A comparative study of SMOTE and ADASYN for multiclass classification of IoT anomalies
2025, vol.17 , no.2, pp. 15-24
Article [2025-02-02]
The rise of IoT technologies has increased the need for robust threat detection to address growing cyber-physical risks. Traditional machine learning (ML) models often struggle with imbalanced datasets, particularly in detecting rare threats. This study tackles this challenge using the "IoT_Modbus" dataset, a benchmark for multiclass cybersecurity classification, and explores advanced resampling techniques to enhance model performance. By combining methods like Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) with ensemble classifiers, Random Forest (RF), Gradient Boosting, XGBoost, LightGBM, and CatBoost, significant improvements in accuracy were achieved. RF outperformed others, reaching 99.06% accuracy with SMOTE and 99.00% with ADASYN. Among boosting-based models, XGBoost led with 86.21% (SMOTE) and 86.63% (ADASYN), while CatBoost followed closely. These results highlight the effectiveness of resampling in addressing class imbalance and improving predictive performance. Visual tools like heatmaps and accuracy charts further clarify these trends. This research provides cybersecurity professionals with a practical framework to enhance IoT threat detection and underscores the importance of resampling strategies for better classification outcomes. By guiding model selection for imbalanced datasets, it offers valuable insights for securing IoT systems in an interconnected world.
IoT, resampling techniques, class imbalance, OvO classification, accuracy optimization
https://doi.org/10.59035/QEFU7977
Fayez Alharbi. A comparative study of SMOTE and ADASYN for multiclass classification of IoT anomalies. International Journal on Information Technologies and Security, vol.17 , no.2, 2025, pp. 15-24. https://doi.org/10.59035/QEFU7977