A Hybrid Intelligent Optimization Algorithm to Assess the NSS Based on FNN Trained by HPS

Abstract

Along with the widespread application of computer network, the network security problem becomes more and more important, and the security problem research also becomes an important topic to the network development. Because the different networks request the different security level, therefore, it is helpful to understand the network security for the customers to assess the network security situation (NSS) scientifically, thus takes the corresponding measure, enhances the network security performance and the overall economic benefit. This paper proposed a novel hybrid intelligent optimization algorithm to assess the network security situation, the algorithm unified the fuzzy neural network (FNN) and hybrid particle swarm (HPS) optimization algorithm, which not only can centralize the FNN’s advantages of learning, association, identification, adaptation and fuzzy information processing, and improve the learning and expression ability, but also can balance the search capability between the global account and partial account using HPS, and have the faster velocity of convergence. The NSS situation of 20 samples in Hebei province shows that the results given by this hybrid model are reliable, and this method to assessment the NSS is feasible.