How to Cite
Abstract
The Internet of Vehicles (IoV) and ITS work as a backbone for the integration of numerous services in data-centric applications associated with future communication paradigms. IoV is expected to offer a wide range of novel ITS driving services and different categories of informative data. That being said, securing the interactions within IoV remains a challenging problem, particularly with regard to effective security measures for various emerging communication paradigms [1]. Generally, to guarantee the authenticity, integrity, and confidentiality of emerging services within the IoV, many communication-centric communication protocols are secure. Usually, this approach of network-centric security paradigms does not offer a comprehensive security solution spanning to the entire forward signaling process of services. Nonetheless, a more diverse security strategy in the security ecosystem is needed.
Autonomous vehicles have proven their potential worldwide by overcoming a variety of challenges such as urban traffic congestion, pollution, traffic crashes, increased transportation, and on-demand mobility requirements [2]. A surge in demand for AVs has entwined these vehicles into the Internet of Things (IoT), Intelligent Transportation Systems (ITS), and Vehicle-to-Everything (V2X) eco-systems. This significant integration of AVs with other interrelated technologies has further led to cyber-security vulnerabilities, which in turn poses a serious threat to transportation technologies [3]. The in-vehicle electronic architecture and multi-level architecture make AVs an attractive target for cyber-attack activities. Due to their high complexity and a large number of Electronic Control Units (ECUs), an adversary can bypass multiple protective layers to achieve pre-set objectives. Thus, the absence of an effective cyber-security mechanism can threaten the swift adoption of AVs and V2X communication networks.
References
[1] R. Singh Rathore, C. Hewage, O. Kaiwartya, and J. Lloret, "In-Vehicle Communication Cyber Security: Challenges and Solutions," 2022. ncbi.nlm.nih.gov
[2] A. Ferdowsi, U. Challita, W. Saad, and N. B. Mandayam, "Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems," 2018. [PDF]
[3] V. Kumar Kukkala, S. Vignesh Thiruloga, and S. Pasricha, "Roadmap for Cybersecurity in Autonomous Vehicles," 2022. [PDF]
[4] M. Hamad, A. Finkenzeller, M. Kühr, A. Roberts et al., "REACT: Autonomous Intrusion Response System for Intelligent Vehicles," 2024. [PDF]
Tatineni, Sumanth. "Customer Authentication in Mobile Banking-MLOps Practices and AI-Driven Biometric Authentication Systems." Journal of Economics & Management Research. SRC/JESMR-266. DOI: doi. org/10.47363/JESMR/2022 (3) 201 (2022): 2-5.
Vemori, Vamsi. "Evolutionary Landscape of Battery Technology and its Impact on Smart Traffic Management Systems for Electric Vehicles in Urban Environments: A Critical Analysis." Advances in Deep Learning Techniques 1.1 (2021): 23-57.
Shaik, Mahammad, Srinivasan Venkataramanan, and Ashok Kumar Reddy Sadhu. "Fortifying the Expanding Internet of Things Landscape: A Zero Trust Network Architecture Approach for Enhanced Security and Mitigating Resource Constraints." Journal of Science & Technology 1.1 (2020): 170-192.
[8] S. Ullah, M. A. Khan, J. Ahmad, S. Shaukat Jamal et al., "HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles," 2022. ncbi.nlm.nih.gov
[9] T. H. H. Aldhyani and H. Alkahtani, "Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity," 2022. ncbi.nlm.nih.gov
[10] M. J. Kang and J. W. Kang, "Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security," 2016. ncbi.nlm.nih.gov
[11] T. H. Luan, Y. Zhang, L. Cai, Y. Hui et al., "Autonomous Vehicular Networks: Perspective and Open Issues," 2021. [PDF]
[12] D. Haileselassie Hagos and D. B. Rawat, "Recent Advances in Artificial Intelligence and Tactical Autonomy: Current Status, Challenges, and Perspectives," 2022. ncbi.nlm.nih.gov
[13] E. Seo, H. Min Song, and H. Kang Kim, "GIDS: GAN based Intrusion Detection System for In-Vehicle Network," 2019. [PDF]
[14] K. M. Ali Alheeti, M. Shaban Al-ani, and K. McDonald-Maier, "A hierarchical detection method in external communication for self-driving vehicles based on TDMA," 2018. ncbi.nlm.nih.gov
[15] L. Yang, A. Moubayed, and A. Shami, "MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles," 2021. [PDF]
[16] C. Oham, R. Jurdak, and S. Jha, "Risk Analysis Study of Fully Autonomous Vehicle," 2019. [PDF]
[17] I. Koley, S. Adhikary, R. Rohit, and S. Dey, "A CAD Framework for Simulation of Network Level Attack on Platoons," 2022. [PDF]
[18] A. Qayyum, M. Usama, J. Qadir, and A. Al-Fuqaha, "Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward," 2019. [PDF]
[19] S. M Mostaq Hossain, S. Banik, T. Banik, and A. Md Shibli, "Survey on Security Attacks in Connected and Autonomous Vehicular Systems," 2023. [PDF]
[20] A. Olivares-Del Campo, S. Palomares-Ruiz, and S. Pascoli, "Implications of a Dark Matter-Neutrino Coupling at Hyper-Kamiokande," 2018. [PDF]
[21] S. Paiva, M. Abdul Ahad, G. Tripathi, N. Feroz et al., "Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges," 2021. ncbi.nlm.nih.gov
[22] L. Yang, A. Moubayed, I. Hamieh, and A. Shami, "Tree-based Intelligent Intrusion Detection System in Internet of Vehicles," 2019. [PDF]
[23] A. Haydari and Y. Yilmaz, "RSU-Based Online Intrusion Detection and Mitigation for VANET," 2022. ncbi.nlm.nih.gov
[24] M. Dibaei, X. Zheng, K. Jiang, S. Maric et al., "An Overview of Attacks and Defences on Intelligent Connected Vehicles," 2019. [PDF]
[25] Y. Dong, K. Chen, Y. Peng, and Z. Ma, "Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network," 2022. [PDF]
[26] M. N. Injadat, A. Moubayed, A. Bou Nassif, and A. Shami, "Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities," 2021. [PDF]
[27] A. Shoker, V. Rahli, J. Decouchant, and P. Esteves-Verissimo, "Intrusion Resilience Systems for Modern Vehicles," 2023. [PDF]
[28] P. Meyer, T. Häckel, T. Lübeck, F. Korf et al., "A Framework for the Systematic Assessment of Anomaly Detectors in Time-Sensitive Automotive Networks," 2024. [PDF]
[29] R. W. van der Heijden, T. Lukaseder, and F. Kargl, "VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs," 2018. [PDF]
[30] S. Boddupalli, A. Someshwar Rao, and S. Ray, "Resilient Cooperative Adaptive Cruise Control for Autonomous Vehicles Using Machine Learning," 2021. [PDF]