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Main Title: Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines
Author(s): Laskov, Pavel
Schäfer, Christin
Kotenko, Igor
Müller, Klaus-Robert
Type: Article
Language Code: en
Abstract: 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.
Issue Date: 2004
Date Available: 15-Oct-2018
DDC Class: 004 Datenverarbeitung; Informatik
Subject(s): machine learning
KDDCup dataset
Sponsor/Funder: BMBF, 01-SC40A, MIND
EC/FP6/506778/EU/Pattern analysis, statistical modelling and computational Learning/PASCAL
Journal Title: Praxis der Informationsverarbeitung und Kommunikation : PIK
Publisher: De Gruyter
Publisher Place: Berlin
Volume: 27
Issue: 4
Publisher DOI: 10.1515/PIKO.2004.228
Page Start: 228
Page End: 236
EISSN: 1865-8342
ISSN: 0930-5157
Notes: 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.
Appears in Collections:FG Maschinelles Lernen » Publications

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