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Artificial Immune Systems for Anomaly Detection

Artificial Immune Systems for Anomaly Detection

Eduard Plett, Sanjoy Das, Dapeng Li, Bijaya K. Panigrahi
Copyright: © 2010 |Pages: 19
ISBN13: 9781605667669|ISBN10: 1605667668|EISBN13: 9781605667676
DOI: 10.4018/978-1-60566-766-9.ch005
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MLA

Plett, Eduard, et al. "Artificial Immune Systems for Anomaly Detection." Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, edited by Emilio Soria Olivas, et al., IGI Global, 2010, pp. 109-127. https://doi.org/10.4018/978-1-60566-766-9.ch005

APA

Plett, E., Das, S., Li, D., & Panigrahi, B. K. (2010). Artificial Immune Systems for Anomaly Detection. In E. Olivas, J. Guerrero, M. Martinez-Sober, J. Magdalena-Benedito, & A. Serrano López (Eds.), Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques (pp. 109-127). IGI Global. https://doi.org/10.4018/978-1-60566-766-9.ch005

Chicago

Plett, Eduard, et al. "Artificial Immune Systems for Anomaly Detection." In Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, edited by Emilio Soria Olivas, et al., 109-127. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-766-9.ch005

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Abstract

This chapter introduces anomaly detection algorithms analogous to methods employed by the vertebrate immune system, with an emphasis on engineering applications. The basic negative selection approach, as well as its major extensions, is introduced. The chapter next proposes a novel scheme to classify all algorithmic extensions of negative selection into three basic classes: self-organization, evolution, and proliferation. In order to illustrate the effectiveness of negative selection based algorithms, one recent algorithm, the proliferating V-detectors method, is taken up for further study. It is applied to a real world anomaly detection problem in engineering, that of automatic testing of bearing machines. As anomaly detection can be considered as a binary classification problem, in order to further show the usefulness of negative selection, this algorithm is then modified to address a four-category problem, namely the classification of power signals based on the type of disturbance.

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