Hawraa A. Habeeb

Proactive Deep Q-Learning Approach for Anomaly Detection in IoT IDSs

An offensive-defensive system based on Deep Reinforcement Learning (DRL) algorithms is proposed, which outperform several state-of-the-art machine learning approaches in the literature, revealing that the systematic incorporation of accurate data engineering and reinforcement learning frameworks generates a field-tested security barrier that offers an expedient reaction to counteract multifaceted threats to IoT networks.