Self-organizing maps
Improving intrusion detection performance using keyword selection and neural networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
SAINT '03 Proceedings of the 2003 Symposium on Applications and the Internet
Applying CMAC-Based On-Line Learning to Intrusion Detection
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Network Intrusion Detection Using an Improved Competitive Learning Neural Network
CNSR '04 Proceedings of the Second Annual Conference on Communication Networks and Services Research
A study in using neural networks for anomaly and misuse detection
SSYM'99 Proceedings of the 8th conference on USENIX Security Symposium - Volume 8
Training a neural-network based intrusion detector to recognize novel attacks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Intrusion Detection is an essential and critical component of network security systems. The key ideas are to discover useful patterns or features that describe user behavior on a system, and use the set of relevant features to build classifiers that can recognize anomalies and known intrusions, hopefully in real time. In this paper, a hybrid neural network technique is proposed, which consists of the self-organizing map (SOM) and the radial basis function (RBF) network, aiming at optimizing the performance of the recognition and classification of novel attacks for intrusion detection. The optimal network architecture of the RBF network is determined automatically by the improved SOM algorithm. The intrusion feature vectors are extracted from a benchmark dataset (the KDD-99) designed by DARPA. The experimental results demonstrate that the proposed approach performance especially in terms of both efficient and accuracy.