Intrusion Detection System using Stacking of Deep Learning Models with Bte-Lgbm for IoT Networks

The recent development of the Internet of Things (IoT) has increased the severity of security threats. It is mainly caused by IoT devices' inherent weaknesses, making them vulnerable to attack. Therefore, strengthening the security of such network systems is crucial. This study proposes a novel stacking model to identify attacks in the IoT environment. As a first step, we preprocess the data to make it more reliable. To address the issue of class imbalance, synthetic minority samples are generated by using SMOTE-SVM. Then, a novel stacking model was built by integrating four neural networks: deep neural network (DNN), recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) with hyper-parameter tuned Light gradient boosting machine (BTE-LGBM). The performance of the proposed stacking model was evaluated on two recent IoT datasets, namely the ToN_IoT and CIC-IoT23. The efficacy of the suggested stacking model is evaluated and compared with Deep learning, machine learning, and state-of-the-art approaches with respect to metrics such as detection rate, precision, accuracy, and F1 score. The findings of our experiments indicate that the suggested IDS achieves a high accuracy of 99.81% and 99.78% for ToN_IoT and CIC-IoT23 datasets, respectively. It might enhance IoT device security, eventually benefiting consumers who depend on these devices. These findings, however, are based on benchmark dataset and could be impacted by variables including class distribution, attack diversity, and dataset characteristics. More research is needed to determine how well the model performs in real-world IoT contexts with changing attack patterns and heterogeneous devices.