PSO-BPNN-Based Prediction of Network Security Situation
Dalian, Liaoning（2008）
摘要
Under the application background of network security evaluation research, this paper proposes a method of situation prediction based on Particle Swarm Optimization (PSO) for optimizing BP Neural Network (BPNN). It uses PSO to reach global optimization of BP network's weight value and threshold value, and then by means of the optimized BP network builds a prediction model to predict the future network security situation. Experiment results show that this method can overcome the shortage of the predicting application in the traditional BP network, and effectively improve the accuracy of situation prediction. It can be applied into the situation prediction of network security situation awareness.
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关键词
network security situation,optimizing bp neural network,network security situation awareness,traditional bp network,network security evaluation research,application background,prediction model,situation prediction,pso-bpnn-based prediction,optimized bp network,future network security situation,bp network,application software,computational modeling,network security,computer science,information security,situation awareness,predictive models,artificial neural networks,global optimization,data security,mathematics,particle swarm optimization,security,intrusion detection,neural network,backpropagation,computer security,accuracy,neural nets
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