Intrusion detection systems and multisensor data fusion
Communications of the ACM
The Design of VisFlowConnect-IP: A Link Analysis System for IP Security Situational Awareness
IWIA '05 Proceedings of the Third IEEE International Workshop on Information Assurance
Using attack graphs for correlating, hypothesizing, and predicting intrusion alerts
Computer Communications
A novel network intrusion attempts prediction model based on fuzzy neural network
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
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The accurate and real-time prediction of network security situation is the premise and basis of preventing intrusions and attacks in a large-scale network. In order to predict the security situation more accurately, a quantitative prediction method of network security situation based on Wavelet Neural Network with Genetic Algorithm (GAWNN) is proposed. After analyzing the past and the current network security situation in detail, we build a network security situation prediction model based on wavelet neural network that is optimized by the improved genetic algorithm and then adopt GAWNN to predict the non-linear time series of network security situation. Simulation experiments prove that the proposed method has advantages over Wavelet Neural Network (WNN) method and Back Propagation Neural Network (BPNN) method with the same architecture in convergence speed, functional approximation and prediction accuracy. What is more, system security tendency and laws by which security analyzers and administrators can adjust security policies in near real-time are revealed from the prediction results as early as possible.