Research On Network Security Situation Assessment And Forecasting Technology
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.
Kodagoda, Neesha(2014,). Concern level assessment: Building domain knowledge into a visual system to support network-security situation awareness. Information Visualization, 13(4), 346-360.
Tim Bass(2000,). Intrusion Detection Systems and Multisensor Data Fusion: Creating Cyberspace Situational Awareness. Communications of the Association for Computing Machinery. 43(4), 99-99.
LAU S(2004,). The spinning cube of potential doom. Communication of the ACM, 47(6), 25-26.
Maleh Y, Ezzati A(2015,). Lightweight Intrusion Detection Scheme for Wireless Sensor Networks. IAENG International Journal of Computer Science, 42(4), 347-354.
Zhu L N, Xia G N, et al(2016,). Multi-dimensional Network Security Situation Assessment. International Journal of Security & Its Applications, 10(11), 153-164.
Moosavi H, Bui F M(2017,). A Game-Theoretic Framework for Robust Optimal Intrusion Detection in Wireless Sensor Networks. IEEE Transactions on Information Forensics and Security, 9(9), 1367-1379.
Shi Y Q, Li R F, Zhang Y, et al(2015,). An immunity-based time series prediction approach and its application for network security situation. Intelligent Service Robotics, 8(1), 1-22.
Chen S X, Yang Zh, Zhu J, et al(2015,). Network security situation prediction method based on PSO-SVM. Application Research of Computers, 32(6), 1778-1781.
Li F W, Zhang X Y, Zhu J, et al(2016,). Network security situation prediction based on APDE-RBF neural network. Systems Engineering and Electronics, 38(12), 2869-2875.
Beng L Y, Manickam S(2016,). A Novel Adaptive Grey Verhulst Model for Network Security Situation Prediction. International Journal of Advanced Computer Science & Applications, 7(1), 90-95.
Shi Y Q, Li R F, Peng XN, et al(2016,). Network Security Situation Prediction Approach Based on Clonal Selection and SCGM(1,1)c Model. Journal of Internet Technology, 17(3), 421-429.
Zhao G Z, Chen A G, Lu G X, et al(2020,). Data Fusion Algorithm Based on Fuzzy Sets and D-S Theory of Evidence. Tsinghua Science and Technology, (1),12-19.
Wang L,Dong C H,Hu J P,et al(2015,). Network Intrusion Detection Using Support Vector Machine Based on Particle Swarm Optimization. Plant Biotechnology Reports, 4(3), 237-242.
Yan X H(2015,). Mining Network Security Logs via Fuzzy Clustering Algorithm. Journal of Computational & Theoretical Nanoscience, 12(12), 6220-6226.
Li X, et al(1998,). On Simultaneous Approximation by Radial Basis Function Neural Networks. Applied Mathematics and Computation, 95(1), 75-89.
Han H G, Lu W, Hou Y, et al(2016,). An Adaptive-PSO-Based Self-Organizing RBF Neural Network. IEEE Transactions on Neural Networks and Learning Systems, (99), 1-14.
Zhu W X(2016,). Network Intrusion Prediction Model based on RBF Features Classification. International Journal of Security & Its Applications, 10(4), 241-248.
Ji W D, Sun L P, Wang K Q, et al(2016,). An Improved Particle Swarm Optimization Algorithm of Radial Basis Neural Network. International Journal of Control & Automation, 9(10), 413-420.
P Asokan, J Jerald, S Arunachalam, et al(2008,). Application of Adaptive Genetic Algorithm and Particle Swarm Optimisation in scheduling of jobs and AS/RS in FMS. International Journal of Manufacturing Research, 3(4), 393-405.
Wang H Z, Ruan J Q, Ma Z W, Zhou B, Fu X Q, et al(2019,). Deep learning aided interval state prediction for improving cyber security in energy internet. Energy, 174(174).
Pu Z Y(2020,). Network security situation analysis based on a dynamic Bayesian network and phase space reconstruction. Journal of supercomputing, 76(2), 1342-1357.
Shen H J , Wan W , Long C , et al(2019,). Security Situation Assessment Method Based on States Transition*. 2018 IEEE International Conference on Information and Automation (ICIA). IEEE.