2023 Vol.6 Aug.N04

• Title: Application of BP NN algorithm in computer network security evaluation
• Name: JianFeng Li
• Company: Nanning Institute of Technology, NanNing 541002,China
• Abstract:

  Computer network has been widely used in various fields, which brings development opportunities to social development and production technology. With the progress of computer network technology, its application scope has been expanded, the circulation of information has been strengthened, and resource sharing has been realized. However, due to the openness of the network, there have been threats such as viruses and loopholes, which are unavoidable computer network security (CNS) problems. In view of the problems of many kinds of safety evaluation indexes and strong nonlinear characteristics of each index in modern computer network, the traditional evaluation method has the problems of difficult operation and low accuracy. A computer network safety evaluation algorithm based on adaptive BP neural network (NN) is proposed. The real-time sharing of information makes computer technology more valuable and gives full play to the advantages of rapid information transmission. This paper studies the application of NN in CNS evaluation, and summarizes the concepts of CNS and NN. Some commonly used tools and software are used to scan and evaluate their networks, and to meet the security control requirements of the current rapid development of computer networks through the security evaluation methods with strong operability, wide application scope and less human interference.

  
  

• Keyword: BP NN algorithm; Computer network; Safety evaluation
• DOI: 10.12250/jpciams2023090607
• Citation form: JianFeng Li.Application of BP NN algorithm in computer network security evaluation [J]. Computer Informatization and Mechanical System,2023,Vol.6,pp.31-34

[1] Sun W, Xu Y. Financial security evaluation of the electric power industry in China based on a back propagation neural network optimized by genetic algorithm. Energy, vol.10, no.15, pp.39, 2016.

[2] Zhuo L, Wang W, Wang C. On the Evolution and Impact of Mobile Botnets in Wireless Networks. IEEE Transactions on Mobile Computing, vol.15, no.9, pp.34, 2016.

[3] Zhang X, Yu T, Xu P, et al. An intelligent sustainability evaluation system of micro milling. Robotics and Computer-Integrated Manufacturing, vol.17, no.31, pp.39, 2022.

[4] Luis R S, Puttnam B J, Rademacher G, et al. Digital Back Propagation in Long-Haul, MIMO-Supported, Multicore Fiber Transmission. IEEE Photonics Technology Letters, vol.19, no.9, pp.11, 2020.

[5] Feng J, Sun Q, Li Z, et al. Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography. Journal of Biomedical Optics, vol.24, no.15, pp.14, 2019.

[6] Carter H, Mood B, Traynor P, et al. Secure outsourced garbled circuit evaluation for mobile devices. Journal of computer security, vol.24, np.12, pp.18, 2016.

[7] Fontaine J, Zheng K, Cosmas V, et al. Evaluation of a proximity card authentication system for health care settings. International Journal of Medical Informatics, vol.12, no.17, pp,14, 2016.

[8] Song H H. Testing and Evaluation System for Cloud Computing Information Security Products. Procedia Computer Science, vol.16, no.6, pp.47, 2020.

[9] Shah Z, Raja M, Chu Y M, et al. Computational intelligence of Levenberg-Marquardt backpropagation neural networks to study the dynamics of expanding/contracting cylinder for Cross magneto-nanofluid flow model. Physica Scripta, vol.19, no.15, pp.28, 2021.

[10] Zhang Q, Wu H, Peng Y, et al. Sign backpropagation: An on-chip learning algorithm for analog RRAM neuromorphic computing systems. Neural Networks, vol.10, no.18, pp.23, 2018.

[11] Samantaray S, Ghose D K. Evaluation of suspended sediment concentration using descent neural networks. Procedia Computer Science, vol.13, no.21, pp.24, 2018.