Adversarially Robust AI for Real-Time Cyber Threat Detection: A Reinforcement Learning Approach

As cyber threats become increasingly sophisticated, particularly adversarial attacks targeting machine learning models, traditional cybersecurity defenses face significant challenges. Conventional Intrusion Detection Systems (IDS) and anomaly detection methods often struggle against adaptive threats designed to bypass detection mechanisms. This paper introduces a novel reinforcement learning (RL)-based cybersecurity model that enhances adversarial robustness in real-time threat detection. Unlike static machine learning models that remain vulnerable to adversarial manipulations, our approach utilizes adaptive AI agents capable of continuous learning and self-improvement in dynamic cybersecurity environments. The proposed framework integrates deep reinforcement learning (DRL) with adversarial training to strengthen detection mechanisms against evolving cyber threats. By employing policy gradient optimization and reward-based adaptation, the system dynamically adjusts its defense strategies, significantly improving its resilience against adversarial intrusions. Experimental results demonstrate that this approach achieves superior threat detection accuracy, lower false positive rates, and heightened robustness against adversarial attacks compared to traditional machine learning-based security models. This research contributes to the advancement of adversarial machine learning, intelligent cybersecurity automation, and autonomous cyber defense, aligning with the growing need for self-learning security mechanisms in critical infrastructure protection. The proposed RL-based model lays the groundwork for next-generation cybersecurity solutions, enabling AI-driven security frameworks to proactively counter emerging cyber threats in real-time.