Enhanced Intrusion Detection System for IoT Networks Using Hybrid Deep Learning and Feature Selection Techniques

Abstract

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, making IoT networks prime targets for cyberattacks. Traditional security mechanisms often fall short in addressing the complexity and scale of these threats. This paper proposes an enhanced Intrusion Detection System (IDS) for IoT networks utilizing a hybrid deep learning approach combined with effective feature selection techniques. The proposed IDS integrates a Convolutional Neural Network (CNN) for feature extraction and a Recurrent Neural Network (RNN), specifically Long Short-Term Memory (LSTM), for temporal sequence analysis of network traffic. A feature selection method based on Information Gain and Variance Thresholding is employed to reduce dimensionality and improve the efficiency and accuracy of the deep learning model. The performance of the hybrid IDS is evaluated using benchmark IoT network datasets, demonstrating superior detection accuracy, lower false positive rates, and enhanced resilience against various attack vectors compared to existing state-of-the-art methods. The results highlight the potential of this approach to significantly improve the security posture of IoT environments.