Constructing detectors in schema complementary space for anomaly detection

Hang, Xiaoshu and Dai, Honghua 2004, Constructing detectors in schema complementary space for anomaly detection, Lecture notes in computer science, vol. 3102/2004, pp. 275-286.

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Title Constructing detectors in schema complementary space for anomaly detection
Author(s) Hang, Xiaoshu
Dai, HonghuaORCID iD for Dai, Honghua
Journal name Lecture notes in computer science
Volume number 3102/2004
Start page 275
End page 286
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 directly construct detectors in the complementary space of self-data space, our approach first evolves a number of common schemata through coevolutionary genetic algorithm in self-data space, and then constructs detectors in the complementary space of the schemata. These common schemata characterize self-data space and thus guide the generation of detection rules. By converting data space into schema space, we can efficiently generate an appropriate number of detectors with diversity for anomaly detection. The approach is tested for its effectiveness through experiment with the published data set iris.
Language eng
Field of Research 080699 Information Systems not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2004, Springer-Verlag Berlin Heidelberg
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