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Real-time intrusion detection system based on Self-Organized maps and feature correlations

Title
Real-time intrusion detection system based on Self-Organized maps and feature correlations
Authors
Oh H.Chae K.
Ewha Authors
채기준
SCOPUS Author ID
채기준scopus
Issue Date
2008
Journal Title
Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008
Citation
Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008 vol. 2, pp. 1154 - 1158
Indexed
SCOPUS scopus
Document Type
Conference Paper
Abstract
Detecting network intrusion has been not only critical but also difficult in the network security research area. Traditional supervised learning techniques are not appropriate to detect anomalous behaviors and new attacks because of temporal changes in network intrusion patterns and characteristics. Therefore, unsupervised learning techniques such as SOM (Self-Organizing Map) are more appropriate for anomaly detection. In this paper, we propose a real-time intrusion detection system based on SOM that groups similar data and visualize their clusters. Our system labels the map produced by SOM using correlations between features. We experiments our system with KDD Cup 1999 data set. Our system yields the reasonable misclassification rates and takes 0.5 seconds to decide whether a behavior is normal or attack. © 2008 IEEE.
DOI
10.1109/ICCIT.2008.362
ISBN
9780769534077
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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