Adversarial Machine Learning for Robust Cybersecurity in Autonomous Vehicle Systems: Investigates the use of adversarial machine learning to enhance cybersecurity in autonomous vehicle systems
Keywords:
Adversarial Machine Learning, RobustnessAbstract
Adversarial Machine Learning (AML) has emerged as a critical approach for enhancing the cybersecurity of Autonomous Vehicle (AV) systems. This paper explores the application of AML techniques to defend AVs against cyber threats, focusing on the development of robust models capable of detecting and mitigating adversarial attacks. The research investigates various types of attacks, including data poisoning, evasion, and model inversion attacks, and proposes novel defense mechanisms using AML. Experimental results demonstrate the effectiveness of the proposed approach in improving the resilience of AV systems against cyber threats.
Downloads
References
Gao, J., Zhang, C., & Lin, Y. (2020). Deep reinforcement learning for defending autonomous vehicles against adversarial attacks. IEEE Transactions on Intelligent Transportation Systems, 21(4), 1488-1497.
Liang, X., Li, H., & Zhang, S. (2019). Adversarial training for robust autonomous vehicle control. IEEE Transactions on Vehicular Technology, 68(11), 11002-11013.
Smith, A., & Jones, B. (2018). Enhancing cybersecurity of autonomous vehicles using adversarial machine learning. Journal of Autonomous Vehicles, 5(2), 87-98.
Tatineni, Sumanth. "INTEGRATING AI, BLOCKCHAIN AND CLOUD TECHNOLOGIES FOR DATA MANAGEMENT IN HEALTHCARE." Journal of Computer Engineering and Technology (JCET) 5.01 (2022).
Zhang, L., Wang, J., & Li, Q. (2019). Defensive distillation for secure autonomous vehicle communication. IEEE Transactions on Vehicular Technology, 68(9), 9087-9096.
Vemoori, V. “Towards Secure and Trustworthy Autonomous Vehicles: Leveraging Distributed Ledger Technology for Secure Communication and Exploring Explainable Artificial Intelligence for Robust Decision-Making and Comprehensive Testing”. Journal of Science & Technology, vol. 1, no. 1, Nov. 2020, pp. 130-7, https://thesciencebrigade.com/jst/article/view/224.
Chen, H., & Liu, W. (2020). Adversarial machine learning for autonomous vehicle cybersecurity: A survey. IEEE Transactions on Intelligent Transportation Systems, 21(5), 1933-1947.
Kim, S., & Lee, J. (2019). Deep learning for defending autonomous vehicles against adversarial attacks. Journal of Intelligent Vehicles, 7(3), 214-225.
Li, X., Zhang, Y., & Wang, L. (2017). Secure autonomous vehicle control using adversarial machine learning. IEEE Transactions on Control Systems Technology, 25(5), 1743-1754.
Zhang, Q., Wang, Z., & Liu, Y. (2018). Adversarial attacks and defenses in autonomous vehicle systems: A comprehensive survey. IEEE Transactions on Vehicular Technology, 67(11), 10647-10661.
Wang, H., Zhang, X., & Chen, Y. (2019). Robust deep learning for autonomous vehicle navigation in adversarial environments. IEEE Transactions on Intelligent Transportation Systems, 20(6), 2339-2348.
Liu, J., & Wang, F. (2020). Defensive distillation for secure autonomous vehicle perception. IEEE Transactions on Intelligent Vehicles, 5(4), 301-311.
Smith, A., & Brown, C. (2018). Feature squeezing for enhancing cybersecurity of LiDAR sensors in autonomous vehicles. IEEE Sensors Journal, 18(8), 3274-3283.
Zhang, L., Wang, J., & Li, Q. (2019). Defensive distillation for secure autonomous vehicle communication. IEEE Transactions on Vehicular Technology, 68(9), 9087-9096.
Chen, H., & Liu, W. (2020). Adversarial machine learning for autonomous vehicle cybersecurity: A survey. IEEE Transactions on Intelligent Transportation Systems, 21(5), 1933-1947.
Kim, S., & Lee, J. (2019). Deep learning for defending autonomous vehicles against adversarial attacks. Journal of Intelligent Vehicles, 7(3), 214-225.
Li, X., Zhang, Y., & Wang, L. (2017). Secure autonomous vehicle control using adversarial machine learning. IEEE Transactions on Control Systems Technology, 25(5), 1743-1754.
Zhang, Q., Wang, Z., & Liu, Y. (2018). Adversarial attacks and defenses in autonomous vehicle systems: A comprehensive survey. IEEE Transactions on Vehicular Technology, 67(11), 10647-10661.
Wang, H., Zhang, X., & Chen, Y. (2019). Robust deep learning for autonomous vehicle navigation in adversarial environments. IEEE Transactions on Intelligent Transportation Systems, 20(6), 2339-2348.
Liu, J., & Wang, F. (2020). Defensive distillation for secure autonomous vehicle perception. IEEE Transactions on Intelligent Vehicles, 5(4), 301-311.
