Enhancing Cyber-Attack Detection in IoT Networks through Deep Reinforcement Learning

Security of connected devices and networks is becoming an increasingly important concern as the ecosystem of the Internet of Things (IoT) continues to grow. It is difficult for traditional cybersecurity tactics to keep up with the ever-changing threat landscape and the complexity of surroundings that are associated with the IoT. Deep reinforcement learning (DRL) is the method that we propose use in this paper to improve the detection of cyberattacks in IoT networks. Our platform, which allows for intelligent decision-making and adaptive responses to cyber threats in real time, is able to leverage the capabilities of DRL. An environment modelling, state representation, action space definition, reward design, algorithm selection, training, evaluation, deployment, continuous learning, and security considerations are all included in the conceptual framework that we present for the implementation of DRL-based cyber-attack detection systems in IoT networks. Our suggested method is shown to be successful and robust in detecting a variety of cyber threats, while simultaneously minimising the number of false positives and false negatives. This is demonstrated through rigorous experimentation and evaluation. The results of our study shed light on the usefulness of DRL as a potent instrument for strengthening cybersecurity in IoT environments and open the way for additional research in this rapidly developing subject.