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All Journal Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science
Saare, Murtaja Ali
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Design and Evaluation of Secure Software Architectures for 5G-Enabled Vehicular Driving System Saare, Murtaja Ali; Mattar, Ali K.; Sari, Sari Ali; Wong, Seng Yue
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26032

Abstract

Vehicular Ad-hoc Networks (VANETs) represent support for Intelligent Transport Systems (ITS) that allow vehicles and infrastructures to exchange real-time information. Nevertheless, the introduction of the 5G technology for the VANETs poses new security challenges, especially considering the emerging quantum computing threats. In response to this problem, we present a secure software architecture, Lattice Efficient Mutual Authentication (LEMA), designed to improve vehicular communication in 5G supported environments. The research novelty is the construction of LEMA—a lightweight and scalable framework for robust authentication in the fog, based on lattice-based postquantum cryptography, which is also resilient to classical and quantum-based attacks and provides low latency. The framework operates based on three core phases: initialization by a Trusted Authority, secure private key generation, and mutual authentication via LWE-based schemes. A testbed which is built on a Raspberry Pi is used for simulating OBUs to verify LEMA performance in a resource-constrained environment. We compare LEMA with the state of art and get the performance numbers for the computational overhead, communication cost and storage efficiency. Simulation results show that with LEMA, the computational time, the communication amount and the storage consumed can be decreased by at least 25%, 30% and 20% than the benchmark protocols, respectively, and it is secure against the man-in-themiddle and the key-compromise attacks. The authors’ use of fog servers for deployment of the system also significantly boosts real-time responsiveness. Finally, the LEMA model presents a promising quantum-secure authentication technique for 5G-based vehicular networks. In the future we plan to combine it with AIbased anomaly detection and blockchain, for better scalability, privacy and decentralization.
Artificial Intelligence-Driven and Secure 5G-VANET Architectures for Future Transportation Systems Saare, Murtaja Ali; Abdulhamed, Mohamed Abdulrahman; Al-Shareeda‬‏, ‪Mahmood A.; Almaiah, Mohammed Amin; Shehab, Rami
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26295

Abstract

The advent of 5G has opened a new era of intelligent, adaptive and secure VANETs that is envisaged to serve as the backbone network architecture for next generation of vehicular transportation systems. In this work, we present a connected 5G VANETs-to-Edge Computing systems with Artificial Intelligence (AI) infrastructure to improve system adaptability, anomaly detection, trust management, and real-time decisionmaking. Crucial enabling technologies like Software-Defined Networking (SDN). Mobile Edge Computing (MEC), and millimeterwave communication are investigated in detail. We examine key security threats such as identity forgery, data interception, and denial-of-service attacks, and assess the AI-enhanced defense measures such as intrusion detection systems and blockchainbased trust models. Applications, like autonomous platooning, and collaborative vehicle authentication provide additional examples of AI technologies’ added value in the context of vehicular communications and safety. The paper concludes by providing open issues and future directions, including quantum-resistant protocols, lightweight AI models and cognitive networking in the context AI-driven 5G-VANET ecosystems.
Proposed security mechanism for preventing fake router advertisement attack in IPv6 link-local network Al-Shareeda, Mahmood A.; Manickam, Selvakumar; Saare, Murtaja Ali; Arjuman, Navaneethan C.
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp518-526

Abstract

The design of router discovery (RD) is a trust mechanism to confirm the legitimacy of the host and router. Fake router advertisement (RA) attacks have been made possible by this RD protocol design defect. Studies show that the standard RD protocol is vulnerable to a fake RA attack where the host will be denied a valid gateway. To cope with this problem, several prevention techniques have been proposed in the past to secure the RD process. Nevertheless, these methods have a significant temporal complexity as well as other flaws, including the bootstrapping issue and hash collision attacks. Thus, the SecMac-secure router discovery (SecMac-SRD) technique, which requires reduced processing time and may thwart fake RA assaults, is proposed in this study as an improved secure RD mechanism. SecMac-SRD is built based on a UMAC hashing algorithm with ElGamal public key distribution cryptosystem that hides the RD message exchange in the IPv6 link-local network. Based on the obtained expected results display that the SecMac-SRD mechanism achieved less processing time compared to the existing secure RD mechanism and can resist fake RA attacks. The outcome of the expected results clearly proves that the SecMac-SRD mechanism effectively copes with the fake RA attacks during the RD process.
Co-Authors Abdulhamed, Mohamed Abdulrahman Al-Shareeda, Mahmood A. Al-Shareeda‬‏, ‪Mahmood A. Almaiah, Mohammed Amin Arjuman, Navaneethan C. Manickam, Selvakumar Mattar, Ali K. Sari, Sari Ali Shehab, Rami Wong, Seng Yue