A blackboard-based learning intrusion detection system: a new approach

Authors:
Mayukh Dass;James Cannady;Walter D. Potter
Affiliations:
Artificial Intelligence Center, University of Georgia, Athens, Georgia;Artificial Intelligence Center, University of Georgia, Athens, Georgia;Artificial Intelligence Center, University of Georgia, Athens, Georgia
Venue:
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Year:
2003

Citing 3
Cited 1

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Adaptive Intrusion Detection: A Data Mining Approach

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Fast Learning Neural Network Intrusion Detection System

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Abstract

Intrusion Detection is one of the crucial real-time problems in the field of computer networking. With the changing technology and the exponential growth of Internet traffic, it is becoming difficult for any existing intrusion detection system to offer a reliable service. From earlier research, we have found that there exists a behavioral pattern in the attacks that can be learned. That is why an Artificial Neural Network is so successful in detecting network intrusions. Still, this approach is not effective in a dynamic environment where changes take place frequently. This paper proposes a blackboard-based Learning Intrusion Detection System, which is controlled by autonomous agents and has an online learning capability. This feature enables the system to adapt itself with the changing environment and to perform better than present systems.