Securing IoT Networks with Deep Belief Network-Based Intrusion Monitoring Systems

The rapid growth of Internet of Things (IoT) introduces new vulnerabilities, requiring advanced security measures to protect interconnected devices and sensitive data. Hence, this paper introduce an innovative Intrusion Monitoring System (IMS) utilizing Deep Belief Networks (DBNs) to address the complexities of IoT network security. The proposed DBNs, is a Deep Learning (DL) model which is designed to effectively model and classify complex, high-dimensional IoT data, enabling efficient anomaly detection and pattern recognition. Also, this work incorporates a preprocessing stage, including data cleaning, normalization, and transformation, offering high-quality input for the DL model. In addition, the framework handles dynamic and heterogeneous IoT traffic, ensuring adaptability to evolving threats. The validation of the proposed work using Python demonstrates a detection accuracy of 95.20%, indicates the superior ability of DBNs to learn complex patterns in IoT data. Thereby, the proposed work contributes to proposing a reliable and scalable solution for improving IoT security, supporting the development of next-generation IMS.