一种基于分层置信规则库的云制造系统网络安全态势预测模型  

A Hierarchical Confidence Rule Base Based Cybersecurity Posture Prediction Model for Cloud Manufacturing System

作　　者：李赞庆 李世明[1] 王禹贺 

机构地区：[1]哈尔滨师范大学计算机科学与信息工程学院,黑龙江 哈尔滨

出　　处：《人工智能与机器人研究》2024年第4期837-849,共13页Artificial Intelligence and Robotics Research

摘　　要：云制造系统(Cloud Manufacturing System, CMS)的网络安全对当今制造业影响重大,因此对于云制造系统进行网络安全态势预测(Network Security Situation Prediction, NSSP)变得十分重要。本文提出了一种基于证据推理(Evidential Reasoning, ER)和分层置信规则库(Hierarchical Belief Rule Base, HBRB)的云制造系统网络安全态势预测模型。首先,分析了影响CMS网络安全状况的因素,建立了评估框架,并采用ER算法进行融合,推导出CMS的安全态势值。其次,构建了基于HBRB的云制造系统网络安全态势预测模型,避免了属性过多引起的组合爆炸问题。此外,还使用了一种鲸鱼优化算法(Whale Optimization Algorithm, WOA),用于优化预测模型中的参数。该模型能够充分利用不确定信息与半定量信息,解决了专家知识的不完备性,提高了该预测模型的准确率。The cyber security of Cloud Manufacturing System (CMS) has a significant impact on today’s manufacturing industry, so it becomes important to perform Network Security Situation Prediction (NSSP) for CMS. In this paper, we propose a network security situation prediction model for CMS based on Evidential Reasoning (ER) and Hierarchical Belief Rule Base (HBRB). First, the factors affecting the cybersecurity status of CMS are analyzed, an assessment framework is established, and the ER algorithm is fused to derive the security posture value of CMS. Second, a cybersecurity posture prediction model for cloud manufacturing systems based on HBRB was constructed to avoid the combinatorial explosion problem caused by too many attributes. In addition, a Whale Optimization Algorithm (WOA) is used to optimize the parameters in the prediction model. The model can make full use of uncertain and semi-quantitative information, which solves the incompleteness of expert knowledge and improves the accuracy of this prediction model.

关 键 词：云制造系统 网络安全态势预测 证据推理 分层置信规则库 鲸鱼优化算法 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]