Reinforcement Learning Algorithms for Adaptive Cyber Defence Systems: A Proactive Approach

Abstract

In an increasingly interconnected digital landscape, cybersecurity remains a critical concern, with adversaries continuously refining their methods to exploit vulnerabilities. Traditional cybersecurity measures, often reactive in nature, are insufficient to counter sophisticated, evolving threats. This paper proposes a framework for adaptive cyber defense systems that leverage reinforcement learning (RL) algorithms. Reinforcement learning, a subset of machine learning, focuses on training systems to make sequential decisions through trial and error, thereby optimizing performance over time. When applied to cybersecurity, RL can enable defense systems to proactively respond to emerging threats, evolving their defense mechanisms in real-time based on new information and past experiences. The framework we propose outlines various RL algorithms, such as Q-learning, deep Q-networks (DQN), and proximal policy optimization (PPO), and their applications in cybersecurity. The discussion highlights real-world scenarios where RL-driven adaptive defenses could mitigate attacks, such as distributed denial-of-service (DDoS) attacks, malware propagation, and phishing schemes. We also explore the challenges, including computational complexity and system scalability, while proposing strategies to address them. The framework illustrates how a proactive, adaptive approach using reinforcement learning can revolutionize the field of cyber defense by enhancing threat detection, response times, and overall system resilience.