| Publisher | World Scientific Publishing | ||
|---|---|---|---|
| Format | 239.5KB PDF | Date added | 23 Feb 2005 |
| Topics | Network Security, Security Tools, Intrusion Detection Systems | ||
| Downloads | 38 | ||
Off late data mining methods have gained importance in addressing network security issues, including network intrusion detection - a challenging task in network security. Intrusion detection systems aim to identify attacks with a high detection rate and a low false alarm rate. Classification-based data mining models for intrusion detection are of-ten ineffective in dealing with dynamic changes in intrusion patterns and characteristics. Consequently, unsupervised learning methods have been given a closer look for network intrusion detection. The paper investigates multiple centroid-based unsupervised clustering algorithms for intrusion detection, and proposes a simple yet effective self-labeling heuristic for detecting attack and normal clusters of network traffic audit data.
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