Explainable Hybrid Feature Selection for Intrusion Detection in Internet of Medical Things Environments

Abstract:Internet of Medical Things (IoMT) networks are hard to protect: devices are heterogeneous, computing resources are scarce, and traffic must be analyzed in real time. We present an intrusion detection system that addresses these constraints through feature selection. A Pearson correlation filter first removes redundant attributes; a hybrid strategy then combines model-based feature importance with SHAP attribution to pick a compact subset, on which we train Random Forest and LightGBM classifiers. SHAP and LIME explain what each retained feature contributes to the decisions. On CIC-IoMT 2024 and CIC-IDS 2017, the method cuts the feature space by up to 88% - from 40 to as few as 5 features - and accuracy and F1-score stay within a few points of models trained on all features. Compact, interpretable detectors of this kind are practical candidates for deployment on resource-limited medical networks.

Cryptography and Security (cs.CR); Machine Learning (cs.LG)

arXiv:2608.00869 [cs.CR]

(or arXiv:2608.00869v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2608.00869 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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