Volume 8 , Issue 1 , PP: 33-39, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Harith Yas 1 , Manal M. Nasir 2 *
Doi: https://doi.org/10.54216/IJWAC.080104
Connected Vehicle Systems (CVS) are a combination of transportation and digital technologies that have the potential to revolutionize road safety and efficiency. However, this interconnectivity exposes them to various evolving cyber threats that require proactive detection and mitigation strategies. This study examines the security threat landscape in CVS, focusing on the challenges posed by malicious intrusions, unauthorized access, and vulnerabilities within vehicular networks. By using Deep Neural Networks (DNNs) and conducting an extensive literature review on cybersecurity frameworks, autonomous vehicles, and network vulnerabilities, this research provides a robust methodology for detecting and mitigating attacks in vehicular networks. The results show that the proposed approach is effective with improved predictive capabilities as well as the ability to detect abnormal behaviors. The findings highlight the need for standardized cybersecurity frameworks, cooperation among stakeholders, and continuous improvement of security protocols to ensure safe interconnected vehicular networks in a rapidly changing technological environment.
Connected Vehicle , Cybersecurity , Threat Identification , Risk Mitigation Strategies , Automotive Network Security , Intrusion Detection Systems , Vehicle-to-Everything (V2X) Security , Malware Detection , Wireless Communication , Vehicle Telematics Security.
[1] Elliott, David, Walter Keen, and Lei Miao. 2019. “Recent Advances in Connected and Automated Vehicles.” Journal of Traffic and Transportation Engineering (English Edition) 6 (2): 109–31.
[2] Han, Jinpeng, Zhiyang Ju, Xiaoguang Chen, Manzhi Yang, Hui Zhang, and Rouxing Huai. 2023. “Secure Operations of Connected and Autonomous Vehicles.” IEEE Transactions on Intelligent Vehicles.
[3] Girdhar, Mansi, Yongsik You, Tai-Jin Song, Subhadip Ghosh, and Junho Hong. 2022. “Post-Accident Cyberattack Event Analysis for Connected and Automated Vehicles.” IEEE Access 10: 83176–94.
[4] Bayless, Steven H, Sean Murphy, and Anthony Shaw. 2011. “Connected Vehicle Assessment.” ITS America.
[5] Hidalgo, Carlos, Myriam Vaca, Mateusz P Nowak, Piotr Frölich, Martin Reed, Mays Al-Naday, Asterios Mpatziakas, Aikaterini Protogerou, Anastasios Drosou, and Dimitrios Tzovaras. 2022. “Detection, Control and Mitigation System for Secure Vehicular Communication.” Vehicular Communications 34: 100425.
[6] McCall, Sophia, Cagatay Yucel, and Vasilios Katos. 2021. “Education in Cyber Physical Systems Security: The Case of Connected Autonomous Vehicles.” In 2021 IEEE Global Engineering Education Conference (EDUCON), 1379–85.
[7] Park, Hyungjun, Zulqarnain Khattak, and Brian Smith. 2018. “Glossary of Connected and Automated Vehicle Terms.” University of Virginia Center for Transportation Studies.
[8] Ismail, M. and F.Abd El-Gawad , A. (2023) “Revisiting Zero-Trust Security for Internet of Things”, Sustainable Machine Intelligence Journal, 3. doi: 10.61185/SMIJ.2023.33106.
[9] El-Rewini, Zeinab, Karthikeyan Sadatsharan, Daisy Flora Selvaraj, Siby Jose Plathottam, and Prakash Ranganathan. 2020. “Cybersecurity Challenges in Vehicular Communications.” Vehicular Communications 23: 100214.
[10] Giannaros, Anastasios, Aristeidis Karras, Leonidas Theodorakopoulos, Christos Karras, Panagiotis Kranias, Nikolaos Schizas, Gerasimos Kalogeratos, and Dimitrios Tsolis. 2023. “Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions.” Journal of Cybersecurity and Privacy 3 (3): 493–543.
[11] Alqahtani, Hamed, and Gulshan Kumar. 2024. “Machine Learning for Enhancing Transportation Security: A Comprehensive Analysis of Electric and Flying Vehicle Systems.” Engineering Applications of Artificial Intelligence 129: 107667.
[12] Pendleton, Scott Drew, Hans Andersen, Xinxin Du, Xiaotong Shen, Malika Meghjani, You Hong Eng, Daniela Rus, and Marcelo H Ang. 2017. “Perception, Planning, Control, and Coordination for Autonomous Vehicles.” Machines 5 (1): 6.
[13] Ismail, M. and F.Abd El-Gawad , A. (2023) “Revisiting Zero-Trust Security for Internet of Things”, Sustainable Machine Intelligence Journal, 3. doi: 10.61185/SMIJ.2023.33106.
[14] Taeihagh, Araz, and Hazel Si Min Lim. 2019. “Governing Autonomous Vehicles: Emerging Responses for Safety, Liability, Privacy, Cybersecurity, and Industry Risks.” Transport Reviews 39 (1): 103–28.
[15] Heemstra, Jennifer. 2018. “Autonomous Vehicle Technology-The Need for a National Standard on Cybersecurity.” Ave Maria L. Rev. 16: 130.
[16] Adu-Kyere, Akwasi, Ethiopia Nigussie, and Jouni Isoaho. 2023. “Self-Aware Cybersecurity Architecture for Autonomous Vehicles: Security through System-Level Accountability.” Sensors 23 (21): 8817.
[17] A. Metwaly, A. and Elhenawy, I. (2023) “Sustainable Intrusion Detection in Vehicular Controller Area Networks using Machine Intelligence Paradigm”, Sustainable Machine Intelligence Journal, 4. doi: 10.61185/SMIJ.2023.44104.
[18] Ding, W., Alrashdi, I., Hawash, H. and Abdel-Basset, M., 2023. DeepSecDrive: An explainable deep learning framework for real-time detection of cyberattack in in-vehicle networks. Information Sciences, p.120057.
[19] Chowdhury, Abdullahi, Gour Karmakar, Joarder Kamruzzaman, Alireza Jolfaei, and Rajkumar Das. 2020. “Attacks on Self-Driving Cars and Their Countermeasures: A Survey.” IEEE Access 8: 207308–42.
[20] Islam, Md Aminul, and Sarah Alqahtani. 2023. “Autonomous Vehicles an Overview on System, Cyber Security, Risks, Issues, and a Way Forward.” ArXiv Preprint ArXiv:2309.14213.
[21] Ahmad, Jameel, Muhammad Umer Zia, Ijaz Haider Naqvi, Jawwad Nasar Chattha, Faran Awais Butt, Tao Huang, and Wei Xiang. 2023. “Machine Learning and Blockchain Technologies for Cybersecurity in Connected Vehicles.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1515.
