Innovations in Cyber Defense with Deep Reinforcement Learning: A Concise and Contemporary Review

Abstract

This study presents a concise review for exploring the burgeoning intersection of Deep Reinforcement Learning (DRL) and cybersecurity, delving into its basics, applications, and open challenges. In particular, DRL is introduced as a dynamic approach to cybersecurity, permitting adaptive threat detection, intrusion prevention, and occurrence response through continuous learning and decision-making. Nevertheless, there are many technical, operational, and ethical challenges that obstruct its widespread adoption, including data scarcity, computational complexity, vulnerability to adversarial attacks, and privacy concerns. To deal with these obstacles, researchers and practitioners must work together to come up with strong and ethical DRL-based security solutions. However, despite these difficulties, integrating DRL into cybersecurity frameworks may be a promising way to improve resilience against evolving cyber threats. Through tackling its limitations and utilizing its promise, we can create a more robust, quick-reacting cyberspace.