Vulnerability assessment method of network topology structure based on maximum likelihood function

被引：0

作者：

Li Q. ^{[1
]}

机构：

[1] Pingdingshan Industrial College of Technology, Pingdingshan

来源：

International Journal of Reasoning-based Intelligent Systems | 2023年 / 15卷 / 3-4期

关键词：

Constraints; Maximum likelihood function; Network topology; Normalisation; Vulnerability assessment;

D O I：

10.1504/IJRIS.2023.136358

中图分类号：

学科分类号：

摘要：

In order to improve the accuracy of network vulnerability assessment results, and reduce the node loss rate, a network topology vulnerability assessment method based on maximum likelihood function is proposed. The network characteristics and network topology are analysed. The reliability of the network topology is estimated by using the maximum likelihood function according to the analysis results. An assessment index set is constructed according to the reliability assessment results, and each assessment index in the assessment index set is standardised. At the same time, a fuzzy assessment matrix and a judgement scale matrix of network topology vulnerability are constructed, and the weight of each assessment index is calculated. A network topology vulnerability assessment model is established to achieve vulnerability assessment. The experimental results show that the minimum assessment time of the proposed method is 1.93 s, the node loss rate is always lower than 2%, and the assessment accuracy is high. © 2023 Inderscience Publishers. All rights reserved.

引用

页码：243 / 248

页数：5

共 15 条

[1]

Bai W., Pan Z., Guo S., Chen Z., Xia S., MDC-checker: A novel network risk assessment framework for multiple domain configurations, Computers & Security, 86, 9, pp. 388-401, (2019)

[2]

Deng C., Tang J.Y., Tan Q.T., Reliability evaluation model of two-Terminal network system under correlated failure, Statistics and Decision, 35, 1, pp. 5-9, (2019)

[3]

Liu S.C., Wang S.J., Reliability evaluation model of topological structure of independent distributed network based on maximum likelihood function, Bulletin of Science and Technology, 36, 3, pp. 80-83, (2020)

[4]

Ma Q.F., Wang T.J., Shan L., Niu L.J., Xu L., Wang L., Reliability evaluation model of electric power communication network based on business link, Mathematics in Practice and Theory, 49, 19, pp. 207-215, (2019)

[5]

Neri A., Carli M., Battisti F., A maximum likelihood approach for depth field estimation based on epipolar plane images, IEEE Transactions on Image Processing, 28, 2, pp. 827-840, (2019)

[6]

Rui L., Design of reliability evaluation model based on multifactor analysis for wireless sensor network, Modern Electronics Technique, 42, 12, pp. 156-160, (2019)

[7]

Sharafaldin I., Lashkari A.H., Ghorbani A.A., An evaluation framework for network security visualizations, Computers & Security, 84, 7, pp. 70-92, (2019)

[8]

Song Y., Yang N.D., Zhang Y.L., Feng L.J., Vulnerability evaluation of R&D network based on cloud model, Science and Technology Management Research, 39, 6, pp. 49-54, (2019)

[9]

Wang Y.S., Zhang Y.M., Wang C., A method of vulnerability assessment for power communication network based on mechanical model, Telecommunications Science, 35, 1, pp. 54-61, (2019)

[10]

Wang Y.S., Zhang Y.M., Wang C., Qi W.B., Power grid vulnerability assessment based on RBF neural network and its trend estimation, Electrical Measurement & Instrumentation, 56, 9, pp. 49-55, (2019)

← 1 2 →