用卷积神经网络检测医疗物联网攻击，准确率达99%。
Securing Healthcare with Deep Learning: A CNN-Based Model for medical IoT Threat Detection
- 用CNN分析医疗设备网络流量的时序特征
- 在CICIoMT2024数据集上达到99%准确率
- 适合关注医疗网络安全的研究者和工程师
医疗物联网（IoMT）在提升患者护理水平的同时，也带来了严峻的网络安全挑战。本文提出一种基于卷积神经网络（CNN）的新型方法，用于检测IoMT环境中的网络攻击。与以往主要依赖传统机器学习模型或简单深度神经网络的研究不同，该模型利用CNN有效捕捉网络流量数据的时序特性。在包含18类攻击、覆盖多种IoMT设备的CICIoMT2024数据集上训练并评估，该模型在二分类、多分类及类别分类任务中均达到99%的准确率，显著优于逻辑回归、AdaBoost、DNN和随机森林等传统方法。结果表明，CNN能大幅提升IoMT系统的安全性，保障互联医疗系统的信息保护与完整性。
原文摘要 · Abstract (English)
The increasing integration of the Internet of Medical Things (IoMT) into healthcare systems has significantly enhanced patient care but has also introduced critical cybersecurity challenges. This paper presents a novel approach based on Convolutional Neural Networks (CNNs) for detecting cyberattacks within IoMT environments. Unlike previous studies that predominantly utilized traditional machine learning (ML) models or simpler Deep Neural Networks (DNNs), the proposed model leverages the capabilities of CNNs to effectively analyze the temporal characteristics of network traffic data. Trained and evaluated on the CICIoMT2024 dataset, which comprises 18 distinct types of cyberattacks across a range of IoMT devices, the proposed CNN model demonstrates superior performance compared to previous state-of-the-art methods, achieving a perfect accuracy of 99% in binary, categorical, and multiclass classification tasks. This performance surpasses that of conventional ML models such as Logistic Regression, AdaBoost, DNNs, and Random Forests. These findings highlight the potential of CNNs to substantially improve IoMT cybersecurity, thereby ensuring the protection and integrity of connected healthcare systems.
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