Building Autonomous Cybersecurity Systems with Artificial Intelligence: Reinforcement Learning Approaches for Self-Healing and Adaptive Network Defense

Abstract

Autonomous cybersecurity systems are essential in mitigating the escalating sophistication and scale of cyber threats. Artificial Intelligence (AI), particularly Reinforcement Learning (RL), offers promising methodologies to enhance self-healing and adaptive defense capabilities within network infrastructures. This paper investigates the integration of RL in the development of autonomous cybersecurity systems, emphasizing their application in self-healing and adaptive network defense mechanisms. By leveraging RL, systems can learn optimal strategies for detecting, responding to, and recovering from cyberattacks with minimal human intervention. We explore the design and deployment of RL models in dynamic threat environments, focusing on challenges such as scalability, real-time decision-making, and robustness against adversarial tactics. The study also examines the role of simulation environments in training RL agents, highlighting their importance in replicating complex network conditions. Additionally, this paper discusses the synergy between RL and other AI paradigms, such as deep learning and graph neural networks, to address specific cybersecurity challenges. Our findings demonstrate that RL-based approaches significantly improve the resilience of networked systems by enabling rapid adaptation and proactive mitigation strategies. The conclusion outlines future directions for research, emphasizing the need for standardized evaluation metrics, advanced simulation frameworks, and enhanced interpretability of RL-based decisions.