00
Days
00
Hrs
00
Min
00
Sec
Submit Your Paper

Challenges of Securing Artificial Intelligence-powered Systems from Cyber Threats: Case Study of Autonomous Vehicles

Authors

Oluwatosin Ogunlade

University of East London, UK (GB)

Abimbola Ogunlade

Engineering Institute of Technology, Australia (GB)

Mobolaji Tenibiaje

Bamidele Olumilua University of Education, Science and Technology Ikere Ekiti. (GB)

Article Information

DOI: 10.51583/IJLTEMAS.2025.1402007

Subject Category: Artificial Intelligence

Volume/Issue: 14/2 | Page No: 59-65

Publication Timeline

Submitted: 2025-03-07

Published: 2025-03-07

Abstract

Abstract: The integration of Artificial intelligence (AI) into various sectors, including transportation, has a significant impact on human endeavors, in addition to eco-friendly advantages. One of the most promising areas of AI-powered systems is the manufacture of Autonomous Vehicles (AVs). These self-driving cars, also known as driverless, are intelligent vehicles that can operate without human aid or support. AVs are equipped with sophisticated AI-powered technologies such as sensors, radars, Global Positioning System (GPS), and advanced algorithms that can transmit information and navigate the environment using analyzed data. These driverless cars have the potential of revolutionizing the transport sector by improving efficiency, reducing road accidents, improving flexibility, and decreasing congestion. However, AI in AV applications poses some risks and challenges associated with securing systems from cybersecurity threats and attacks. This paper explores the dangers and difficulties of securing AI systems from cyber threats, highlighting various detection and prevention mechanisms. The ethical and legal implications, including strategies to address these challenges proactively, are also discussed. It is believed that the challenges in the automotive industry can be mitigated through collaboration among stakeholders, manufacturers, researchers, IT professionals, and policymakers by implementing robust security measures, conducting regular vulnerability assessments, and leveraging the expertise of software security specialists. Collaboration between industry and cybersecurity professionals is essential to safeguarding public safety.

Keywords

Autonomous Vehicles, Artificial Intelligence, Transportation, Cyber Threats

Downloads

References

1. Algarni, A., & Thayananthan, V. (2022). Autonomous vehicles: The cybersecurity vulnerabilities and countermeasures for big data communication (pp. 2–19). [Google Scholar] [Crossref]

2. Alrajeh, D., & Prenosil, V. (2019). Cybersecurity in autonomous vehicles: Trends and challenges. In *Proceedings of the 14th International Conference on Availability, Reliability and Security (ARES)* (pp. 1–10). IEEE. [Google Scholar] [Crossref]

3. Alrubaian, M., Abolhasan, M., & Ni, W. (2019). Securing connected autonomous vehicles: A survey. *IEEE Communications Surveys & Tutorials, 21*(1), 616–646. [Google Scholar] [Crossref]

4. Ansari, M. T. J., Pandey, D., & Alenezi, M. (2018). STORE: Security Threat Oriented Requirements Engineering Methodology. *Journal of King Saud University - Computer and Information Sciences, 34*(2), 191. https://doi.org/10.1016/j.jksuci.2018.12.005 [Google Scholar] [Crossref]

5. Antonini, M., Barenghi, A., & Pelosi, G. (2020). Security issues in autonomous driving: Threats and a survey of solutions. *ACM Transactions on Cyber-Physical Systems, 4*(1), 1–34. [Google Scholar] [Crossref]

6. Atakishiyev, S., Salameh, M., Yaoa, H., & Goebel, R. (2023). Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions (pp. 1–19). [Google Scholar] [Crossref]

7. Aurangzeb, S., Aleem, M., Khan, M. T., Anwar, H., & Siddique, M. S. (Year). Cybersecurity for autonomous vehicles against malware attacks in smart-cities (pp. 2–12). [Google Scholar] [Crossref]

8. Ayres, N., Deka, L., & Paluszczyszyn, D. (2021). Continuous automotive software updates through container image layers. *Electronics, 10*(6), 739. https://doi.org/10.3390/electronics10060739 [Google Scholar] [Crossref]

9. Boddupalli, S., Rao, A. K., & Ray, S. (2022). Resilient cooperative adaptive cruise control for autonomous vehicles using machine learning. *IEEE Transactions on Intelligent Transportation Systems, 23*(9), 15655. https://doi.org/10.1109/tits.2022.3144599 [Google Scholar] [Crossref]

10. Catuogno, L., & Galdi, C. (2023). Secure firmware update: Challenges and solutions. *Cryptography, 7*(2), 30. https://doi.org/10.3390/cryptography7020030 [Google Scholar] [Crossref]

11. Greenberg, A. (2018, March 15). The untold story of NotPetya, the most devastating cyberattack in history. *Wired*. https://www.wired.com/story/notpetya-cyberattack-ukraine-russia-code-crashed-the-world/ [Google Scholar] [Crossref]

12. Henze, M., Popper, C., & Shabtai, A. (2018). Autonomous vehicles security: An overview. *IEEE Security & Privacy, 16*(1), 14–20. https://doi.org/10.1109/MSP.2018.2701161 [Google Scholar] [Crossref]

13. Horowitz, B., & Lucero, D. S. (2017). System‐aware cyber security: A systems engineering approach for enhancing cyber security. *Insight, 20*(3), 66. https://doi.org/10.1002/inst.12165 [Google Scholar] [Crossref]

14. Khan, S. K., Shiwakoti, N., Stasinopoulos, P., & Chen, Y. (2020). Cyber-attacks in the next-generation cars, mitigation techniques, anticipated readiness and future directions. *Accident Analysis & Prevention, 148*, 105837. https://doi.org/10.1016/j.aap.2020.105837 [Google Scholar] [Crossref]

15. Khattak, Z. H., Smith, B. L., & Fontaine, M. D. (2021). Impact of cyberattacks on safety and stability of connected and automated vehicle platoons under lane changes. *Accident Analysis & Prevention, 150*, 105861. https://doi.org/10.1016/j.aap.2020.105861 [Google Scholar] [Crossref]

16. Kojchev, S., Hult, R., & Fredriksson, J. (2022). Optimization based coordination of autonomous vehicles in confined areas. In *2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)* (p. 1957). https://doi.org/10.1109/itsc55140.2022.9922180 [Google Scholar] [Crossref]

17. Kshetri, N. (2018). Blockchain's roles in meeting key supply chain management objectives. *International Journal of Information Management, 39*, 80–89. https://doi.org/10.1016/j.ijinfomgt.2017.12.007 [Google Scholar] [Crossref]

18. Kucuk, Y., & Yilmaz, E. (2019). A survey of cybersecurity challenges in intelligent transportation systems. *Computers & Electrical Engineering, 74*, 444–462. [Google Scholar] [Crossref]

19. Kukkala, V. K., Thiruloga, S. V., & Pasricha, S. (2020). Roadmap for cybersecurity in autonomous vehicles. *Colorado State University* (pp. 1–8). [Google Scholar] [Crossref]

20. Kukkala, V. K., Thiruloga, S. V., & Pasricha, S. (2022). Roadmap for cybersecurity in autonomous vehicles. *arXiv (Cornell University)*. https://doi.org/10.48550/arXiv.2201 [Google Scholar] [Crossref]

21. McKeever, S., Kozlowski, D., & Venkateswaran, N. (2020). Cybersecurity in autonomous vehicles: A systematic review. *ACM Computing Surveys, 53*(6), 1–34. [Google Scholar] [Crossref]

22. Meissner, M. (2019). The future of autonomous vehicles and cybersecurity. *Journal of Cybersecurity, 5*(1), tyz002. https://doi.org/10.1093/cybsec/tyz002 [Google Scholar] [Crossref]

23. Miettinen, M., & Gasser, L. (2017). Security and privacy challenges in industrial Internet of Things. *ACM Transactions on Internet Technology, 17*(3), 1–24. [Google Scholar] [Crossref]

24. Radoglou-Grammatikis, P., Sarigiannidis, P., Moscholios, I., & Obaidat, M. S. (2018). Cybersecurity for connected autonomous vehicles: Adversarial machine learning, threats, and countermeasures. *IEEE Communications Magazine, 56*(12), 95–101. [Google Scholar] [Crossref]

25. Sadiku, M. N. O., Musa, S. M., & Ajayi-Majebi, A. (2021). Artificial intelligence in autonomous vehicles. *International Journal of Trend in Scientific Research and Development (IJTSRD), 5*(2), 715–720. [Google Scholar] [Crossref]

26. Savitha, P. B., & Madhu, S. (2023). Cyber security issues in connected autonomous vehicle. *International Journal of Research Publication and Reviews, 4*(3), 929–936. [Google Scholar] [Crossref]

27. Scalas, M., & Giacinto, G. (2019). Automotive cybersecurity: Foundations for next-generation vehicles (p. 1). https://doi.org/10.1109/ictcs.2019.8923077 [Google Scholar] [Crossref]

28. Wang, X., Lin, X., & Li, M. (2021). Aggregate modeling and equilibrium analysis of the crowdsourcing market for autonomous vehicles. *arXiv (Cornell University)*. https://doi.org/10.48550/arXiv.2102 [Google Scholar] [Crossref]

29. Youssef, A., Satam, S., Latibari, B. S., Pacheco, J., Salehi, S., Hariri, S., & Satam, P. (2024). Autonomous vehicle security: A deep dive into threat modeling. *arXiv (Cornell University)*. https://doi.org/10.48550/arxiv.2412.15348 [Google Scholar] [Crossref]

30. Zamindar, A. (2022). Artificial intelligence in self-driving cars research and innovation. *International Research Journal of Modernization in Engineering Technology and Science, 4*(3), 895–890. [Google Scholar] [Crossref]

Metrics

Views & Downloads

Similar Articles

© 2026 IJLTEMAS · RSIS International. All rights reserved. ISSN 2278-2540.