{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T18:41:13Z","timestamp":1791312073100,"version":"4.3.1"},"reference-count":45,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T00:00:00Z","timestamp":1728777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bradley University and the National Science Foundation","award":["HBCU EiR 2101181"],"award-info":[{"award-number":["HBCU EiR 2101181"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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\u2014biometric and network flow metric\u2014have 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.<\/jats:p>","DOI":"10.3390\/s24206601","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T07:58:32Z","timestamp":1729065512000},"page":"6601","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["ACGAN for Addressing the Security Challenges in IoT-Based Healthcare System"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3691-3186","authenticated-orcid":false,"given":"Babu Kaji","family":"Baniya","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Systems, Bradley University, Peoria, IL 61625, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106576","DOI":"10.1109\/ACCESS.2020.3000421","article-title":"Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study","volume":"8","author":"Hady","year":"2020","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s10916-019-1254-0","article-title":"Low-Cost and Active Control of Radiation of Wearable Medical Health Device for Wireless Body Area Network","volume":"43","author":"Jin","year":"2019","journal-title":"J. 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