Protection architecture of endogenous safety and security for industrial control systems

Xin, Yaozhong

doi:10.1051/sands/2023001

Cited by 2 publications

(2 citation statements)

References 11 publications

(12 reference statements)

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“…Industrial control systems adopt a similar concept: parallel redundant mimicry (this is similar to the DHR architecture). The system resilience is enhanced, introducing four protection dimensions, Technology, Emergency, Management, and Time (TEMt) in [17]. The internal structure of the basic framework of the zero-trust system is restructured in [18] through the lens of the DHR architecture, termed ZETSA.…”

Section: Related Workmentioning

confidence: 99%

“…From this perspective, the DHR architecture system is a specified multi-heterogeneous system. Due to the superiority, the ESS theory along with the DHR architecture have been introduced to the fields of wireless communications systems [14], sixth-generation mobile communication (6G) networks [15], embedded systems [16], industrial control systems [17], zero trust systems [18], etc.…”

Section: Introductionmentioning

confidence: 99%

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A Differentially Private Framework for the Dynamic Heterogeneous Redundant Architecture System in Cyberspace

Kang,

Zhang,

Jiang

et al. 2024

Electronics

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With the development of information technology, tremendous vulnerabilities and backdoors have evolved, causing inevitable and severe security problems in cyberspace. To fix them, the endogenous safety and security (ESS) theory and one of its practices, the Dynamic Heterogeneous Redundant (DHR) architecture, are proposed. In the DHR architecture, as an instance of the multi-heterogeneous system, a decision module is designed to obtain intermediate results from heterogeneous equivalent functional executors. However, privacy-preserving is not paid attention to in the architecture, which may cause privacy breaches without compromising the ESS theory. In this paper, based on differential privacy (DP), a theoretically rigorous privacy tool, we propose a privacy-preserving DHR framework called DP-DHR. Gaussian random noise is injected into each (online) executor output in DP-DHR to guarantee DP, but it also makes the decision module unable to choose the final result because each executor output is potentially correct even if it is compromised by adversaries. To weaken this disadvantage, we propose the advanced decision strategy and the hypersphere clustering algorithm to classify the perturbed intermediate results into two categories, candidates and outliers, where the former is closer to the correct value than the latter. Finally, the DP-DHR is proven to guarantee DP, and the experimental results also show that the utility is not sacrificed for the enhancement of privacy by much (a ratio of 4–7% on average), even in the condition of some executors (less than one-half) being controlled by adversaries.

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Section: Related Workmentioning

confidence: 99%