Multi-Agent Actor-Critics in Autonomous Cyber Defense
This work addresses cybersecurity challenges for organizations needing adaptive defense, but it appears incremental as it applies existing MADRL methods to a new domain without major methodological innovations.
The paper tackles the problem of enhancing autonomous cyber defense against evolving threats by applying Multi-Agent Actor-Critic algorithms, demonstrating that agents can learn quickly and counter threats in simulated scenarios, with results indicating significant capability improvements.
The need for autonomous and adaptive defense mechanisms has become paramount in the rapidly evolving landscape of cyber threats. Multi-Agent Deep Reinforcement Learning (MADRL) presents a promising approach to enhancing the efficacy and resilience of autonomous cyber operations. This paper explores the application of Multi-Agent Actor-Critic algorithms which provides a general form in Multi-Agent learning to cyber defense, leveraging the collaborative interactions among multiple agents to detect, mitigate, and respond to cyber threats. We demonstrate each agent is able to learn quickly and counter act on the threats autonomously using MADRL in simulated cyber-attack scenarios. The results indicate that MADRL can significantly enhance the capability of autonomous cyber defense systems, paving the way for more intelligent cybersecurity strategies. This study contributes to the growing body of knowledge on leveraging artificial intelligence for cybersecurity and sheds light for future research and development in autonomous cyber operations.