Integrating IoT and Machine Learning to Provide Intelligent Security in Smart Homes

Abstract

The technology is built entirely on the IoT and ML methods and is very desired in the sector of security. This method allows consumers to secure and regulate their homes. The system is used to track the treats in real time with response mechanism that detect with different sensor and send feed security information through internet. Smart home offer intelligent, real-time threat detection and response capabilities by utilizing decision tree, transfer learning logistic regression and SVM models.  Smart home collects, preprocessed and trained data from many IoT sensors, such as cameras and motion detectors and then apply Ml models to detect the threads and perform the action according to requirements. A total accuracy of 0.992 indicated the model’s strong performance, confirming its suitability for practical use. Decision tree and transfer learning models perform higher Accuracy rates than SVM and logistic regression. These findings highlight the great potential of fusing IoT and machine learning technologies to produce flexible, effective, and scalable security solutions. They also offer a strong foundation for the implementation of dependable and high-performing machine learning models in real-world smart home security systems.