Language-based opacity in modular discrete event systems: Compositional secret-based verification using labeled petri nets

IF 6.8 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS
Salwa Habbachi , Imen Ben Hafaiedh , Zhiwu Li
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引用次数: 0

Abstract

This work focuses on verifying language-based opacity within modular discrete-event systems. We consider a distributed system that is modeled as a composition of multiple interacting modules, each modeled by a labeled Petri net. Ensuring confidentiality in such systems is critical for cyber-physical systems and industrial networks, where unauthorized inference of sensitive data can lead to security breaches. We introduce a new definition of language-based opacity for modular systems and propose three secret-based verification methods that avoid the construction of the monolithic system through parallel composition. Our approach includes three methods: (1) global secret verification via observer synchronization; (2) local, module-level secret verification; and (3) an iterative composition optimization that avoids building the entire modular system, yielding significant computational savings. Experimental results on a benchmark smart manufacturing system demonstrate the practical efficiency of our approach, showing orders-of-magnitude improvement in verification time and memory usage over traditional monolithic approaches.
模块化离散事件系统中基于语言的不透明性:使用标记petri网的基于组合秘密的验证
这项工作的重点是验证模块化离散事件系统中基于语言的不透明性。我们考虑一个分布式系统,它被建模为多个相互作用模块的组合,每个模块由一个标记的Petri网建模。确保此类系统的机密性对于网络物理系统和工业网络至关重要,在这些系统中,未经授权的敏感数据推断可能导致安全漏洞。我们引入了模块化系统基于语言的不透明性的新定义,并提出了三种基于秘密的验证方法,避免了通过并行组合来构建单一系统。我们的方法包括三种方法:(1)通过观测器同步进行全局秘密验证;(2)本地、模块级保密验证;(3)迭代组合优化,避免构建整个模块化系统,从而显著节省计算量。在一个基准智能制造系统上的实验结果证明了我们的方法的实际效率,与传统的单片方法相比,在验证时间和内存使用方面有了数量级的改进。
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
期刊介绍: Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.
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