AI-optimized elliptic curve with Certificate-Less Digital Signature for zero trust maritime security

Al-Khalidi, Mohammed ORCID: https://orcid.org/0000-0002-1655-8514, Al-Zaidi, Rabab ORCID: https://orcid.org/0000-0002-2198-7794, Ali, Tarek ORCID: https://orcid.org/0000-0002-8380-1625, Khan, Safiullah ORCID: https://orcid.org/0000-0001-8342-6928 and Bashir, Ali Kashif ORCID: https://orcid.org/0000-0003-2601-9327 (2025) AI-optimized elliptic curve with Certificate-Less Digital Signature for zero trust maritime security. Ad Hoc Networks, 166. 103669. ISSN 1570-8705

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Abstract

The proliferation of sensory applications has led to the development of the Internet of Things (IoT), which extends connectivity beyond traditional computing platforms and connects all kinds of everyday objects. Marine Ad Hoc Networks are expected to be an essential part of this connected world, forming the Internet of Marine Things (IoMaT). However, marine IoT systems are often highly distributed, and spread across large sparse areas which makes it challenging to implement and manage centralized security measures. Despite some ongoing efforts to establish network connectivity in such environment, securing these networks remains an unreached goal. The use of Certificate-Less Digital Signatures (CLDS) with Elliptic Curve Cryptography (ECC) shows great promise in providing secure communication in these networks and achieving zero trust IoMaT security. By eliminating the need for certificates and associated key management infrastructure, CLDS simplifies the key management process. ECC also enables secure communication with smaller key sizes and faster processing times, which is crucial for resource-limited IoMaT devices. In this paper, we introduce CLDS using ECC as a means of securing IoT networks in a marine environment, creating a zero trust security framework for Internet of Marine Things (IoMaT). To increase security and robustness of the framework, we optimize the ECC parameters using two vital artificial intelligence algorithms, namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Evaluation results demonstrate a reduction in ECC parameter generation time by over 40% with GA optimization and 20% with PSO optimization. Additionally, the computational cost and memory usage for major ECC attacks increased significantly by up to 40% and 67% for Rho attacks, 34% and 53% for brute-force attacks, and 30% and 67% for improved hybrid attacks, respectively.