Leveraging Reinforcement Learning for Autonomous Cyber Defense Systems

Abstract

As cyberattacks grow in frequency, scale, and sophistication, traditional defense mechanisms are struggling to keep pace. Autonomous cyber defense systems have emerged as a promising solution, capable of adapting to new threats in real-time. Reinforcement learning (RL), a branch of machine learning, provides a framework for training agents to make decisions in complex environments, which is critical for autonomous cyber defense. This paper explores how RL can be leveraged to build adaptive, self-learning cyber defense systems capable of identifying, responding to, and mitigating threats with minimal human intervention.