Multi-Agent Systems for Coordinated Cyber Defense: A Reinforcement Learning Approach

Abstract

This paper explores the application of multi-agent systems (MAS) combined with reinforcement learning (RL) to enhance coordinated cyber defense mechanisms. We investigate how MAS frameworks can be leveraged to improve threat detection and response in dynamic cybersecurity environments. The paper outlines the key challenges, presents a novel RL-based approach for coordinating multiple agents, and discusses the effectiveness of this approach through simulations and empirical evaluations.