An Adversarial Environment Reinforcement Learning-driven Intrusion Detection Algorithm for Internet of Things

Abstract

The increasing prevalence of Internet of Things (IoT) systems has made them attractive targets for malicious actors. To address the evolving threats and the growing complexity of detection, there is a critical need to search for and develop new algorithms that are fast and robust in detecting and classifying dangerous network traffic. In this context, Deep Reinforcement Learning (DRL) is gaining recognition as a prospective solution in numerous fields as it enables autonomous agents to cooperate with their environment for decision-making without relying on human experts. This article presents a novel approach to intrusion detection in IoT systems through the utilization of an adversarial reinforcement learning (RL) algorithm, known for its exceptional predictive capabilities. The predictive process relies on a classifier, which is implemented as a streamlined and highly efficient neural network. Embedded within this classifier is a policy function, meticulously trained using an innovative RL model. Importantly, this model ensures that the environment’s behavior is dynamically fine-tuned simultaneously with the learning process, improving the overall effectiveness of the intrusion detection approach. The efficiency of our proposal was assessed using the Bot-IoT database, consisting of a mixture of legitimate IoT network traffic and simulated attack scenarios. Our scheme shows superior performance compared to existing ones. Therefore, our approach to IoT intrusion detection can be considered a valuable alternative to existing methods, capable of significantly improving the IoT systems’ security.