A Model of Network Security Situation Assessment Based on BPNN Optimized by SAA-SSA

Ran Zhang, Zhihan Pan, Yifeng Yin, Zengyu Cai

OAI: oai:igi-global.com:302877 • DOI: 10.4018/IJDCF.302877

In order to address the problems that the accuracy and convergence of current network security situation assessment models need to be improved, a model of network security situation assessment based on SAA-SSA-BPNN is proposed. Using the characteristics of sparrow search algorithm (SSA) optimized by simulated annealing algorithm (SAA) with good stability, fast convergence speed and is not easy to fall into local optimum to improve the BP neural network (BPNN), so as to find the best fitness individual, and obtain the optimal weight and threshold, then assign them to the BP neural network as the initial values. The preprocessed index data is input into the improved BP neural network model for training, and finally the threat degree of the network system is assessed based on the trained model. Comparative experimental results show that this assessment model has higher accuracy and faster convergence than other situation assessment models based on improved BP neural network.