Conference Proceedings

CESVM: Centered hyperellipsoidal support vector machine based anomaly detection

Sutharshan Rajasegarar, Christopher Leckie, Marimuthu Palaniswami

2008 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, PROCEEDINGS, VOLS 1-13 | IEEE | Published : 2008

Abstract

A challenge in using machine learning for tasks such as network intrusion detection and fault diagnosis is the difficulty in obtaining clean data for training in order to model the normal behavior of the system. Unsupervised anomaly detection techniques such as one class support vector machines (SVMs) have been introduced to overcome this difficulty. One class support vector machines model the normal or target data using non-linear surfaces in the input space while ignoring the anomalous data. Our approach to this problem is based on fitting a hyperellipsoid with a minimal effective radius, centered at the origin, around a majority of the data vectors in a higher dimensional space. We formul..

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