Deep Learning-Based Monitoring System to Enhance IoT Network Performance

The rapid growth and increasing complexity of Internet of Things (IoT) networks require efficient real-time monitoring and anomaly detection mechanisms. Traditional machine learning approaches often struggle to handle the dynamic and high-dimensional traffic generated by IoT environments. This study investigates the effectiveness of deep learning models, including Feedforward Neural Networks (FFNN), Convolutional Neural Networks (CNN), and Multilayer Perceptron (MLP), for enhancing IoT network monitoring. The models were trained using both synthetic and real-world IoT traffic datasets in MATLAB with Adam and Stochastic Gradient Descent with Momentum (SGDM) optimizers to improve convergence and training stability. Experimental results demonstrate that deep learning models outperform traditional machine learning techniques in detecting complex traffic patterns and anomalies. Among the evaluated models, CNN achieved the highest accuracy of 94%, compared with Decision Trees (78.5%) and Support Vector Machines (85.7%). CNNs effectively capture spatiotemporal traffic characteristics, while MLPs efficiently model nonlinear relationships in network data. The proposed framework provides a scalable, reliable approach to real-time IoT network monitoring.