Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
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
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Intrusion detection based on dynamic self-organizing map neural network clustering
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
An Enhanced Swarm Intelligence Clustering-Based RBF Neural Network Detection Classifier
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
An Enhanced Swarm Intelligence Clustering-Based RBF Neural Network Web Text Classifier
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Frontiers of Computer Science in China
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An approach to network anomaly detection is investigated, based on dynamic self-organizing maps (DSOM) and ant colony optimization (ACO) clustering. The basic idea of the method is to produce the cluster by DSOM and ACO. 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 and ACO clustering can settle these problems effectively. The experiment results show that our approach can detect unknown intrusions efficiently in the real network connections.