Securing the Driverless Highway: AI, Cyber Threats, and the Future of Autonomous Vehicles

References

[1] Alshammari, A., & Yoon, J. (2021). Threats in autonomous vehicle networks: Cybersecurity perspectives. Journal of Network Security, 19(4), 475-489. https://doi.org/10.1016/j.jns.2021.06.003

[2] Anderson, B., & Lee, H. (2021). Strategic Timing in AI-Powered Cyber Attacks. Cybersecurity Defense Journal, 9(1), 112-126. https://doi.org/10.1016/j.cydef.2021.101

[3] Anderson, E., & Shinde, K. (2022). Artificial Intelligence in Cybersecurity: Opportunities and Risks. International Journal of Information Security, 4(2), 79-92. https://doi.org/10.1007/s10207-022-00665-0

[4] Brown, T., & Tran, H. (2022). Evasion Strategies in Malware: The Role of AI in Adaptive Attacks. Journal of Malware Analysis, 14(3), 107-118. https://doi.org/10.1016/j.mala.2022.10718

[5] Carter, L., & Thompson, E. (2023). Multi-layered cybersecurity for autonomous vehicle fleets. Cyber-Physical Logistics Journal, 10(1), 33-50. https://doi.org/10.1177/2047491

[6] Chen, T., & Huang, J. (2023). Understanding cyber risks in autonomous vehicle sensors. Transportation Security Review, 13(1), 45-60. https://doi.org/10.1016/j.tsr.2023.01.009

[7] Chen, T., & Singh, P. (2021). The significance of cybersecurity in autonomous vehicle adoption. Journal of Cybersecurity and Mobility, 9(2), 101-115. https://doi.org/10.1007/s41060-021-00256-y

[8] Davis, R., & Li, F. (2023). AI and malware in autonomous systems: An emerging threat landscape. Cyber-Physical Systems Journal, 8(1), 55-71. https://doi.org/10.1109/TCPS.2023.3284767

[9] Dutta, A., & Smith, B. (2023). Monitoring Challenges in Complex Supply Chains. Cyber Supply Chain Security Journal, 9(2), 65-78. https://doi.org/10.1093/cysu.2023.0195

[10] Feng, S., Zhang, X., & Wang, D. (2022). Cybersecurity challenges in intelligent and connected vehicles. IEEE Transactions on Transportation Systems, 10(1), 91-107. https://doi.org/10.1109/TTS.2022.3150334

[11] Garcia, F., & Brown, T. (2022). Cybersecurity challenges in autonomous trucks for industrial applications. International Journal of Industrial Cybersecurity, 7(1), 45-61. https://doi.org/10.1007/s11963-022-02047-w

[12] Garcia, P., & Liu, Y. (2022). Manipulating Machine Learning Models: Risks and Mitigations in Supply Chain Security. International Journal of Cybersecurity, 8(1), 45-60. https://doi.org/10.1080/ijcs.2022.03.008

[13] Gade, R., & Nagar, S. (2023). Supply Chain Security: New Challenges from AI-Driven Attacks. Cybersecurity Technology Review, 5(1), 45-58. https://doi.org/10.1016/j.cyber2023.11002

[14] Ghosh, A., & Lee, J. (2021). AI-Enabled Cyber Threats: Risks and Defenses. Journal of Cybersecurity and Digital Forensics, 6(4), 45-58. https://doi.org/10.1177/11798032

[15] Huang, L., & Kim, J. (2024). Real-world cyber incidents in autonomous vehicle systems. Transportation Security Review, 13(1), 97-114. https://doi.org/10.1016/j.tsr.2024.01.011

[16] Jackson, T., & Lee, R. (2023). Cybersecurity in urban autonomous vehicles: The case of Waymo taxis. Journal of Autonomous Vehicle Studies, 12(2), 88-102. https://doi.org/10.1016/j.javs.2023.04.005

[17] Jiang, H., Lee, P., & Choi, Y. (2022). Model Poisoning in Autonomous Systems: Risks and Mitigations. Journal of Information Security, 14(1), 39-50. https://doi.org/10.1007/s10207-022-00361-5

[18] Jiang, R., & Wu, X. (2022). Optimizing Attack Timing with Predictive AI. Journal of Cybersecurity Applications, 14(2), 89-98. https://doi.org/10.1016/j.cyberapp.2022.098

[19] Kavi, A., & Shah, A. (2022). Analyzing Vulnerabilities in Modern Supply Chains. Cyber Risk Journal, 13(2), 33-47. https://doi.org/10.1093/crj/cyb0393

[20] Kwon, Y., & Lee, M. (2022). Fleet disruptions and cybersecurity threats in autonomous transport. International Journal of Logistics Security, 14(2), 141-155. https://doi.org/10.1080/2023145

[21] Lee, C., & Chen, H. (2021). Connected vehicle systems and cybersecurity: Emerging challenges. Journal of Network Security, 12(3), 109-127. https://doi.org/10.1016/j.jns.2021.06.019

[22] Lee, H., & Thompson, J. (2023). Target Selection in AI-Powered Supply Chain Attacks. Information Systems Security Journal, 19(2), 133-148. https://doi.org/10.1016/issj.2023.02.003

[23] Martinez, A., & Kim, D. (2022). Expanding Attack Surfaces: The Impact of IoT and Cloud Computing on Supply Chain Security. Computers & Security, 110, Article 102345. https://doi.org/10.1016/j.cose.2021.102345

[24] Mitra, A., Zhang, Y., & Lee, C. (2022). Adversarial AI in Malware and Phishing Attacks. ACM Computing Surveys, 55(4), Article 47. https://doi.org/10.1145/3397531

[25] Morgan, J., & Gray, T. (2023). Interpreting Cybersecurity Threat Data. Computers & Cybersecurity Insights, 22(1), 9-21. https://doi.org/10.1080/03018032

[26] Morris, S., & Gupta, R. (2023). Adversarial Manipulation in Machine Learning Models. International Journal of Cyber Threat Intelligence, 11(2), 55-70. https://doi.org/10.1145/3523321

[27] Oswald, R., & Gibson, A. (2023). Privacy challenges in connected vehicle ecosystems. Journal of Digital Privacy, 6(3), 218-233. https://doi.org/10.1007/s11057-023-01980-w

[28] Patel, S., & Gomez, M. (2022). AI-driven autonomous systems: Enhancing capabilities and risks. AI & Society, 37(3), 411-426. https://doi.org/10.1007/s00146-022-01418-8

[29] Perez, D., & Kim, J. (2023). Real-time threat detection in autonomous taxis: Case studies and implications. Journal of Smart Transportation, 6(2), 95-113. https://doi.org/10.1109/JST.2023.003485

[30] Robinson, P., & Evans, K. (2023). Economic impact of AV disruptions in logistics and industrial sectors. Journal of Autonomous Fleet Management, 15(2), 109-125. https://doi.org/10.1009/jafm.2023.028

[31] Smith, K., & Robertson, L. (2022). Data collection methods in cybersecurity research. Journal of Security Studies, 11(2), 112-128. https://doi.org/10.1016/j.jss.2022.02.004

[32] Turner, K., & Morgan, L. (2023). The vulnerabilities of autonomous vehicles: A cybersecurity perspective. Cybersecurity Journal, 15(3), 309-327. https://doi.org/10.1016/j.cysec.2023.05.018

[33] Wang, H., & Zhao, Y. (2024). Establishing secure frameworks for autonomous vehicle systems. Journal of Transportation Technology, 12(1), 22-39. https://doi.org/10.1145/3680003

@article{1706489,
      author = {Oladoyin Akinsuli},
      title = {Securing the Driverless Highway: AI, Cyber Threats, and the Future of Autonomous Vehicles},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {2},
      pages = {957-970},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706489.pdf},
      abstract = {AVs are somewhat in a fairly active evolution that provides opportunities for considerable changes to safety, effectiveness, and transport accessibility. Being integrated and AI-based controlled systems, AVs are designed to reduce crashes, reduce transport costs, and reimagine urban mobility. However, the over-defense of the AV system becomes a problem as AV technology becomes more advanced and becomes a massive cybersecurity threat. Real automated systems involve numerous sensors, efficient decision-makers, and cloud-based architecture, and all such systems are vulnerable to hackers. The issues in these interconnected systems are the possibility of disruption of sensors, data breaches, and malware, to name a few. Future work depends upon cybersecurity measures where the threat space for AVs might continue expanding and ill-equipped to handle unauthorized access and harm risk for passengers and the public. In this article, the author identifies several key risks associated with cybersecurity in autonomous vehicles (AVs) and provides new examples that illustrate the consequences of these threats. This article offers a user-friendly overview of the AV industry today by evaluating the current state of defensive initiatives and regulatory activities related to cybersecurity. The author explores how the landscape for Chief Data Officers (CDOs) may evolve, emphasizing the importance of cooperative defense measures, advanced AI protection methods, and emerging regulations. Given the complexities of the AV ecosystem, critical research directions are essential for achieving robust security. Future security solutions will not be one-time fixes; instead, they will require an ongoing process of collaboration among industries, continuous research and development of innovative solutions, and new regulations to protect AV systems from evolving cyber threats.},
      keywords = {Autonomous Vehicles, Cybersecurity, AI Malware, Data Privacy, V2V Communication, Threat Mitigation},
      month = {August},
  }