Reinforcement Learning for Adaptive Cybersecurity

Abstract

As cyber threats become increasingly sophisticated, adaptive and intelligent cybersecurity
solutions are essential to detect, respond to, and mitigate these threats in real time.
Reinforcement learning (RL), a branch of machine learning, has emerged as a powerful
tool for developing adaptive cybersecurity systems capable of learning from their
environments and improving their defenses autonomously. This paper explores the
application of reinforcement learning in cybersecurity, including its use in intrusion
detection, automated threat hunting, dynamic defense strategies, and malware analysis. It
provides a comprehensive overview of various reinforcement learning techniques, such
as Q-learning, deep Q-networks (DQNs), and policy gradient methods, and discusses
their advantages in handling complex and evolving cyber-attack scenarios. The paper also
includes case studies and real-world examples to demonstrate the effectiveness of
reinforcement learning in adaptive cybersecurity, highlighting its potential to reduce
response times, enhance threat detection accuracy, and improve overall system resilience.
Additionally, the challenges associated with deploying reinforcement learning models,
such as computational demands, model interpretability, and the risk of adversarial
manipulation, are discussed. The paper concludes with future research directions,
including the integration of reinforcement learning with other advanced technologies like
federated learning, explainable AI, and quantum computing, to further enhance
cybersecurity defenses.