Security analysis of information in campus network based on improved back-propagation neural network

被引：0

作者：

Zhao X.H. ^{[1
]}

机构：

[1] Zhuhai City Polytechnic, Jiner Road, Xihu Urban Community, Jinwan District, Guangdong, Zhuhai

来源：

Telecommunications and Radio Engineering (English translation of Elektrosvyaz and Radiotekhnika) | 2021年 / 80卷 / 02期

关键词：

Campus network; Network security; Neural network; Particle swarm optimization;

D O I：

10.1615/TelecomRadEng.2021036674

中图分类号：

学科分类号：

摘要：

This study focuses mainly on the role of back-propagation neural network (BPNN) in the analysis of campus network information security. First, the BPNN algorithm was analyzed. Then, in order to improve the performance of the algorithm in security analysis, it was improved by the particle swarm optimization (PSO) algorithm by optimizing the initial weight of the BPNN. Taking some safety logs from the campus network of Zhuhai City Polytechnic in October 2019 as experimental data, the error and classification accuracy of the BPNN and improved BPNN were compared. The results showed that the error of the improved BPNN was obviously smaller than that of BPNN, and the false alarm rate and missing report rate were also lower, indicating that the algorithm had good reliability. From the perspective of accuracy, the improved BPNN had excellent classification in big data samples. It could achieve 92.57% accuracy under 25,000 data volume, while the accuracy of the original BPNN was only 78.36%. The experimental results prove that the improved BPNN has good classification performance and can accurately identify attack logs in a campus network, suggesting that the improved BPNN was effective in information security analysis and is conducive to improving the security of a campus network. It is worth further promotion and application. © 2021 Begell House Inc.. All rights reserved.

引用

页码：35 / 46

页数：11

共 25 条

[1]

Abualigah L.M., Khader A.T., Unsupervised Text Feature Selection Technique Based on Hybrid Particle Swarm Optimization Algorithm with Genetic Operators for the Text Clustering, J. Supercomput, 73, 11, pp. 1-23, (2017)

[2]

Chang Y., Jung C., Single Image Reflection Removal Using Convolutional Neural Networks, IEEE Trans. Image Process, 28, 4, pp. 1954-1966, (2019)

[3]

Chen Y., Yao W., Luo J., Research on the Active Defense Security System Based on Cloud Computing of Wisdom Campus Network, Chinese Control and Decision Conference, pp. 1292-1297, (2016)

[4]

Fahad H.M., Khan M.U.G., Saba T., Rehman A., Iqbal S., Microscopic Abnormality Classification of Cardiac Murmurs Using ANFIS and HMM, Microsc. Res. Techniq, 81, 5, pp. 449-457, (2018)

[5]

Hoz E.D.L., Hoz E.D.L., Ortiz A., Ortega J., Prieto B., PCA Filtering and Probabilistic SOM for Network Intrusion Detection, Neurocomputing, 164, 21, pp. 71-81, (2015)

[6]

Huang M., Luo W., Wan X., Research on Network Security of Campus Network, J. Phys. Conf. Ser, 1187, 4, (2019)

[7]

Jing X.L., Research and Practice on the Construction of Campus Network Security, Appl. Mech. Mater, 738-739, (2015)

[8]

Jing X.L., Research and Practice on the Construction of Campus Network Security, Appl. Mech. Mater, 738-739, pp. 1189-1192, (2015)

[9]

Keegan N., Ji S.Y., Chaudhary A., Concolato C., A Survey of Cloud-based Network Intrusion Detection Analysis, Human-Centric Comput. Info. Sci, 6, 1, (2016)

[10]

Ke G., Hong Y.H., The Research of Network Intrusion Detection Technology Based on Genetic Algorithm and BP Neural Network, Appl. Mech. Mater, 599-601, pp. 726-730, (2014)

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