Top-K Feature Selection for IoT Intrusion Detection: Contributions of XGBoost, LightGBM, and Random Forest

The rapid growth of the Internet of Things (IoT) has created vast networks of interconnected devices that are increasingly exposed to cyberattacks. Ensuring the security of such distributed systems requires efficient and adaptive intrusion detection mechanisms. However, conventional methods face limitations in processing large and complex feature spaces. To address this issue, this study proposes an optimized intrusion detection approach based on Top-K feature selection combined with ensemble learning models, evaluated on the CICIoMT2024 dataset. Three algorithms, XGBoost, LightGBM, and Random Forest, were trained and tested on IoT datasets using three feature configurations: Top-10, Top-15, and the complete feature set. The results show that the Random Forest model provides the best balance between accuracy and computational efficiency, achieving 91.7% accuracy and an F1-score of 93% with the Top-10 subset while reducing processing time by 35%. These findings demonstrate that the Top-K selection strategy enhances the interpretability and performance of IDSs in IoT environments. Future work will extend this framework to real-time adaptive detection and edge computing integration for large-scale IoT deployments.