A Preliminary Design-Phase Security Methodology for Cyber-Physical Systems
A Preliminary Design-Phase Security Methodology for Cyber-Physical Systems
复制标题
网络物理系统的初步设计阶段安全方法
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
C. Fleming
中科院分区：
文献类型：
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作者：
Bryan T. Carter;Stephen C. Adams;Georgios Bakirtzis;Tim Sherburne;P. Beling;B. Horowitz;C. Fleming
尽管名称中有“网络”，但网络物理系统具有独特的特征，限制了传统网络安全技术和策略的适用性和适用性。此外，网络物理系统的漏洞可能会产生重大的安全影响。这些系统固有的物理和网络交互不仅要求防御或预防网络漏洞，而且要求系统在面对成功的攻击时具有弹性。鉴于网络物理系统的复杂性，必须以有针对性和系统的方式识别和评估适当的防御和弹性策略。具体来说，哪些弹性策略适合给定系统，在哪里以及在给定时间和/或预算限制的情况下应该实施哪些策略？本文提出了两种方法：(1) 网络安全需求方法；(2) 系统理论、基于模型的方法，用于识别和优先考虑在给定系统和任务中实施的适当弹性策略。该方法通过基于假设武器系统的案例研究进行了论证。对两种方法结果的评估和比较表明，本文提出的技术可以通过基于模型的证据来增强和增强现有的系统工程方法。
Despite “cyber” being in the name, cyber–physical systems possess unique characteristics that limit the applicability and suitability of traditional cybersecurity techniques and strategies. Furthermore, vulnerabilities to cyber–physical systems can have significant safety implications. The physical and cyber interactions inherent in these systems require that cyber vulnerabilities not only be defended against or prevented, but that the system also be resilient in the face of successful attacks. Given the complex nature of cyber–physical systems, the identification and evaluation of appropriate defense and resiliency strategies must be handled in a targeted and systematic manner. Specifically, what resiliency strategies are appropriate for a given system, where, and which should be implemented given time and/or budget constraints? This paper presents two methodologies: (1) the cyber security requirements methodology and (2) a systems-theoretic, model-based methodology for identifying and prioritizing appropriate resiliency strategies for implementation in a given system and mission. This methodology is demonstrated using a case study based on a hypothetical weapon system. An assessment and comparison of the results from the two methodologies suggest that the techniques presented in this paper can augment and enhance existing systems engineering approaches with model-based evidence.