Building AI-Driven Frameworks for Real-Time Threat Detection and Mitigation in IoT Networks

The fast growth of Internet of Things (IoT) networks has made security risks more complicated and numerous. To find and stop these threats in real time, we need new and creative solutions. This article talks about an AI-powered system that can make IoT networks safer by finding and stopping threats as they happen. The system uses both machine learning (ML) and deep learning (DL) to look at real-time data from IoT devices and find strange patterns that could be signs of an attack. This is done by using advanced algorithms, like decision trees, neural networks, and reinforcement learning, to learn new things all the time and adjust to new dangers. To improve the efficiency of identification and cut down on false positives, a mixed method is used that combines controlled and unstructured learning. The system also has an automatic mitigating procedure that separates infected devices and stops them from doing more damage when risks are found. Compared to standard security methods, our results show that the proposed structure makes danger identification and prevention much more effective in IoT networks. The system is very good at finding different kinds of threats, like DDoS, malware, and illegal access, and it does this while using very little computing power. This means it can be used in IoT settings with limited resources. This study provides a flexible and scalable way to protect the IoT environment, which is becoming more and more linked.