Immunology-based subspace detectors for anomaly detection

Hang, X. and Dai, Honghua 2008, Immunology-based subspace detectors for anomaly detection, in Challenges in information technology management, World Scientific, New Jersey, pp.204-212.

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Title Immunology-based subspace detectors for anomaly detection
Author(s) Hang, X.
Dai, Honghua
Title of book Challenges in information technology management
Editor(s) Chan, Man-Chung
Cheung, Ronnie
Liu, James N.K.
Publication date 2008
Total chapters 30
Start page 204
End page 212
Total pages 9
Publisher World Scientific
Place of Publication New Jersey
Keyword(s) Information technology -- Management
Summary A key problem in high dimensional anomaly detection is that the time spent in constructing detectors by the means of generateand-test is tolerable. In fact, due to the high sparsity. of the data, it is ineffective to construct detectors in the whole data space. Previous investigations have shown that most essentIal patterns can be discovered in different subspaces. This inspires us to construct detectors in signIficant subspaces only for anomaly detection. We first use ENCLUS-based method to discover all significant subspaces and .then use a greedy-growth algorithm to construct detectors in each subspace. The elements used to constItute a detector are gods Instead of data points, which makes the time-consumption irrelevant to the size of the nonnal data. We test the effectiveness and efficiency of our method on both synthetic and benchmark datasets. The results reveal that our method is particularly useful in anomaly detection in high dimensional data spaces.
ISBN 9789812819062
9812819061
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
HERDC collection year 2008
Copyright notice ©2008, World Scientific
Persistent URL http://hdl.handle.net/10536/DRO/DU:30017053

Document type: Book Chapter
Collection: School of Engineering and Information Technology
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