Self-Learning Systems for Adaptive Cybersecurity in Autonomous Vehicles - A Deep Reinforcement Learning Approach: Explores self-learning systems for adaptive cybersecurity in AVs, utilizing deep reinforcement learning techniques

Abstract

Self-Learning Systems for Adaptive Cybersecurity in Autonomous Vehicles: A Deep Reinforcement Learning Approach

As autonomous vehicles (AVs) become more prevalent, ensuring their cybersecurity is paramount. Traditional cybersecurity measures often struggle to keep pace with evolving threats. This paper proposes a novel approach to cybersecurity in AVs using self-learning systems, specifically deep reinforcement learning (DRL). DRL has shown remarkable success in complex decision-making tasks and could be a game-changer in the cybersecurity domain. This paper explores the application of DRL for adaptive cybersecurity in AVs, aiming to create a self-learning system that can adapt to new threats in real-time.

We begin by providing an overview of the cybersecurity challenges facing AVs, highlighting the limitations of current approaches. We then delve into the fundamentals of DRL, explaining how it can be applied to cybersecurity. Next, we present a conceptual framework for integrating DRL into AV cybersecurity, outlining the components of the system and how they interact. We also discuss the training and evaluation of the DRL model, emphasizing the need for realistic simulations to capture the complexity of real-world threats.

To demonstrate the feasibility of our approach, we present a case study where we simulate a cyber attack on an AV and show how the DRL system can adapt to mitigate the attack. Our results indicate that the DRL system is capable of quickly adapting to new threats, outperforming traditional cybersecurity measures in terms of speed and effectiveness. Finally, we discuss the implications of our findings and suggest future research directions in this exciting field.

Overall, this paper contributes to the growing body of research on cybersecurity in AVs by proposing a novel approach that leverages the power of DRL. Our work opens up new possibilities for creating adaptive cybersecurity systems that can keep pace with the ever-changing threat landscape, ultimately enhancing the safety and security of AVs.