The Impact of Deep Reinforcement Learning on Real-Time Intrusion Detection in IoT Environments

The interconnected smart equipment which can be produced at home, in the health care, transport, and critical infrastructure is made possible by the Internet of Things ecosystem, invented at a rapid pace. The fact that there is this connectivity that is accompanied by maximum levels of efficiency and convenience coupled with high levels of security lapses. The signature driven or rule based approach is no longer providing the timely and even effective as well as efficient defense as the IoT ecosystems start expanding in size and complexity. The present paper is a review of DRC systems intrusion detection and performance based on such important requirements as accuracy of detection, latency, adaptability and scalability. The findings show high likelihood of the DRM models improving the performance of the actual-time threat detectors to some level of the computational effectiveness. However, the problem of training overhead, the interpretability of the decisions, and the exploitation of energy of resource-constrained devices should also be mentioned before it spreads even more. Lastly, the research provides functionality information of the possibility of DRL as a potential security system to the IoT systems. This study can be regarded as a step towards smart, dynamic, and scalable cybersecurity devices, which once can be expanded to guarantee the survival of infrastructures based on IoT to a new level of cyber-attacks by exposing the advantages and disadvantages of using DRL models in practice.