Abstract
The research article demonstrates that current IT advancements include the rapid growth of the Internet of Things (IoT). The 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. The Internet of Things affects energy, transportation, manufacturing, healthcare, and education. The research demonstrates the effectiveness of a machine learning model for classifying normal and anomalous data in IoT systems. To evaluate the performance of our proposed model, we used an open-source BoT-IoT dataset for the IoT environment. 80 percent of the dataset used for the study was used for model training, while 20 percent was implemented in testing mode. Classification metrics, including precision, recall, and F1-score, approach 1.0, indicating high accuracy with a 100% classification rate. The model, particularly the Random Forest, successfully identifies anomalies in network behavior and triggers 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.