DocumentCode :
2316710
Title :
Applying CMAC-based online learning to intrusion detection
Author :
Cannady, James
Author_Institution :
Nova Southeastern Univ., Fort Lauderdale, FL, USA
Volume :
5
fYear :
2000
fDate :
2000
Firstpage :
405
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
Keywords :
cerebellar model arithmetic computers; learning (artificial intelligence); security of data; CMAC-based online learning; adaptive neural networks; computer intrusions; intrusion detection; modified reinforcement learning method; network system intrusions; Computer crime; Computer network reliability; Computer networks; Expert systems; Floods; Information security; Intrusion detection; Monitoring; Neural networks; Protection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
ISSN :
1098-7576
Print_ISBN :
0-7695-0619-4
Type :
conf
DOI :
10.1109/IJCNN.2000.861503
Filename :
861503
Link To Document :
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