Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation
The framework achieved 99.54% attack mitigation rate, demonstrating strong adaptive and real-time defensive capabilities and demonstrating the potential of reinforcement learning as a powerful and scalable approach for autonomous cybersecurity in modern cloud environments.