Evaluations for immunity-based anomaly detection with dynamic updating of profiles

Authors:
Takeshi Okamoto;Yoshiteru Ishida
Affiliations:
Department of Information Network and Communication, Kanagawa Institute of Technology, Atsugi, Kanagawa, Japan 243-0292;Department of Knowledge-Based Information Engineering, Toyohashi University of Technology, Aichi, Japan
Venue:
Artificial Life and Robotics
Year:
2010

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Abstract

This article presents evaluations of an immunity-based anomaly detection method with dynamic updating of profiles. Our experiments showed that the updating of both self and nonself profiles markedly decreased both the false alarm and missed alarm rates in masquerader detection. In computer worm detection, all the random-scanning worms and simulated metaserver worms examined were detected. The detection accuracy of the simulated passive worm was markedly improved.