Derin öğrenme algoritmalarına dayalı ağ performansını iyileştirmek için ıot ağlarının eğitimli izlenmesi

The introduction of the IoT has forced the integration of billions of devices in different sectors, thereby creating huge data and change. Nevertheless, IoTs have some few challenges when it comes to internetworking and securing the networks. Networking problems like latency, packet loss, congestion and probable security holes make it imperative that networking headers are monitored and monitored are checked for anomalies. This thesis proposes a deep learning-based approach to real-time IoT network monitoring and anomaly detection, focusing on three models: FFNN, CNN, and MLP are the popular categories of Deep Learning Algorithms. The models were created and built with MATLAB to review IoT network data to identify discrepancies, distinguish malfunctioning nodes, and diagnose future problems. In an effort to enhance the outcome of the models, optimization methods of Adam and Stochastic Gradient Descent with Momentum (SGDM) was used. The models were tested on synthetic IoT data and the results highlighted by using the quality control indicators such as accuracy, precision, recall, and F1-score. However, the results show the proposed methods improve the existing results where MLP and CNN have higher accuracy and anomaly detection rates than FFNN, MLP-93 (92.3%), CNN-94 (94%), DT (78.5%), and SVM (85.7%). This considerable enhancement is due to efficiency of CNN for extracting spatial relationship and MLP to learn non-linear relationship in IoT network data. Comparing with other methodologies implemented in the current research, deep learning models provide a higher level of accuracy in the identification of sophisticated abnormalities, which is beneficial for real-time IoT monitoring. In this case, the CNN model showed remarkable improvement on its capability in the identification of network patterns and the prediction of issues as compared to prior models. These outcomes give a strong signal that deep learning models, including CNN and MLP, are more beneficial for real-time IoT network performance monitoring and anomaly identification than conventional models of machine learning. The work from this research can be generalized as follows to be used in improving the dependability and securability of the IoT networks while giving a much better solution than the current method.