Sensor fusion and automatic vulnerability analysis
WISICT '05 Proceedings of the 4th international symposium on Information and communication technologies
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Improvement in intrusion detection with advances in sensor fusion
IEEE Transactions on Information Forensics and Security
SAID: a self-adaptive intrusion detection system in wireless sensor networks
WISA'06 Proceedings of the 7th international conference on Information security applications: PartI
Intrusion detection for mobile devices using the knowledge-based, temporal abstraction method
Journal of Systems and Software
Computational intelligence for network intrusion detection: recent contributions
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
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Most modern intrusion detection systems employ multiple intrusion sensors to maximize theirtrustworthiness. The overall security view of the multi-sensor intrusion detection system can serve as an aid to appraise the trustworthiness in the system. This paper presents our research effort in that direction by describing a Decision Engine for an Intelligent Intrusion Detection System (IIDS) that fuses information from different intrusion detection sensors using an artificial intelligence technique. The Decision Engine uses Fuzzy Cognitive Maps (FCMs) and fuzzy rule-bases for causal knowledge acquisition and to support the causal knowledge reasoning process. In this paper, we report on the workings of the Decision Engine that has been successfully embedded into the IIDS architecture being built at the Center for Computer Security Research (CCSR), Mississippi State University.