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

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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.

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Author information

Authors and Affiliations

  1. Department of Information Network and Communication, Kanagawa Institute of Technology, 1030 Shimo-ogino, Atsugi, Kanagawa, 243-0292, Japan

    Takeshi Okamoto

  2. Department of Knowledge-Based Information Engineering, Toyohashi University of Technology, Aichi, Japan

    Yoshiteru Ishida

Authors
  1. Takeshi Okamoto
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  2. Yoshiteru Ishida
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Corresponding author

Correspondence to Takeshi Okamoto.

Additional information

This work was presented in part at the 15th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2010

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Okamoto, T., Ishida, Y. Evaluations for immunity-based anomaly detection with dynamic updating of profiles. Artif Life Robotics 15, 225–228 (2010). https://doi.org/10.1007/s10015-010-0800-0

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  • DOI: https://doi.org/10.1007/s10015-010-0800-0

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