Improving IoT Network Security Using Deep Learning and Hybrid Feature Selection Techniques

Abstract

The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges due to heterogeneous devices, high-dimensional data, and evolving cyberattacks. Traditional intrusion detection systems (IDSs) often struggle to achieve high accuracy while maintaining computational efficiency in such environments. This thesis proposes an enhanced deep learning-based IDS that integrates a dual statistical feature selection framework to improve detection performance and reduce computational complexity. The proposed approach combines Pearson correlation and Mutual Information in a two-stage feature selection process to identify the most relevant features from IoT network traffic. This framework is integrated with a fully connected deep neural network designed to learn complex intrusion patterns. Two IDS architectures are evaluated using the NSL-KDD and RT-IoT2022 datasets. Experimental results show that the proposed system achieves high detection accuracy, exceeding 99% in most scenarios, while reducing training time. Comparative analysis with existing machine learning and deep learning approaches confirms the effectiveness and robustness of the proposed solution for IoT intrusion detection.