Network security situation prediction based on optimized BP neural network
Yunfeng Zhang, Cheng He, Han Wu · 2021 IEEE International Conference on Electronic Technology, Communication and Information (ICETCI) · 2021
With the rapid development of the network, the scale of the network has continued to expand, and network security issues have become increasingly prominent. Data from the National Internet Emergency Response Center show that my country's computer malicious programs, DDoS attacks, information security vulnerabilities, website implantation, and other threats have all been multiplied. The increasing trend. To respond to network security issues promptly and grasp the network security situation, predicting the network security situation has become important research in recent years. This paper proposes a network security situation prediction based on an optimized BP neural network. By analyzing the data, extracting the characteristics of security-related elements in the network, using the BP neural network to continuously adjust the weights and thresholds, the actual output value of the network is compared with the expected value. The error means the square error is the smallest, and the nonlinear mapping relationship of the network situation value is found. And through the simulated annealing algorithm (Simulated Annealing, SA) to optimize the BP neural network to avoid falling into the local minimum, to predict the network security situation. Simulation experiments verify the feasibility and effectiveness of the proposed method in network security situation prediction.