Anomaly intrusion detection based on hyper-ellipsoid in the kernel feature space

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Title
Anomaly intrusion detection based on hyper-ellipsoid in the kernel feature space
Author(s)
H Lee; D Moon; I Kim; Ho Seok Jung; D Park
Bibliographic Citation
KSII Transactions on Internet and Information Systems, vol. 9, no. 3, pp. 1173-1192
Publication Year
2015
Abstract
The Support Vector Data Description (SVDD) has achieved great success in anomaly detection, directly finding the optimal ball with a minimal radius and center, which contains most of the target data. The SVDD has some limited classification capability, because the hyper-sphere, even in feature space, can express only a limited region of the target class. This paper presents an anomaly detection algorithm for mitigating the limitations of the conventional SVDD by finding the minimum volume enclosing ellipsoid in the feature space. To evaluate the performance of the proposed approach, we tested it with intrusion detection applications. Experimental results show the prominence of the proposed approach for anomaly detection compared with the standard SVDD.
Keyword
Anomaly detectionIntrusion detectionKernel principal component analysisMinimum enclosing ellipsoid
ISSN
1976-7277
Publisher
KSII-Kor Soc Internet Information
DOI
http://dx.doi.org/10.3837/tiis.2015.03.019
Type
Article
Appears in Collections:
1. Journal Articles > Journal Articles
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