LightGBM

Despite the high amount of research on networks and systems security, that comes out every day, the field of intrusion detection still attracts the interest of many researchers as one of the most important axes that contribute to understand the attacks behavior for providing an efficient and accurate solution with low cost.  Wireless sensor network and Internet of Things are one of the special networks that have several constraints, limited computational resources, and the most vulnerable for attacks, especially, DoS and DDoS attacks, which considered as the most danger

This article studies salary prediction under distributional drift using explainable boosting models and hybrid forecasting.  We integrate unseen-aware feature engineering, robust objectives, SHAP-based interpretability, drift detection, and time-series forecasting (Prophet/SARIMAX) on multi-year data (2020–2024), and report a comprehensive evaluation aligned with typical MMC guidelines.  Modern salary data are heterogeneous, heavy-tailed, and non-stationary.  Therefore we combine robust tree-based learners with drift monitoring and explainable forecasting to prioritize