Gesellschaft fr Informatik e.V.

Lecture Notes in Informatics


Detection of Intrusions and Malware & Vulnerability Assessment, GI SIG SIDAR Workshop, DIMVA 2004, Dortmund, Germany, July 6.7, 2004 P-46, 71-82 (2004).

GI, Gesellschaft für Informatik, Bonn
2004


Editors

Ulrich Flegel, Michael Meier (eds.)


Copyright © GI, Gesellschaft für Informatik, Bonn

Contents

Intrusion detection in unlabeled data with quarter-sphere support vector machines

Pavel Laskov , Schäfer Christin and Igor Kotenko

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.


Full Text: PDF

GI, Gesellschaft für Informatik, Bonn
ISBN 3-88579-375-X


Last changed 24.01.2012 21:45:57