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Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines

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Published/Copyright: December 14, 2007
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From the journal Volume 27 Issue 4

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.

Published Online: 2007-12-14
Published in Print: 2004-December

© Copyright by K.G. Saur Verlag 2004

Articles in the same Issue

  1. Reactive Security – Intrusion Detection, Honeypots, and Vulnerability Assessment
  2. Honeynet Operation within the German Research Network – A Case Study
  3. Vulnerability Assessment using Honeypots
  4. A Network of IDS Sensors for Attack Statistics
  5. Foundations for Intrusion Prevention
  6. Using Alert Verification to Identify Successful Intrusion Attempts
  7. Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines
  8. Trust-Based Monitoring of Component-Structured Software
  9. Linux Diskless Clients – Festplattenlose Systeme für den Kursraumbetrieb
  10. Alois Potton hat das Wort
https://doi.org/10.1515/PIKO.2004.228

Articles in the same Issue

  1. Reactive Security – Intrusion Detection, Honeypots, and Vulnerability Assessment
  2. Honeynet Operation within the German Research Network – A Case Study
  3. Vulnerability Assessment using Honeypots
  4. A Network of IDS Sensors for Attack Statistics
  5. Foundations for Intrusion Prevention
  6. Using Alert Verification to Identify Successful Intrusion Attempts
  7. Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines
  8. Trust-Based Monitoring of Component-Structured Software
  9. Linux Diskless Clients – Festplattenlose Systeme für den Kursraumbetrieb
  10. Alois Potton hat das Wort
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