Open Access

ARTICLE

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

Jae Dong Lee^1,2, Hyo Soung Cha¹, Shailendra Rathore², Jong Hyuk Park^2,*

1 Healthcare AI Team, National Cancer Center, Goyang-si, 10408, Korea
2 Department of Computer Science and Engineering, Seoul National University of Science and Technology, (SeoulTech), Seoul, 01811, Korea

* Corresponding Author: Jong Hyuk Park. Email:

(This article belongs to the Special Issue: Machine Learning-based Secured and Privacy-preserved Smart City)

Computers, Materials & Continua 2021, 67(2), 1537-1553. https://doi.org/10.32604/cmc.2021.014774

Received 15 October 2020; Accepted 12 December 2020; Issue published 05 February 2021

Abstract

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.

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Citations