D-SCIDS: distributed soft computing intrusion detection system
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
IAS '07 Proceedings of the Third International Symposium on Information Assurance and Security
HiNFRA: Hierarchical Neuro-Fuzzy Learning for Online Risk Assessment
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
Fuzzy Online Risk Assessment for Distributed Intrusion Prediction and Prevention Systems
UKSIM '08 Proceedings of the Tenth International Conference on Computer Modeling and Simulation
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Very often, risk assessment in security systems is often done by human experts, because there is no exact and mathematical solution to the problem. Usually the human reasoning and perception process cannot be expressed precisely. Different people have different opinions about risk and the association of its dependent variables. We first present the role of fuzzy inference methods to develop intelligent online risk assessment models. We further illustrate the optimization of fuzzy inference systems using neural learning and evolutionary learning for using such models in an online environment. All the developed models are used in an intrusion detection/prevention system for online risk assessment. Finally, we present genetic programming models that could combine both intrusion detection and risk assessment and easily deployed in a mobile environment.