Authors: Kotenko, Igor
Laskov, Pavel
Schäfer, Christin
Title: Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines
Language (ISO): en
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.
Subject Headings: Intrusion Detection
Support Vector Machines
Issue Date: 2004-07
Provenance: Gesellschaft für Informatik
Citation: Pavel Laskov, Christin Schäfer, Igor Kotenko: Intrusion detection in unlabeled data with quarter-sphere Support Vector Machines. In Flegel, U.; Meier, M. (Eds.): Proc. of the International GI Workshop on Detection of Intrusions and Malware & Vulnerability Assessment, number P-46 in Lecture Notes in Informatics, pp. 71-82, Dortmund, Germany, July 2004, Köllen Verlag; ISBN 3-88579-365-X.
Is part of: DIMVA 2004, July 6-7, Dortmund, Germany
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