Recovery from Adversarial Attacks in Cyber-physical Systems: Shallow, Deep and Exploratory Works

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-03-27 DOI:10.1145/3653974
Pengyuan Lu, Lin Zhang, Mengyu Liu, Kaustubh Sridhar, Oleg Sokolsky, Fanxin Kong, Insup Lee
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Abstract

Cyber-physical systems (CPS) have experienced rapid growth in recent decades. However, like any other computer-based systems, malicious attacks evolve mutually, driving CPS to undesirable physical states and potentially causing catastrophes. Although the current state-of-the-art is well aware of this issue, the majority of researchers have not focused on CPS recovery, the procedure we defined as restoring a CPS’s physical state back to a target condition under adversarial attacks. To call for attention on CPS recovery and identify existing efforts, we have surveyed a total of 30 relevant papers. We identify a major partition of the proposed recovery strategies: shallow recovery vs. deep recovery, where the former does not use a dedicated recovery controller while the latter does. Additionally, we surveyed exploratory research on topics that facilitate recovery. From these publications, we discuss the current state-of-the-art of CPS recovery, with respect to applications, attack type, attack surfaces and system dynamics. Then, we identify untouched sub-domains in this field and suggest possible future directions for researchers.

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从网络物理系统中的对抗性攻击中恢复:浅层、深层和探索性工作
近几十年来,网络物理系统(CPS)经历了快速发展。然而,与其他基于计算机的系统一样,恶意攻击也会相互演变,使 CPS 陷入不理想的物理状态,并可能造成灾难。尽管目前的先进技术已经意识到了这一问题,但大多数研究人员并没有把重点放在 CPS 恢复上,我们将这一过程定义为在对抗性攻击下将 CPS 的物理状态恢复到目标状态。为了唤起人们对 CPS 恢复的关注并确定现有的努力,我们调查了总共 30 篇相关论文。我们确定了所提出的恢复策略的主要分区:浅层恢复与深层恢复,前者不使用专门的恢复控制器,而后者使用。此外,我们还调查了有关促进恢复主题的探索性研究。从这些出版物中,我们讨论了当前 CPS 恢复在应用、攻击类型、攻击面和系统动态方面的最新进展。然后,我们确定了该领域尚未触及的子领域,并为研究人员提出了未来可能的研究方向。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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