Autonomous Cyber Defense Systems Using Reinforcement Learning

Abstract

With the increasing frequency and sophistication of cyber threats, traditional defense mechanisms struggle to keep pace. Autonomous Cyber Defense Systems (ACDS) powered by Reinforcement Learning (RL) offer promising solutions by enabling adaptive and intelligent responses to evolving threats. This paper explores the theoretical foundations of RL, its application in ACDS, the architecture of RL-based cyber defense systems, and the challenges and future prospects of implementing these systems in real-world scenarios.