A Heterogenous IoT Attack Detection Through Deep Reinforcement Learning: A Dynamic ML Approach

This paper presents an innovative Intrusion Detection System (IDS) architecture using Deep Reinforcement Learning (DRL). To accomplish this, we started by analysing the DRL issue for IoT devices, followed by designing intruder attacks using Label Flipping Attack (LFA). We propose an artificial intelligence DRL model to imitate IoT attack detection, along with two defence strategies: Label-based Semi-supervised Defence (LSD) and Clustering-based Semi-supervised Defence (CSD). Finally, we provide the evaluation results of the adaptive attack and defence models on multiple IoT scenarios with the NSL-KDD, IoT-23, and NBaIoT datasets. The research proves that DRL functions effectively with dynamically produced traffic in contrast to existing conventional techniques.