01518nas a2200145   4500000000100000008004100001260000900042100001800051700001500069700001500084245011500099856005900214520108500273022001401358       2023                            d  c20231 aMansi Girdhar1 aJunho Hong1 aJohn Moore00aCybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models  uhttps://ieeexplore.ieee.org/abstract/document/100974553 aAutonomous driving (AD) has developed tremendously in parallel with the ongoing development and improvement of deep learning (DL) technology. However, the uptake of artificial intelligence (AI) in AD as the core enabling technology raises serious cybersecurity issues. An enhanced attack surface has been spurred on by the rising digitization of vehicles and the integration of AI features. The performance of the autonomous vehicle (AV)-based applications is constrained by the DL models' susceptibility to adversarial attacks despite their great potential. Hence, AI-enabled AVs face numerous security threats, which prevent the large-scale adoption of AVs. Therefore, it becomes crucial to evolve existing cybersecurity practices to deal with risks associated with the increased uptake of AI. Furthermore, putting defense models into practice against adversarial attacks has grown in importance as a field of study amongst researchers. Therefore, this study seeks to provide an overview of the most recent adversarial defensive and attack models developed in the domain of AD.  a2644-1330