Publication

Abstract : Nowadays, Internet of Things (IoT) applications are growing and gaining popularity.This accelerated development encounters several obstacles, including the sheer volume of data generated, network scalability, and security concerns. Distributed Denial of Service (DDoS) attacks are widespread in IoT systems because security is frequently overlooked in detection systems. Therefore, it is necessary to propose an efficient IDS to detect DDoS attacks from immense network traffic. The present study suggests a hybrid feature selection model by combining filter-based Pearson Correlation and wrapper-based Boruta with the Light gradient Boost Model (LGBM) as a base classifier (PCB-LGBM). In addition, the hyperparameters of the model are tuned by using the shap-hyper tune approach and retrieved more informative features. The proposed architecture was tested on the CICIDS-2017 dataset. The experimental results reveal that the suggested model PCB-LGBM has better results with the LGBM classifier when compared with other methods such as Decision Tree (DT), Multi-Layer Perceptron (MLP), and K-Nearest Neighbors (KNNs).