• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Anomaly Intrusion Detection Based on Hyper-ellipsoid in the Kernel Feature Space


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.


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Cite this article

[IEEE Style]
H. Lee, D. Moon, I. Kim, H. Jung and D. Pa, "Anomaly Intrusion Detection Based on Hyper-ellipsoid in the Kernel Feature Space," KSII Transactions on Internet and Information Systems, vol. 9, no. 3, pp. 1173-1192, 2015. DOI: 10.3837/tiis.2015.03.019.

[ACM Style]
Hansung Lee, Daesung Moon, Ikkyun Kim, Hoseok Jung, and Daihee Pa. 2015. Anomaly Intrusion Detection Based on Hyper-ellipsoid in the Kernel Feature Space. KSII Transactions on Internet and Information Systems, 9, 3, (2015), 1173-1192. DOI: 10.3837/tiis.2015.03.019.