Study on the Application of Neural Network in the Computer Network Security Evaluation
Xue-rui WANG, Yan ZHOU Henan Institute of Engineering, Zhengzhou, Zip Code: 450007, China
Abstract: Research computer network security problem. There were nonlinear relations among the evaluation indexes, and it was difficult for an accurate mathematical model to describe the nonlinear relationship. In order to improve the evaluation accuracy of computer network security, this study put forward a combination model to evaluate the computer network security. The combination model used particle swarm optimization (PSO) to optimize the parameters of BP neural network, speed up the BP neural network’s convergence speed, and enhanced its global optimization ability, which effectively improved the accuracy of the evaluation model. Simulation results showed that compared with traditional BP neural network model, the combined model’s learning ability was faster and global search ability was stronger, which effectively improved the evaluation accuracy of computer network security.
Keywords: Neural Network, Application, Computer Network Security Evaluation, PSO Study on the Application of Neural Network in the Computer Network Security Evaluation
References: [1] Apostolopoulos, George., Gudrin, Roth., Kamat, Sanjay., Satish, K.,
Tripathi. (1998). Quality of service based routing: A performance
perspective. 15-27.
[2] TANG, Chen-hua., YAO, Shui-ping., CUI, Zhong-jie. (2002). A network
security policy model and its realization mechanism.
Beilin:Springer-Verlag, 2002. 663-871.
[3] GUO, Li-juan. (2012). Neural network in the application of computer
network security evaluation. Value Engineering.
[4] Brown, Bill., Cutts, Andrew., McGrath, Dennis. (2003). Simulation
of cyber attacks with applications in homeland defense training. San
Jose:SPIE Press, 63-70.
[5] MAO, Zhi-yong. (2008). Evaluation on BP neural network in the
application of computer network security, Journal of Information
Technology, 2008 (6).
[6] Michael, Liljenstam., Jason, Liu., David, Nicol. (2005). RINSE: The
real-time immersive network simulation environment for network security
exercises. San Diego, CA: Simulation Councils, 2005. 118-127.
[7] WU, Ren-jie. (2011). Security evaluation on neural network in the
application of computer network. The Computer Simulation, 2011 (11).
[8] Williams, L., Lippmann, R., Ingols, K. W. (2008). Garnet: A
graphical attack graph and reachability network evaluation tool.
[9] Jin, J., Xu, H., LI B-C. (2011). Multicast scheduling with
cooperation and network coding in cognitive radio networks. San Diego,
California, USA, 2011. 1-9.
