The research article shows current IT advancements include the rapid growth of the Internet of Things (IoT). Internet of Things allows tangible elements to be integrated into digital frameworks. A complex infrastructure of smart sensors, sensor networks, home appliances, industrial controllers, and medical devices automates processes, improves management, and enables new services. Internet of Things use affects energy, transportation, manufacturing, healthcare, and education. The research work shows the effectiveness of a machine learning model in classifying normal and anomalous data within IoT systems.

 Classification metrics, including precision, recall, and F1-score, approach 1.0, indicating high accuracy with a 100% classification rate. The model, particularly the Isolation Forest, successfully identifies anomalies in network behavior, triggering alarms when threats are detected. It highlights the advantages of using deep neural networks for malware detection in IoT environments, which can analyze both static binary files and dynamic process behaviors. These models adapt to new threats, enhancing security measures in a landscape where traditional methods fall short.