Bayesian network parameter learning based on fuzzy constraints

被引：2

作者：

Ru X. ^{[1
]}

Gao X. ^{[1
]}

Wang Y. ^{[1
]}

机构：

[1] School of Electronic Information, Northwestern Polytechnical University, Xi'an

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 02期

关键词：

Bayesian network; membership function; network security assessment; parameter learning;

D O I：

10.12305/j.issn.1001-506X.2023.02.15

中图分类号：

学科分类号：

摘要：

To address the problem of Bayesian networks parameter learning under small datasets, a fuzzy maximum posteriori estimation method is proposed, introducing fuzzy theory into parameter learning. The hyperparameter is determined by using the membership function to measure constraint effectiveness to improve the accuracy of constraint usage for learning. Experiments prove that the proposed method can effectively improve the accuracy of parameter learning. In addition, the proposed parameter learning method is applied to a network security assessment by using common vulnerability scoring system as expert priori parameters and combining vulnerability transfer samples to perform parameter learning. Finally, the node and path security evaluation verifies the effectiveness of the proposed algorithm. © 2023 Chinese Institute of Electronics. All rights reserved.

引用

页码：444 / 452

页数：8

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