American Journal for Young Scientists ยท ISSN 3068-9015
A Gradient Boosting Framework with Phi-K Correlation-Based Feature Selection for Intrusion Detection in IoT Networks
Authors: Emily(Yiming) Ma
Published: November 13, 2026
Category: Computer Science
Abstract
Internet of Things (IoT) devices generate vast network traffic but can hardly run heavyweight intrusion detection systems due to resource constraints. Moreover, the signature-based detectors often fail against zero-day attacks. This study proposes a lightweight detection framework that combines Phi-K correlation-based feature selection with gradient boosting. By using the RT-IoT2022 dataset of over 120,000 real IoT traffic flows, I reduced 82 candidate features to 34 (a 58.5% reduction) and compared nine models spanning logistic regression, decision trees, bagging, random forests, and XGBoost. The tree-based ensembles clustered at the top of the comparison, with no meaningful difference between them; the tuned XGBoost model achieved 99.87% accuracy, 99.92% recall, 99.93% precision, and an F1-score of 0.9993 on held-out test data, with a false-negative rate of 0.08% and a false-positive rate of 0.64%. The feature-importance analysis shows that timing features are dominant: maximum flow inter-arrival time (40.9%) ranks first, and maximum forward inter-arrival time (27.7%) ranks second, which suggests that a small set of timing features can effectively support accurate, resource-efficient detection at IoT gateways. Limitations include a single-dataset evaluation and bias from random splitting, and I also address the outline of extensions for multi-class classification and strategies to improve robustness.