Multi-Agent Deep Reinforcement Learning Applications in Cybersecurity: Challenges and Perspectives
Zakaria Tolba, Nour El Houda Dehimi, Stéphane Galland, Soufiene Boukelloul, Djaber Guassmi · 2024
This paper explores the perspectives and challenges associated with the application of Multi-Agent Deep Reinforcement Learning (MADRL) in the field of cybersecurity. As cyber threats continue to evolve in complexity, the integration of MADRL techniques offers promising solutions for enhancing security measures. The paper delves into various perspectives surrounding the implementation of MADRL in cybersecurity applications, highlighting its potential benefits. Additionally, it addresses the challenges and obstacles faced in the deployment of such advanced techniques, emphasizing the need for further research and development to overcome these hurdles. The findings contribute to a comprehensive understanding of the landscape, paving the way for the effective integration of MADRL in cybersecurity frameworks. The paper outlines the growing significance of MAS and DRL, examines their current applications in cybersecurity, discusses the associated challenges, and provides insights into future directions. By investigating decentralized threat intelligence sharing, privacy-preserving collaboration, robustness against adversarial attacks, and crossdomain collaboration, this research aims to illuminate the path toward a more secure and resilient cyber future.