CPU-GPU Performance Evaluation of Lightweight Tree-Based Machine Learning for IoT Anomaly Detection

The increasing complexity of Internet of Things (IoT) environments necessitates efficient and reliable anomaly detection. This study experimentally evaluates established tree-based machine-learning models instead of proposing a new detection algorithm. Decision Tree (DT), Random Forest (RF), and XGBoost are compared using CPU- and GPU-based implementations on the highly imbalanced DS2OS dataset by considering training time, overall classification performance, and minority-class detection. GPU experiments were conducted using an NVIDIA T4 in a cloud-based environment; the results do not establish performance on physical IoT or edge devices. The recorded training times yielded CPU-to-GPU time ratios ranging from 2.12 to 2.94 across the evaluated implementations. However, the original CPU hardware configuration could not be verified, preventing these differences from being attributed exclusively to GPU acceleration and limiting their reproducibility under equivalent conditions. In the initial evaluation, Random Forest configurations failed to detect minority attack classes despite high overall accuracy, whereas supplementary evaluations demonstrated substantially improved performance with alternative feature encoding. XGBoost achieved near-perfect classification in the initial test partition and reduced-training configurations. Although supplementary evaluations initially showed lower and more variable performance with the complete-training partition under automatic initialisation, a controlled ten-fold evaluation demonstrated that fixing the initial prediction score eliminated this deterioration on both the CPU and GPU within the evaluated DS2OS configurations. Supplementary experiments further demonstrated that the GPU-based Decision Tree proxy achieved classification performance comparable to the CPU Decision Tree following adjustments to its model configuration, indicating that the original performance difference was primarily configuration-dependent rather than an inherent limitation of GPU hardware. Energy efficiency and embedded-device performances were not experimentally established.