MOD-XGBOOST: AN ADAPTIVE MACHINE LEARNING MODEL FOR INTERNET OF THINGS ENVIRONMENT TO DETECT SPOOFING AND DOS ATTACKS
2025, vol.17 , no.1, pp. 79-90
Article [2025-01-08]
The rising number of Internet of Things (IoT) devices necessitates robust security to counter threats like Denial-of-Service (DoS) and spoofing attacks. Many Intrusion Detection Systems (IDS) leverage Machine Learning (ML), but challenges like concept drift and class imbalance persist. This study presents an adaptive ML-based IDS framework using a Modified XGBoost (Mod-XGB) model. Mod-XGB incorporates a weighted loss function for class imbalance, adaptive instance weighting for concept drift, and a security penalty term to enhance feature selection. Evaluated on the CICIoV2024 dataset, Mod-XGB achieved 97.96% accuracy, outperforming models like Random Forest, Logistic Regression, and AdaBoost. This innovative approach effectively addresses IoT security challenges, offering significant advancements in IDS performance for improved attack detection and resilience in dynamic environments.
IoT security, attack detection, XGBoost, concept drift, class imbalance, Modified XGBoost, Intrusion Detection System, CICIoV2024
https://doi.org/10.59035/MZGI5749
Sujit N. Deshpande, Deipali Vikram Gore, Amrapali Shivajirao Chavan, Arundhati Nelli. MOD-XGBOOST: AN ADAPTIVE MACHINE LEARNING MODEL FOR INTERNET OF THINGS ENVIRONMENT TO DETECT SPOOFING AND DOS ATTACKS. International Journal on Information Technologies and Security, vol.17 , no.1, 2025, pp. 79-90. https://doi.org/10.59035/MZGI5749