Geometry-Based Data-Driven Complete Stealthy Attacks Against Cyber-Physical Systems

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-09-12 DOI:10.1109/TNSE.2024.3458095

Kaiyu Wang;Dan Ye

{"title":"Geometry-Based Data-Driven Complete Stealthy Attacks Against Cyber-Physical Systems","authors":"Kaiyu Wang;Dan Ye","doi":"10.1109/TNSE.2024.3458095","DOIUrl":null,"url":null,"abstract":"This paper proposes a data-driven complete stealthy attack strategy against cyber-physical systems (CPSs) based on the geometric approach. The attacker aims to degrade estimation performance and maintain stealthiness by compromising partial communication links of the actuator and sensor. Different from the classic analysis methods that require accurate model parameters, we focus on how to establish the connection between geometry and data-driven approaches to represent the malicious behavior of attacks on state estimation. First of all, the existence of complete stealthy attacks is analyzed. Then, the maximal attached stealthy subspace and the set of estimation errors under complete stealthy attacks are analyzed intuitively from the geometric point of view. On this basis, the complete stealthy subspace is constructed with the subspace identification method, which is applied to generate the corresponding stealthy attack sequence through the collected system input-output data. Finally, simulation results are provided to illustrate the effectiveness of the proposed strategies.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"5839-5849"},"PeriodicalIF":6.7000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Network Science and Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10679707/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

This paper proposes a data-driven complete stealthy attack strategy against cyber-physical systems (CPSs) based on the geometric approach. The attacker aims to degrade estimation performance and maintain stealthiness by compromising partial communication links of the actuator and sensor. Different from the classic analysis methods that require accurate model parameters, we focus on how to establish the connection between geometry and data-driven approaches to represent the malicious behavior of attacks on state estimation. First of all, the existence of complete stealthy attacks is analyzed. Then, the maximal attached stealthy subspace and the set of estimation errors under complete stealthy attacks are analyzed intuitively from the geometric point of view. On this basis, the complete stealthy subspace is constructed with the subspace identification method, which is applied to generate the corresponding stealthy attack sequence through the collected system input-output data. Finally, simulation results are provided to illustrate the effectiveness of the proposed strategies.

查看原文本刊更多论文

针对网络物理系统的基于几何的数据驱动型完整隐形攻击

本文基于几何方法，提出了一种针对网络物理系统（CPS）的数据驱动型完全隐身攻击策略。攻击者旨在通过破坏执行器和传感器的部分通信链路来降低估计性能并保持隐蔽性。与需要精确模型参数的经典分析方法不同，我们的重点是如何建立几何方法与数据驱动方法之间的联系，以表示状态估计攻击的恶意行为。首先，我们分析了完全隐形攻击的存在。然后，从几何角度直观地分析了最大附加隐身子空间和完全隐身攻击下的估计误差集。在此基础上，利用子空间识别方法构建了完整的隐身子空间，并通过收集到的系统输入输出数据生成相应的隐身攻击序列。最后，还提供了仿真结果，以说明所提策略的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.