Loading…
Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines
Laskov, Pavel; Schäfer, Christin; Kotenko, Igor; Müller, Klaus-Robert
The anomaly detection methods are receiving growing attention in the intrusion detection community. The two main reasons for this are their ability to handle large volumes of unlabeled data and to detect previously unknown attacks. In this contribution we investigate the application of a modern machine learning technique – one-class Support Vector Machines (SVM) – for anomaly detection in unlabeled data. We propose a novel formulation of this technique which is particularly suited for the data typical for intrusion detection systems. Our evaluation on the well-known KDDCup dataset demonstrates a significant improvement over previous formulations of the one-class SVM.
Published in: Praxis der Informationsverarbeitung und Kommunikation : PIK, 10.1515/PIKO.2004.228, De Gruyter
- Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
- This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.
