Kai Wang, Jiankuo Dong, Yijie Xu, Xinyi Ji, Letian Sha, Fu Xiao
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引用次数: 0
Abstract
In recent vigorous developments, digital twin edge networks (DITEN) have emerged as a network paradigm to improve network communication efficiency. Given that Web 3.0 technologies promise secure decentralized data storage and effective information exchange, it is feasible to construct a wireless edge intelligence-enabled Web 3.0 physical infrastructure through DITEN. However, DITEN encounters various security threats related to communication and authentication, and establishing a secure and cost-effective authentication scheme for confidential access to physical entities poses a significant challenge. To tackle this issue, in this article, we introduce READ, a provably secure multi-factor user authentication scheme tailored for DITEN in industrial applications. Using designed ASCON cryptography primitive cipher suite, physical unclonable functions, extended Chebyshev chaotic maps, one-way secure collision-resistant hash functions, and lightweight bitwise exclusive-or operations, READ enables mutual authentication and session key negotiation among mobile users, smart gateways, and smart industrial devices. Rigorous security assessments, conducted through the real-or-random (ROR) model, the automated validation of internet security-sensitive protocols and applications (AVISPA) simulation tool, and heuristic informal security analysis, confirm that READ meets all 13 security evaluation criteria. Furthermore, compared to other seven advanced multi-factor user authentication schemes, READ excels in security and efficiency, making it ideal for practical multi-factor user authentication scenarios.
期刊介绍:
Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications.
Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration.
Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.