Online Anomaly Detection under Adversarial Impact

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Marius Kloft, Pavel Laskov ;
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:405-412, 2010.

Abstract

Security analysis of learning algorithms is gaining increasing importance, especially since they have become target of deliberate obstruction in certain applications. Some security-hardened algorithms have been previously proposed for supervised learning; however, very little is known about the behavior of anomaly detection methods in such scenarios. In this contribution, we analyze the performance of a particular method—online centroid anomaly detection—in the presence of adversarial noise. Our analysis addresses three key security-related issues: derivation of an optimal attack, analysis of its efficiency and constraints. Experimental evaluation carried out on real HTTP and exploit traces confirms the tightness of our theoretical bounds.

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