Abstract

The exponential growth of cyber threats in the digital landscape necessitates the development of autonomous defense mechanisms capable of real-time threat detection and mitigation. This research presents a comprehensive examination of autonomous cyber defense agents utilizing reinforcement learning (RL) methodologies to address the dynamic nature of modern cyber threats. Through extensive analysis of current literature and empirical studies, this work demonstrates how RL-based agents can adapt to evolving attack patterns, make autonomous decisions, and provide scalable defense solutions for complex network infrastructures. The findings indicate that multi-agent reinforcement learning frameworks show significant promise in enhancing cybersecurity posture while reducing human intervention requirements in critical defense scenarios.

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