Data mining: concepts and techniques
Data mining: concepts and techniques
Self-Organizing Maps
Using Artificial Anomalies to Detect Unknown and Known Network Intrusions
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Learning nonstationary models of normal network traffic for detecting novel attacks
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Network Anomaly Detection Based on DSOM and ACO Clustering
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
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An approach to network intrusion detection is investigated, based on dynamic self-organizing maps (DSOM) neural network clustering. The basic idea of the method is to produce the cluster by DSOM. With the classified data instances, anomaly data clusters can be easily identified by normal cluster ratio. And then the identified cluster can be used in real data detection. In the traditional clustering-based intrusion detection algorithms, clustering using a simple distance-based metric and detection based on the centers of clusters, which generally degrade detection accuracy and efficiency. Our approach based on DSOM clustering can settle these problems effectively. The experiment result shows that our approach can detect unknown intrusions efficiently in the real network connections.