Enhancing Autonomous Vehicles Security: A Survey on GAT, Neural Networks, and Reinforcement Learning in AI-Driven Defense

Incorporating Artificial Intelligence (AI) in the fast development of Autonomous Vehicles (AVs) offers significant opportunities while presenting notable cybersecurity challenges and risks due to the complexity of the interconnected systems and the essential function of AI in operations. This paper presents a comprehensive survey investigating AI-driven defense mechanisms tailored for AV security with a particular emphasis on Graph Attention Networks (GAT), Long Short-Term Memory (LSTM) networks, and reinforcement learning (RL). This study investigates the way these technologies improve threat detection, anomaly identification, and decision-making in AVs. The discussion additionally addresses Vehicle-to-Vehicle and Vehicle-to-infrastructure communications risks by combining sensor fusion and Explainable AI (XAI). The survey examines the recent developments in real-time data analysis, intrusion detection, and zero-day attack mitigation. Key essential issues, including model interpretability, computational overhead, and data privacy, are extensively analysed. The paper discusses the necessity for scalable and interpretable AI frameworks that guarantee a flexible and reliable cybersecurity solution with the challenge of changing the Autonomous Vehicle (AV) landscapes. This study aims to direct future research in generating AI-driving security frameworks that defend and adapt to the dynamic threat environments of AVs.