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Artificial Immune Systems for Anomaly Detection
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Author(s): Eduard Plett (Kansas State University at Salina, USA), Sanjoy Das (Kansas State University, USA), Dapeng Li (Kansas State University, USA)and Bijaya K. Panigrahi (Indian Institute of Technology, India)
Copyright: 2010
Pages: 19
Source title:
Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
Source Author(s)/Editor(s): Emilio Soria Olivas (University of Valencia, Spain), José David Martín Guerrero (University of Valencia, Spain), Marcelino Martinez-Sober (University of Valencia, Spain), Jose Rafael Magdalena-Benedito (University of Valencia, Spain)and Antonio José Serrano López (University of Valencia, Spain)
DOI: 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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