Deep Reinforcement Learning for Adaptive Cyber Defense in Autonomous Vehicle Networks: Utilizes deep reinforcement learning to develop adaptive cyber defense mechanisms for AV networks
Keywords:
Cybersecurity, Resilience, ReliabilityAbstract
Autonomous Vehicle (AV) networks are increasingly susceptible to cyber threats, requiring sophisticated defense mechanisms to ensure their security and reliability. Traditional cybersecurity approaches often fall short in addressing the dynamic and evolving nature of cyber-attacks. Deep Reinforcement Learning (DRL) offers a promising avenue for developing adaptive cyber defense systems that can autonomously detect and mitigate threats in AV networks. This paper explores the application of DRL in designing adaptive cyber defense mechanisms for AV networks, focusing on enhancing security, resilience, and reliability. We present a comprehensive overview of the current challenges in securing AV networks and discuss how DRL can be leveraged to address these challenges. We also propose a framework for implementing DRL-based cyber defense systems in AV networks and highlight potential research directions in this emerging field.
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