Research Article
Preventing Adversarial Attacks on Autonomous Driving Models
@INPROCEEDINGS{10.1007/978-3-031-27041-3_1, author={Junaid Sajid and Bareera Anam and Hasan Ali Khattak and Asad Waqar Malik and Assad Abbas and Samee U. Khan}, title={Preventing Adversarial Attacks on Autonomous Driving Models}, proceedings={Wireless Internet. 15th EAI International Conference, WiCON 2022, Virtual Event, November 2022, Proceedings}, proceedings_a={WICON}, year={2023}, month={2}, keywords={Autonomous driving models Autonomous driving system Adversarial attacks Support vector machine}, doi={10.1007/978-3-031-27041-3_1} }- Junaid Sajid
Bareera Anam
Hasan Ali Khattak
Asad Waqar Malik
Assad Abbas
Samee U. Khan
Year: 2023
Preventing Adversarial Attacks on Autonomous Driving Models
WICON
Springer
DOI: 10.1007/978-3-031-27041-3_1
Abstract
Autonomous driving systems are among the exceptional technological developments of recent times. Such systems gather live information about the vehicle and respond with skilled human drivers’ skills. The pervasiveness of computing technologies has also resulted in serious threats to the security and safety of autonomous driving systems. Adversarial attacks are among one the most serious threats to autonomous driving models (ADMs). The purpose of the paper is to determine the behavior of the driving models when confronted with a physical adversarial attack against end-to-end ADMs. We analyze some adversarial attacks and their defense mechanisms for certain autonomous driving models. Five adversarial attacks were applied to three ADMs, and subsequently analyzed the functionality and the effects of these attacks on those ADMs. Afterward, we propose four defense strategies against five adversarial attacks and identify the most resilient defense mechanism against all types of attacks. Support Vector Machine and neural regression were the two machine learning models that were utilized to categorize the challenges for the model’s training. The results show that we have achieved 95% accuracy.
