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

arXiv:2608.00869 · cs.CR, cs.LG · Submitted 2026-08-01 · Read on arXiv

cs.CR, cs.LG

Submitted: 2026-08-01

Updated: 2026-09-14

Comments: 6 pages, 9 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 40× in this paper · explained in 1 episode
• Feature Selection — 31× in this paper · explained in 4 episodes
• AUC — 20× in this paper · explained in 2 episodes
• Random Forest — 18× in this paper
• Explainability — 9× in this paper · explained in 4 episodes
• Ensemble Learning — 7× in this paper · explained in 2 episodes
• Feature Importance — 7× in this paper · explained in 2 episodes
• Internet of Medical Things — 7× in this paper
• Hybrid Feature Selection — 6× in this paper
• Interpretability — 6× in this paper · explained in 15 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Machine Learning — 5× in this paper
• Generalization — 3× in this paper · explained in 14 episodes
• Inter-Arrival Time — 3× in this paper
• Hybrid Approach — 2× in this paper · explained in 4 episodes
• Intrusion Detection Systems — 2× in this paper
• Meta-learning — 2× in this paper · explained in 3 episodes
• XGBoost — 2× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Leakage — 1× in this paper · explained in 2 episodes
• Deep Belief Network — 1× in this paper
• Dimensionality Reduction — 1× in this paper · explained in 2 episodes
• Ensemble Approach — 1× in this paper · explained in 3 episodes
• Explainable AI (XAI) — 1× in this paper · explained in 7 episodes
• Local Interpretable Modelagnostic Explanations — 1× in this paper
• Machine Learning (ML) — 1× in this paper · explained in 2 episodes
• Model Interpretability — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Surrogate Model — 1× in this paper · explained in 3 episodes

Sources

• "Why Should I Trust You?": Explaining the Predictions of Any Classifier

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