通信学报2024，Vol.45Issue(6)：87-100,14.DOI:10.11959/j.issn.1000-436x.2024091
基于数字孪生的工业互联网安全检测与响应研究
Research on industrial Internet security detection and response based on digital twin
摘要
Abstract
Considering that traditional network security defense methods cannot meet the strict requirements of industrial Internet for reliability and stability,a method for anomaly detection and response in digital space was studied based on the idea of digital twins by collecting on-site data and using twin model security cognition.Firstly,four types of model-ing methods were summarized and integrated into the multi module digital twin(DT)architecture by analyzing the digi-tal twin modeling solutions.Secondly,the cognition of different twin models was transformed into a standard signal tem-poral logic(STL)specification set by introducing signal temporal logic technology,and anomaly detection was achieved by monitoring system behavior based on the specification set,by the reliability of detection results was increased.Fi-nally,anomaly localization was achieved through the analysis of violations of the STL specification set,and correspond-ing STL weak specifications were designed through the analysis of known device faults to achieve anomaly classifica-tion.Two aspects of response to anomalies were beneficial for helping the system restore normal operation.The case study demonstrates that the effectiveness of the proposed method in anomaly detection and response.Comparing the pro-posed method with the intrusion detection system based on deep learning,the experimental results show that the detec-tion rate of the proposed method increases by 25%～40.9%in detecting anomalies.关键词
工业互联网/数字孪生/异常检测/异常响应/信号时序逻辑Key words
industrial Internet/digital twin/anomaly detection/anomaly response/signal temporal logic分类
信息技术与安全科学引用本文复制引用
马佳利,郭渊博,方晨,陈庆礼,张琦..基于数字孪生的工业互联网安全检测与响应研究[J].通信学报,2024,45(6):87-100,14.基金项目
国家自然科学基金资助项目(No.62276091) （No.62276091）
国家社科基金资助项目(No.2022-SKJJ-B-057) （No.2022-SKJJ-B-057）
河南省重大公益专项基金资助项目(No.201300311200)The National Natural Science Foundation of China(No.62276091),The National Social Science Fund of China(No.2022-SKJJ-B-057),The Major Public Welfare Project of Henan Province(No.201300311200) （No.201300311200）
