Network security situation assessment model based on information quality control

被引：0

作者：

Ren J. ^{[1
]}

机构：

[1] College of Mechanical and Electrical Engineering, Zhengzhou Business University, Gongyi

来源：

Ren, Junjun (renjunjun9855@163.com) | 1600年 / Totem Publishers Ltd卷 / 16期

关键词：

Assessment; Communication; Network; Security situation; Wireless optical waveguide;

D O I：

10.23940/ijpe.20.04.p19.673680

中图分类号：

学科分类号：

摘要：

The network security situation assessment of wireless optical waveguide communication networks is non-stationary. In order to improve the evaluation accuracy and ensure the security of these networks, a network security situation assessment algorithm of wireless optical waveguide communication networks based on information quality control is proposed. The intrusion information transmission channel model is constructed, and the fuzzy correlation characteristics of simulating the security situation attribute are extracted. The distributed transmission channel equalization method is used to design the transmission channel equilibrium, and the security situation is evaluated in the equalization channel model. The network security situation assessment parameters of the detected wireless optical waveguide communication network virus attack information are estimated jointly, and the security situation assessment is realized according to the parameter estimation results. The simulation results show that the method has good accuracy and anti-interference ability, and it improves the ability of network security situation assessment. © 2020 Totem Publisher, Inc. All rights reserved.

引用

页码：673 / 680

页数：7

共 14 条

[1]

Hsieh T.J., A bacterial gene recombination algorithm for solving constrained optimization problems, Applied Mathematics and Computation, 231, 15, pp. 187-204, (2014)

[2]

Long M.S., Wang J.M., Ding G.G., Pan S.J., Yu P.S., Adaptation regularization: A general framework for transfer learning, IEEE Transactions on Knowledge and Data Engineering, 26, 5, pp. 1076-1089, (2014)

[3]

Patricia N., Caputo B., Learning to learn, from transfer learning to domain adaptation: A unifying perspective, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1442-1449, (2014)

[4]

Sun L., Guo C.H., Incremental affinity propagation Clustering based on message passing, IEEE Transactions on Knowledge and Data Engineering, 26, 11, pp. 2731-2744, (2014)

[5]

Liang J., Qu B.Y., Mao X.B., Chen T.J., Differential evolution based on fitness euclidean-Distance Ratio for Multimodal Optimization, Neurocomputing, 137, 8, pp. 252-260, (2014)

[6]

Mernik M., Liu S.H., Karaboga D., Repinsek M.C., On clarifying misconceptions when comparing variants of the artificial bee colony algorithm by offering a new implementation, Information Sciences, 291, 10, pp. 115-127, (2015)

[7]

Zhang W., Cao X.B., Yin H.Z., Chat room social network mining based on Multi-features fusion, Journal of University of Science and Technology of China, 39, 5, pp. 540-546, (2009)

[8]

Tian Q.H., Huang C., Yu H.D., Du Y.X., Zhou X.M., Approach for determining weight of resource allocation in Coupled Task Set based on AHP, Computer Engineering and Application, 54, 21, pp. 25-30, (2018)

[9]

Lin Y., Zhu X.L., Zheng Z.G., Dou Z., Zhou R.L., The individual identification Method of wireless device based on Dimensionality Reduction and Machine Learning, Journal of Supercomputing, 5, pp. 1-18, (2017)

[10]

Tu Y., Lin Y., Wang J., Kim J.-U., Semi-supervised learning with generative adversarial networks on digital signal modulation classification, CMC-Computers Materials and Continua, 55, 2, pp. 243-254, (2018)

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