The Internet of Things will become commonplace by making global connections possible at any moment. The necessity of well-thought-out, carefully implemented, and strictly enforced security standards over the entire lifecycle of IoT devices cannot be overstated.The Internet of Things (IoT) is a relatively new phenomenon that connects disparate computing infrastructures and infrastructure components. Given that the vast majority of the data collected will be shared with an unknowable audience, security is of paramount importance when connecting multiple independent IoT units across the Internet. This article provides a comprehensive review of the state of security in the Internet of Things.The essay emphasizes the necessity to provide security in the device itself alongside conventional security solutions to offer a method employing machine learning exible for preventing, detecting, diagnosing, isolating, and counteracting successful breaches.The bulk of IoT end hosts are low-end devices, This means that many common security practices cannot be used to protect IoTdevices., leaving IoT services and the wider Internet vulnerable to attacks and exploits.To solve this problem, this article presents a unified IoT framework that employs machine learning to implementthe proposed GNRS&NC architecture. This framework's primary goal is to ensure the safety of IoT devices.The framework makes use of random forest classifier. The suggested architecture allows for the seamless incorporation of regional IoT infrastructures into global frameworks without compromising on usability, interoperability, or security.

IoT, Security, Machine Learning, Random Forest, Architecture, Framework