HealthGuard: An Intelligent Healthcare System Security Framework Based on Machine Learning

Amit Sundas, Sumit Badotra, Salil Bharany (), Ahmad Almogren, Elsayed M. Tag-ElDin () and Ateeq Ur Rehman
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Amit Sundas: Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
Sumit Badotra: Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
Salil Bharany: Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar 143005, Punjab, India
Ahmad Almogren: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
Elsayed M. Tag-ElDin: Faculty of Engineering and Technology, Future University of Egypt, New Cairo 11835, Egypt
Ateeq Ur Rehman: Faculty of Engineering, Uni de Moncton, Moncton, NB E1A3E9, Canada

Sustainability, 2022, vol. 14, issue 19, 1-16

Abstract: Utilization of the Internet of Things and ubiquitous computing in medical apparatuses have “smartified” the current healthcare system. These days, healthcare is used for more than simply curing patients. A Smart Healthcare System (SHS) is a network of implanted medical devices and wearables that monitors patients in real-time to detect and avert potentially fatal illnesses. With its expanding capabilities comes a slew of security threats, and there are many ways in which a SHS might be exploited by malicious actors. These include, but are not limited to, interfering with regular SHS functioning, inserting bogus data to modify vital signs, and meddling with medical devices. This study presents HealthGuard, an innovative security architecture for SHSs that uses machine learning to identify potentially harmful actions taken by users. HealthGuard monitors the vitals of many SHS-connected devices and compares the vitals to distinguish normal from abnormal activity. For the purpose of locating potentially dangerous actions inside a SHS, HealthGuard employs four distinct machine learning-based detection approaches (Artificial Neural Network, Decision Tree, Random Forest, and k-Nearest Neighbor). Eight different smart medical devices were used to train HealthGuard for a total of twelve harmless occurrences, seven of which are common user activities and five of which are disease-related occurrences. HealthGuard was also tested for its ability to defend against three distinct forms of harmful attack. Our comprehensive analysis demonstrates that HealthGuard is a reliable security architecture for SHSs, with a 91% success rate and in F1-score of 90% success.

Keywords: machine learning; healthcare system security; Smart Healthcare System; monitoring; smart medical devices; malicious activity detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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