Vol. 2 No. 1 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Adaptive Cybersecurity Policies for Autonomous Vehicle Systems: A Machine Learning Approach

PDF
  • Dr. Felipe Bustamante
Dr. Felipe Bustamante
Associate Professor of Industrial Engineering, University of Santiago de Chile
Cover

Published 24-06-2024

Keywords

  • autonomous vehicle,
  • AI
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Felipe Bustamante, “Adaptive Cybersecurity Policies for Autonomous Vehicle Systems: A Machine Learning Approach”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 1, pp. 1–24, Jun. 2024, Accessed: Sep. 18, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/49

Abstract

Autonomous vehicles (AV) are an emerging technology that has driven the next wave of urban development. This futuristic technology may reshape the current transportation approach and help address some challenging societal issues. One of the key technological advancements behind the success of AV is its centralized decision engine, a complex system that collects and processes data from various sensors and takes driving commands. Computational advances in deep learning and machine learning algorithms in the past decade have largely improved the quality of centralized decision engines. Unfortunately, performance is not the only matter of the decision engine. The vulnerabilities and security concerns associated with a centralized decision engine could allow adversaries to manipulate an AV system in favor of their destructive goals. Governments and research communities have to take necessary actions to update the policies, standards, best practices, and regulations that effectively mitigate the security concerns associated with the centralized decision engine of AVs. The potential use of AI in both offensive and adversarial applications to stimulate user behaviors, and the lack of an effective AI alarm clock to turn adaptive policies on, necessitate proactively proposed policies that are, to a certain extent, adaptive in nature.

PDF

Downloads

Download data is not yet available.

References

  1. S. Chen, L. Zhang, Q. Liu, and Y. Zhang, "Adaptive Cybersecurity Mechanisms for Autonomous Vehicles," IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1234-1245, March 2021.
  2. M. Peng, X. Xu, and H. Liu, "Machine Learning-Based Cybersecurity for Connected Vehicles," IEEE Transactions on Vehicular Technology, vol. 69, no. 6, pp. 5872-5884, June 2020.
  3. X. Shen, Z. Sun, and Y. Wang, "Dynamic Cybersecurity Policy Adaptation in Autonomous Vehicle Networks," IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4500-4511, May 2020.
  4. T. Li, J. Liu, and S. Hu, "Reinforcement Learning for Cyber Defense in Autonomous Vehicle Networks," IEEE Transactions on Network and Service Management, vol. 17, no. 2, pp. 812-824, June 2020.
  5. K. Zhang, Q. Zhu, and S. Bu, "Deep Learning-Driven Intrusion Detection for In-Vehicle Networks," IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 1314-1323, January 2020.
  6. Tatineni, Sumanth. "Beyond Accuracy: Understanding Model Performance on SQuAD 2.0 Challenges." International Journal of Advanced Research in Engineering and Technology (IJARET) 10.1 (2019): 566-581.
  7. Vemoori, Vamsi. "Comparative Assessment of Technological Advancements in Autonomous Vehicles, Electric Vehicles, and Hybrid Vehicles vis-à-vis Manual Vehicles: A Multi-Criteria Analysis Considering Environmental Sustainability, Economic Feasibility, and Regulatory Frameworks." Journal of Artificial Intelligence Research 1.1 (2021): 66-98.
  8. 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.
  9. Vemori, Vamsi. "Human-in-the-Loop Moral Decision-Making Frameworks for Situationally Aware Multi-Modal Autonomous Vehicle Networks: An Accessibility-Focused Approach." Journal of Computational Intelligence and Robotics 2.1 (2022): 54-87.
  10. Y. Yang, H. Li, and X. Li, "Adaptive Security Framework for Autonomous Vehicles Using Machine Learning," IEEE Access, vol. 8, pp. 56789-56800, 2020.
  11. J. Park, H. Kim, and K. Lee, "Privacy-Preserving Data Analytics for Autonomous Vehicle Systems," IEEE Transactions on Information Forensics and Security, vol. 15, pp. 4531-4542, December 2020.
  12. S. Wang, X. Zhang, and Z. Zhang, "Adaptive Machine Learning for Enhanced Cybersecurity in Autonomous Vehicles," IEEE Transactions on Vehicular Technology, vol. 68, no. 7, pp. 6702-6713, July 2019.
  13. R. Yu, Y. He, and X. Shen, "Distributed Learning-Based Cybersecurity in IoT-Enabled Autonomous Vehicles," IEEE Journal on Selected Areas in Communications, vol. 38, no. 1, pp. 63-75, January 2020.
  14. H. Sun, B. Peng, and Y. He, "Secure Communication Protocols for Autonomous Vehicle Networks," IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 8456-8467, August 2020.
  15. G. Liu, L. Duan, and H. Li, "Adaptive Threat Detection in Autonomous Vehicles Using Machine Learning," IEEE Transactions on Information Forensics and Security, vol. 14, no. 11, pp. 2987-2998, November 2019.
  16. A. Chen, L. Zhao, and J. Ma, "Cyber-Physical Security for Autonomous Vehicle Networks," IEEE Transactions on Cybernetics, vol. 50, no. 9, pp. 4005-4016, September 2020.
  17. Z. Liu, Q. Li, and S. Yang, "AI-Driven Adaptive Cybersecurity Solutions for Autonomous Vehicles," IEEE Transactions on Artificial Intelligence, vol. 1, no. 2, pp. 124-135, December 2020.
  18. F. Wu, X. Zhang, and J. Wu, "Real-Time Anomaly Detection in Autonomous Vehicles Using Deep Learning," IEEE Transactions on Industrial Informatics, vol. 16, no. 5, pp. 3238-3249, May 2020.
  19. M. Lin, J. Wang, and H. Wu, "Federated Learning for Cybersecurity in Autonomous Vehicle Networks," IEEE Transactions on Vehicular Technology, vol. 70, no. 4, pp. 3507-3519, April 2021.
  20. C. Tang, Y. Zhang, and X. Wang, "Privacy-Aware Cybersecurity Framework for Autonomous Vehicles," IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7102-7113, August 2020.
  21. Y. Chen, T. Huang, and S. Li, "Machine Learning-Based Cyber Resilience in Autonomous Vehicle Systems," IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 3, pp. 1232-1243, May-June 2021.
  22. J. Zhang, W. Liu, and P. Zhang, "Adaptive Intrusion Detection for Autonomous Vehicle Networks," IEEE Transactions on Vehicular Technology, vol. 69, no. 9, pp. 10345-10356, September 2020.
  23. K. Wang, X. Li, and Y. Liu, "AI-Enhanced Cybersecurity for Connected and Autonomous Vehicles," IEEE Transactions on Intelligent Vehicles, vol. 5, no. 4, pp. 736-747, December 2020.
  24. B. Liu, L. Wang, and H. Wu, "Adaptive Learning-Based Cybersecurity for Autonomous Vehicles," IEEE Transactions on Vehicular Technology, vol. 70, no. 5, pp. 4512-4523, May 2021.

Similar Articles

You may also start an advanced similarity search for this article.