HFXL-Model: A Hybrid Deep Learning and Ensemble Boosting Framework for Binary Intrusion Detection in IoT Networks

Recently, the cyberattack surface has expanded significantly due to the proliferation of Internet of Things (IoT) devices, and intrusion detection remains a vital component of modern network security. Traditional Intrusion Detection Systems (IDSs) often cannot detect complex and newly generated threats due to the heterogeneous characteristics and constrained computing capabilities of IoT devices. In this paper, we utilise ensemble learning techniques, specifically Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM), in combination with a Feed-Forward Neural Network (FFNN) to develop a hybrid IDS. To address class imbalance and complex feature interactions that can be managed by Gradient Boosting (GB) techniques, the proposed model combines the robustness of gradient boosting with that of deep learning in learning complex non-linear patterns in network traffic. The experimental results show the hybrid model achieves strong performance, such as an accuracy of 99.42%, precision of 98.43%, recall of 99.65%, F1-score of 99.04%, and Area Under the receiver operating characteristic Curve (AUC) of 0.9994. These findings indicate the effectiveness of the proposed hybrid IDS in securing the IoT from diverse online threats.