RSM: A Real-time Security Monitoring Platform for IoT Networks

The rapid growth of Internet of Things (IoT) resulted in a heightened risk of security breaches, as cybercriminals have begun to target IoT devices and networks with increasingly sophisticated techniques. However, IoT security monitoring platforms face several challenges, including the inability to identify unknown threats, limited real-time prediction capabilities depending on signature-based threat identification, and the need for standardization and integration issues. In this paper, we propose a Real-Time Security Monitoring (RSM) platform based on the results of Deep Learning models, which can predict attacks on IoT networks and visualize the prediction results in a custom-built Power BI dashboard in a real-time manner. To evaluate our proposed solutions, we compare the effectiveness of three deep learning models - Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNN) - using the IoT23 dataset in the context of the binary classification problem. We compare these models based on their accuracy, precision, recall, and F1 score. In addition, our findings show that our proposed platform outperforms existing solutions in terms of accuracy and can predict IoT network attacks with high precision and recall. We also implemented a test bed using a Raspberry PI programmed to send its logs to the nearest connected edge router and a server programmed using Python with a scheduler to pull those logs and show real-time Deep Learning Model prediction results in a Power BI dashboard. Our results demonstrate that the RSM and the Power BI dashboard provide a user-friendly way to monitor IoT Network security in real-time. This study provides valuable insights into applying Deep Learning (DL) and Power BI dashboard in the IoT security monitoring domain.