Applying CMAC-based online learning to intrusion detection

Abstract

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. The paper presents an 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.