面向医疗物联网环境中入侵检测的可解释混合特征选择方法
Explainable Hybrid Feature Selection for Intrusion Detection in Internet of Medical Things Environments
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中文总结 AI 辅助
针对医疗物联网网络防护难题，提出结合皮尔逊相关滤波、模型特征重要性与SHAP归因的混合特征选择方法，在两个公开数据集上实现特征大幅缩减且分类性能接近全特征模型，可用于资源受限医疗网络。
中文摘要 AI 辅助
医疗物联网（IoMT）网络防护难度大，其设备异构、计算资源有限且流量需实时分析。本文提出一种基于特征选择的入侵检测系统以应对这些约束：首先用皮尔逊相关滤波器移除冗余属性，再采用混合策略将基于模型的特征重要性与SHAP归因结合，选出紧凑子集，在此子集上训练随机森林（Random Forest）和轻量级梯度提升机（LightGBM）分类器；通过SHAP和LIME可解释每个保留特征对决策的贡献。在CIC-IoMT 2024和CIC-IDS 2017数据集上，该方法最多可将特征空间缩减88%（从40个特征降至最少5个），且准确率和F1分数与使用全部特征训练的模型仅相差几个百分点，这种紧凑且可解释的检测器是资源受限医疗网络部署的实用候选方案。
英文摘要
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.
发表机构
- Badji Mokhtar University（巴吉·莫克塔大学）
- University of Bari（巴里大学）
机构由 AI 辅助整理，请以论文原文为准。
