Applying CMAC-Based On-Line Learning to Intrusion Detection
Como, Italy / 2000
Proceedings of the 2000 IEEE/INNS Joint International Conference on Neural Networks
ISSN or ISBN
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 lean 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 leaning method that uses feedback from the protected system.
Cannady, James D. Jr., "Applying CMAC-Based On-Line Learning to Intrusion Detection" (2000). CEC Faculty Articles. 460.