Leveraging Machine Learning for Real-time Anomaly Detection and Self-Repair in IoT Devices

The Internet of Things (IoT) gadgets have completely transformed a number of different businesses by producing enormous amounts of data. A machine learning framework for real-time Internet of Things anomaly detection and self-repair is presented in this research for the first time. Incorporating data preparation, anomaly detection, and selfrepair are all components of the framework. Through the use of data preparation module normalization and dimensionality reduction, Internet of Things data analysis can be improved. Through the utilization of deep neural networks, support vector machines, and several other machine learning models that have been trained on previous data, abnormal behavior can be identified. The reliability of ensemble approaches is increased. The self-repair module is responsible for automatically repairing abnormalities by resetting the settings of the device, upgrading the firmware, and applying patches. Device disruptions are reduced to a minimum by validation. Within the context of smart home and industrial automation tests, events were identified with a high degree of precision and a low percentage of false positives. Because it automatically fixes problems, the self-repair module makes the Internet of Things ecosystem better.