SVM-based Anomaly Detection in IoT- Enabled Microelectronic Systems

This research gives the application of Support Vector Machines (SVM) for anomaly identification in IoT-enabled microelectronic based systems. As IoT networks increasingly merge advanced microelectronic modules, ensuring system data-reliability and security becomes important. Anomalies like unexpected device behaviour, signal interferences, or potentially security breaches can significantly impact the performance level and functionality of the systems. By using SVM, a robust supervisor learning algorithm known for its accuracy level in classification tasks, this research work aims in identifying unusual patterns and irregularities within the IoT ecosystem. The proposed model will be designed to improve system monitoring by identifying deviations from normal operation, thus enabling early anomaly intervention and mitigation of potential threats. This approach also offers a scalable and effective solution for enhancing the data-security and performance of IoT-merged microelectronic systems in different applications, ranging from smart cities to industrial automation.