M-IDM: A Multi-Classification Based Intrusion Detection Model in Healthcare IoT

• Published in: Computers, materials & continua Vol. 67; no. 2; pp. 1537 - 1553
• Main Authors: Dong Lee, Jae, Soung Cha, Hyo, Rathore, Shailendra, Hyuk Park, Jong
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2021
• Subjects: Algorithms; Artificial neural networks; Classification; Datasets; Electrocardiography; Electronic devices; Health care; Internet of medical things; Intrusion; Machine learning; Medical electronics; Medical equipment; Neural networks; Support vector machines
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2021.014774

In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed a novel intrusion classification architecture known as a Multi-class Classification based Intrusion Detection Model (M-IDM), which typically relies on data collected by real devices and the use of convolutional neural networks (i.e., it exhibits better performance compared with conventional machine learning algorithms, such as naïve Bayes, support vector machine (SVM)). Unlike existing studies, the proposed architecture employs the actual healthcare IoT environment of National Cancer Center in South Korea and actual network data from real medical devices, such as a patient’s monitors (i.e., electrocardiogram and thermometers). The proposed architecture classifies the data into multiple classes: Critical, informal, major, and minor, for intrusion detection. Further, we experimentally evaluated and compared its performance with those of other conventional machine learning algorithms, including naïve Bayes, SVM, and logistic regression, using neural networks.