Cyber-Physical Attacks and Defenses in Autonomous Vehicles - A Deep Learning Approach: Analyzes cyber-physical attacks and defenses in AVs, employing a deep learning-based defense mechanism

Abstract

Autonomous Vehicles (AVs) are at the forefront of modern transportation, promising increased safety and efficiency. However, their reliance on interconnected systems and sensors makes them vulnerable to cyber-physical attacks. This paper examines the landscape of cyber-physical attacks on AVs and proposes a deep learning-based defense mechanism. We first discuss the types and impacts of such attacks, highlighting the need for robust defenses. Next, we introduce a deep learning approach for detecting and mitigating cyber-physical attacks in real-time. Our proposed system leverages the power of deep neural networks to analyze sensor data and identify anomalies indicative of attacks. We evaluate the effectiveness of our approach through simulations and discuss its implications for securing future AVs.