用机器学习提升医疗物联网安全，精准识别攻击与设备异常。
SHIELD: Securing Healthcare IoT with Efficient Machine Learning Techniques for Anomaly Detection
- 对比八种模型在三种学习方式下表现，选最优组合
- XGBoost达99%准确率，0.04秒耗时，适合实时检测
- KNN对攻击检测近乎完美，计算成本最低，适合部署
医疗物联网的融合带来了严重的安全与可靠性挑战，使其更易受网络攻击和运行异常影响。本研究提出一种基于机器学习的框架，用于（1）检测恶意网络攻击，（2）识别故障设备异常，基于包含20万条记录的数据集进行评估。八种机器学习模型在三种学习范式下被测试：监督学习（XGBoost、K-近邻(K-NN)）、半监督学习（生成对抗网络(GAN)、变分自编码器(VAE)）和无监督学习（一类支持向量机(One-Class SVM)、孤立森林(Isolation Forest)、图神经网络(GNN)、长短期记忆自编码器(LSTM Autoencoders)）。综合评估涵盖F1-score、精确率、召回率、准确率、ROC-AUC及计算效率。XGBoost在异常检测中达到99%准确率，计算开销极低（0.04秒）；孤立森林在精确率与召回率间取得良好平衡。LSTM自编码器表现较差，准确率低且延迟高。攻击检测方面，KNN实现近乎完美的精确率、召回率与F1-score，计算成本最低（0.05秒），其次为VAE，准确率达97%。GAN计算成本最高，准确率与ROC-AUC最低。该研究通过高效异常检测策略增强医疗物联网安全性，有助于提前发现网络威胁与设备故障，防止数据泄露，减少系统停机，保障医疗设备持续安全运行，最终维护患者健康与对物联网医疗解决方案的信任。
原文摘要 · Abstract (English)
The integration of IoT devices in healthcare introduces significant security and reliability challenges, increasing susceptibility to cyber threats and operational anomalies. This study proposes a machine learning-driven framework for (1) detecting malicious cyberattacks and (2) identifying faulty device anomalies, leveraging a dataset of 200,000 records. Eight machine learning models are evaluated across three learning approaches: supervised learning (XGBoost, K-Nearest Neighbors (K- NN)), semi-supervised learning (Generative Adversarial Networks (GAN), Variational Autoencoders (VAE)), and unsupervised learning (One-Class Support Vector Machine (SVM), Isolation Forest, Graph Neural Networks (GNN), and Long Short-Term Memory (LSTM) Autoencoders). The comprehensive evaluation was conducted across multiple metrics like F1-score, precision, recall, accuracy, ROC-AUC, computational efficiency. XGBoost achieved 99\% accuracy with minimal computational overhead (0.04s) for anomaly detection, while Isolation Forest balanced precision and recall effectively. LSTM Autoencoders underperformed with lower accuracy and higher latency. For attack detection, KNN achieved near-perfect precision, recall, and F1-score with the lowest computational cost (0.05s), followed by VAE at 97% accuracy. GAN showed the highest computational cost with lowest accuracy and ROC-AUC. These findings enhance IoT-enabled healthcare security through effective anomaly detection strategies. By improving early detection of cyber threats and device failures, this framework has the potential to prevent data breaches, minimize system downtime, and ensure the continuous and safe operation of medical devices, ultimately safeguarding patient health and trust in IoT-driven healthcare solutions.
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