Proceedings of the IEEK Conference (대한전자공학회:학술대회논문집)
- 2002.07b
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- Pages.928-931
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- 2002
Hybrid Neural Networks for Intrusion Detection System
- Jirapummin, Chaivat (Department of Computer Engineering, Faculty of Engineering, King Mongkut′s University of Technology Thonburi) ;
- Kanthamanon, Prasert (School of Information Technology King Mongkut′s University of Technology Thonburi)
- Published : 2002.07.01
Abstract
Network based intrusion detection system is a computer network security tool. In this paper, we present an intrusion detection system based on Self-Organizing Maps (SOM) and Resilient Propagation Neural Network (RPROP) for visualizing and classifying intrusion and normal patterns. We introduce a cluster matching equation for finding principal associated components in component planes. We apply data from The Third International Knowledge Discovery and Data Mining Tools Competition (KDD cup'99) for training and testing our prototype. From our experimental results with different network data, our scheme archives more than 90 percent detection rate, and less than 5 percent false alarm rate in one SYN flooding and two port scanning attack types.
Keywords
- Intrusion Detection System (IDS);
- Network Security;
- Self Organizing Maps (SOM);
- Visualization;
- Cluster Matching;
- Resilient Propagation Neural Network (RPROP)