelligence; Vol. 2).