Department of Computer Science and Information Systems, Bradley University, Peoria, IL 61625, USA.
Sensors (Basel). 2024 Oct 13;24(20):6601. doi: 10.3390/s24206601.
The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people's lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets-biometric and network flow metric-have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results.
物联网范式的持续发展已经广泛应用于各个应用领域，包括空中交通管制、教育、医疗保健、农业、交通、智能家居等。我们的主要关注点是探索物联网的应用，特别是在医疗保健领域，它在促进安全和实时远程患者监测系统方面发挥着关键作用。这项创新旨在提高服务质量，最终改善人们的生活。在这个生态系统中，一个关键组成部分是医疗监测系统 (HMS)，这是一种基于技术的框架，旨在实时持续监测和管理患者和医疗保健提供者的数据。该系统集成了各种组件，如软件、医疗设备和流程，旨在改善患者护理并支持医疗保健提供者做出明智的决策。这促进了主动医疗保健管理，并在需要时实现及时干预。然而，这些系统中的数据传输在传输过程中存在重大安全威胁，因为恶意行为者可能试图破坏安全协议。这危及医疗物联网 (IoMT) 的完整性，并最终危及患者的安全。两个特征集——生物特征和网络流量指标——已被纳入，以提高医疗系统的检测能力。另一个主要挑战是缺乏可用于分析各种 IoMT 攻击模式的公共可用平衡数据集。为了解决这个问题，辅助分类器生成对抗网络 (ACGAN) 被用来生成类似于少数类样本的合成样本。ACGAN 有两个目标：鉴别器区分真实和合成样本，同时预测正确的类别标签。这种双重功能确保鉴别器可以学习到两个任务的详细特征。同时，生成器生成高质量的样本，这些样本被鉴别器分类为真实样本，并被辅助分类器正确标记。该方法使用 IoMT 数据集进行评估，在关键指标（包括准确性、精度、召回率、F1 得分、曲线下面积 (AUC) 和混淆矩阵结果）上始终优于现有基线模型。
