An extended negative selection algorithm for anomaly detection

Hang, Xiaoshu and Dai, Honghua 2004, An extended negative selection algorithm for anomaly detection, Lecture notes in computer science, vol. 3056, pp. 245-254.

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Title An extended negative selection algorithm for anomaly detection
Author(s) Hang, Xiaoshu
Dai, HonghuaORCID iD for Dai, Honghua
Journal name Lecture notes in computer science
Volume number 3056
Start page 245
End page 254
Publisher Springer-Verlag
Place of publication Berlin, Germany
Publication date 2004
ISSN 0302-9743
Summary This paper proposes an extended negative selection algorithm for anomaly detection. Unlike previously proposed negative selection algorithms which do not make use of non-self data, the extended negative selection algorithm first acquires prior knowledge about the characteristics of the Problem space from the historial sample data by using machine learning techniques. Such data consists of both self data and non-self data. The acquired prior knowledge is represented in the form of production rules and thus viewed as common schemata which characterise the two subspaces: self-subspace and non-self-subspace, and provide important information to the generation of detection rules. One advantage of our approach is that it does not rely on the structured representation of the data and can be applied to general anomaly detection. To test the effectiveness, we test our approach through experiments with the public data set iris and KDDrsquo99 published data set.
Notes Book Title : Advances in Knowledge Discovery and Data Mining
Language eng
Field of Research 080699 Information Systems not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2004, Springer-Verlag Berlin Heidelberg
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