Research On Network Security Situation Assessment And Forecasting Technology

  • Hongbin Wang College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China
  • Dongmei Zhao College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China, Hebei Key Laboratory of Network and Information Security, Shijiazhuang, China
  • Xixi Li Hebei Key Laboratory of Network and Information Security, Shijiazhuang, China
Keywords: network security situation, particle swarm optimization, D-S evidence theory, RBF neural network

Abstract

In recent years, the network security issues have become more prominent, and traditional network security protection technologies have been unable to meet the needs. To solve this problem, this paper improves and optimizes the existing methods, and proposed a set of network security situation assessment and prediction methods. First, the cross-layer particle swarm optimization with adaptive mutation (AMCPSO) algorithm proposed in this paper is combined with the traditional D-S evidence theory to evaluate the current network security situation; Then, the parameters and structure of traditional RBF neural network are optimized by introducing FCM (fuzzy c-means), HHGA (hybrid hierarchy genetic algorithm) and least square method. According to the optimized RBF neural network and situation assessment results, the next stage of network security situation is predicted. Finally, the effectiveness of the network security situation assessment and prediction method proposed in this paper is verified by simulation experiments. The algorithm in this paper improves the accuracy of situation assessment and prediction, and has certain reference significance for the research of network security.

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Author Biographies

Hongbin Wang, College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China

Hongbin Wang, studying in Computer Science and Technology, College of Computer and Cyber Security, Hebei Normal University. His research interests is network security.

Dongmei Zhao, College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China, Hebei Key Laboratory of Network and Information Security, Shijiazhuang, China

Dongmei Zhao, Doctor of Engineering(Master of network Security), Professor. Graduated from the Xidian University in 2007.Worked in Hebei normal university. Her research interests include network security situation estimation and prediction.

Xixi Li, Hebei Key Laboratory of Network and Information Security, Shijiazhuang, China

Xixi Li, master of applied software technology, graduated from Hebei Normal University in 2018. Her research interests include network security situation estimation and prediction.

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Published
2020-11-01
Section
Advanced Practice in Web Engineering