The Application of Fuzzy ARTMAP in the Detection of Computer Network Attacks
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Content-based methodology for anomaly detection on the web
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
Intrusion detection in computer networks with neural and fuzzy classifiers
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A hybrid neural network approach to the classification of novel attacks for intrusion detection
ISPA'05 Proceedings of the Third international conference on Parallel and Distributed Processing and Applications
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The timely and accurate detection of computer and network system intrusions has always been an elusive goal for system administrators and information security researchers. Existing intrusion detection approaches require either manual coding of new attacks in expert systems or the complete retraining of a neural network to improve analysis or learn new attacks. This paper presents a new approach to applying adaptive neural networks to intrusion detection that is capable of autonomously learning new attacks rapidly by a modified reinforcement learning method that uses feedback from the protected system.