A Novel Method to Prevent Misconfigurations of Industrial Automation and Control Systems

Yu Zhang, Yani Ge, Peiran Yu, Jianzhong Zhang, Yongzheng Zhang, Thar Baker

Research output: Contribution to journalArticlepeer-review

Abstract

Configuration errors are among the dominant causes of system faults for the industrial automation and control systems (IACS). It is difficult to detect and correct such errors of IACS as there are various kinds of systems and devices with miscellaneous configuration specifications. In this article, we first propose a streaming algorithm to keep all the configuration changes in the limited memory space. When making a new configuration change, another novel streaming algorithm is proposed to search and return all the similar historical changes, which can be used to validate this new one. So far, we are the first to model the configuration changes of IACS as a data stream and apply the streaming similarity search in correcting configuration errors while overcoming the inherent unbounded-memory bottleneck. The theoretical correctness and complexity analyses are presented. Experiments with real and synthetic datasets confirm the theoretical analyses and demonstrate the effectiveness of the proposed method in preventing misconfigurations of IACS.
Original languageEnglish
Pages (from-to)4210 - 4218
Number of pages8
JournalIEEE Transactions on Industrial Informatics
Volume17
Issue number6
DOIs
Publication statusPublished - 19 Aug 2020

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