Network security risk assessment model based on fuzzy theory
Bo Yi, Yuan Ping Cao, Ying Song
Abstract
With the rapid development of information science and technology, network security has occupied a very important position in people’s lives. Since the network security situation problem does not form a unified optimal solution in the model and algorithm, it is still necessary for researchers to continue to explore. In order to better evaluate the network security risk, based on fuzzy theory, particle swarm optimization and RBF neural network, this paper proposes a network security risk assessment model based on fuzzy theory. By mining the rules in the historical data of the network security situation and combining with the current network status, the assessment of the current network security situation is realized, and the objectivity and comprehensibility of the evaluation results are improved. The experimental comparison shows that the fuzzy theory prediction model with PSO-RBF neural network has more rapid and effective evaluation and prediction results than the fuzzy theory prediction model with RBF neural network only.