Utilizing Machine Learning Techniques for Detecting Anomalies in IoT Networks

The proliferation of IoT devices has enabled unprecedented data collection and connectivity, revolutionizing various sectors. Due to the huge network of linked devices, security and anomaly detection are harder. Our research examines how machine learning may improve anomaly detection in IoT networks to answer the demand for effective cyber security. The proposed method evaluates network traffic patterns using supervised and unsupervised machine learning. Training supervised learning models on annotated datasets lets the system discriminate good and poor behavior. The research also examines how real-time monitoring might assist dynamic Internet of Things systems identify and adapt to new risks. The proposed method is evaluated on IoT network datasets with different devices and communication protocols. Results indicate that machine learning algorithms can identify anomalies without false positives. In addition, the system's ability to learn and adapt to new normal and deviant behavior patterns in changing IoT environments is tested. This research helps IoT cyber security become proactive and robust. Organizations can use machine learning to detect and mitigate security vulnerabilities to ensure safe and dependable IoT integration and development.