WNN-Based Network Security Situation Quantitative Prediction Method and Its Optimization

The accurate and real-time prediction of networksecurity situation is the premise and basis of preventing intrusionsand attacks in a large-scale network. In order to predict the securitysituation more accurately, a quantitative prediction method of networksecurity situation based on Wavelet Neural Network with GeneticAlgorithm (GAWNN) is proposed. After analyzing the past and the currentnetwork security situation in detail, we build a network securitysituation prediction model based on wavelet neural network that isoptimized by the improved genetic algorithm and then adopt GAWNN topredict the non-linear time series of network security situation.Simulation experiments prove that the proposed method has advantagesover Wavelet Neural Network (WNN) method and Back Propagation NeuralNetwork (BPNN) method with the same architecture in convergence speed,functional approximation and prediction accuracy. What is more, systemsecurity tendency and laws by which security analyzers andadministrators can adjust security policies in near real-time arerevealed from the prediction results as early as possible.