AI-Enabled Adaptive Cybersecurity Response Using Reinforcement Learning

Abstract

Cyber threats are evolving in complexity and frequency, rendering traditional cybersecurity response mechanisms insufficient. Conventional rule-based and supervised machine learning (ML) models struggle to adapt to novel attack patterns, leaving security systems vulnerable to emerging threats. Reinforcement learning (RL) offers a promising approach to adaptive cybersecurity by enabling systems to learn optimal defense strategies through continuous interaction with adversarial environments. This study explores an RL-based cybersecurity response framework that dynamically adjusts mitigation strategies based on real-time threat intelligence. The proposed model leverages deep Q-networks (DQN) and proximal policy optimization (PPO) to enhance automated threat detection, response efficiency, and adaptability to evolving attack vectors.

The research evaluates the performance of RL-driven security automation through simulated attack scenarios, including distributed denial-of-service (DDoS) attacks, ransomware propagation, and zero-day exploits. The findings demonstrate that the RL model significantly improves incident response time, reduces false positives, and enhances overall threat mitigation success rates compared to traditional security frameworks. Additionally, the study identifies key challenges associated with RL-based cybersecurity, including computational overhead, adversarial vulnerabilities, and model interpretability. The results suggest that RL-driven security frameworks can serve as a viable alternative to static security models, offering organizations a scalable, self-learning defense mechanism against advanced cyber threats.