Securing 5G ‐Enabled CPS and IoT With VANET : Key Agreement Methods for Enhanced Identification
Year: 2025
Type: article
Abstract: ABSTRACT Despite presenting significant safety concerns, the proliferation of Cyber‐Physical Systems (CPS) in industries such as electricity distribution and medicine has spurred innovative applications. The integration of 5G technology, Software‐Defined Networking (SDN), and Vehicular Ad Hoc Networks (VANET) within Industrial CPS (ICPS) enables dynamic, real‐time interactions but also exposes these systems to vulnerabilities and potential malicious activities. Addressing these risks requires robust security strategies that safeguard both the physical and digital components of CPS. In this context, the Strengthening the Safety of 5G‐Enabled CPS using Key Agreement Method (SSKAM) framework is proposed. It incorporates a triple‐element user identification technique leveraging user passwords, mobile devices, and unique biometrics. This approach ensures two‐way authentication and facilitates the establishment of secure session keys for encrypted communication between registered users, CPS smart devices, and VANETs. The study also explores advanced key agreement methods tailored for 5G‐enabled CPS, focusing on enhancing identification protocols while maintaining a balance between computational efficiency, communication overhead, and resilience to emerging threats. By integrating SDN, the framework enforces dynamic security measures at the network level, ensuring real‐time adaptability to potential threats. Comprehensive evaluations demonstrate the efficacy of the proposed SSKAM framework in mitigating risks such as replay attacks, impersonation, and man‐in‐the‐middle assaults. The results highlight its viability in safeguarding the integrity and confidentiality of CPS, offering a scalable, efficient, and practical solution to address the evolving security challenges in 5G‐enabled CPS integrated with IoT ecosystems and VANETs.
DOI: 10.1002/ett.70259
Authors: P. B. Smitha, P. Kavipriya
Institution: Sathyabama Institute of Science and Technology
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7092184244