Multi-Agent Actor-Critics in Autonomous Cyber Defense

TL;DR

This paper investigates how Multi-Agent Actor-Critic Deep Reinforcement Learning can improve autonomous cyber defense by enabling multiple agents to collaboratively detect and respond to cyber threats in simulated scenarios.

Contribution

It introduces the application of Multi-Agent Actor-Critic algorithms to cyber defense, demonstrating their effectiveness in simulated attack scenarios.

Findings

MADRL enhances autonomous cyber defense capabilities.

Agents learn quickly and respond effectively to threats.

Results show significant improvement over traditional methods.

Abstract

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.