PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines
Pavel Laskov, Christin Schaefer and Igor Kotenko
Detection of Intrusions and Malware & Vulnerability Assessment, Proceedings, Ulrich Flegel, Micheal Meier (eds.) pp. 71-82, 2004. ISSN 1617-5468

Abstract

Practical application of data mining and machine learning techniques to intrusion detection is often hindered by the difficulty to produce clean data for the training. To address this problem a geometric framework for unsupervised anomaly detection has been recently proposed. In this framework, the data is mapped into a feature space, and anomalies are detected as the entries in sparsely populated regions. In this contribution we propose a novel formulation of a one-class Support Vector Machine (SVM) specially designed for typical IDS data features. The key idea of our ''quarter-sphere'' algorithm is to encompass the data with a hypersphere anchored at the center of mass of the data in feature space. The proposed method and its behavior on varying percentages of attacks in the data is evaluated on the KDDCup 1999 dataset.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:769
Deposited By:Christin Schaefer
Deposited On:30 December 2004