| Title | HealthGuard: A Machine Learning-Based Security Framework for Smart Healthcare Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Newaz, AKM Iqtidar, Sikder, Amit Kumar, Rahman, Mohammad Ashiqur, Uluagac, A. Selcuk |
| Conference Name | 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS) |
| Date Published | oct |
| Keywords | anomaly detection, artificial neural network, computer network security, critical medical conditions, Decision Tree, Decision trees, diseases, Health Care, healthcare, healthguard, Human Behavior, human factors, implantable medical devices, Internet, Internet of Things, Internet-of-Things, k-nearest neighbor, machine learning-based detection techniques, machine learning-based security framework, malicious activities detection, malicious threats, medical emergency, medical information systems, Metrics, nearest neighbour methods, neural nets, patient monitoring, Pervasive computing, Pervasive Computing Security, pubcrawl, Random Forest, random forests, resilience, Resiliency, Scalability, security, SHS, Smart Healthcare System, smart healthcare systems, smart medical devices |
| Abstract | The integration of Internet-of-Things and pervasive computing in medical devices have made the modern healthcare system "smart." Today, the function of the healthcare system is not limited to treat the patients only. With the help of implantable medical devices and wearables, Smart Healthcare System (SHS) can continuously monitor different vital signs of a patient and automatically detect and prevent critical medical conditions. However, these increasing functionalities of SHS raise several security concerns and attackers can exploit the SHS in numerous ways: they can impede normal function of the SHS, inject false data to change vital signs, and tamper a medical device to change the outcome of a medical emergency. In this paper, we propose HealthGuard, a novel machine learning-based security framework to detect malicious activities in a SHS. HealthGuard observes the vital signs of different connected devices of a SHS and correlates the vitals to understand the changes in body functions of the patient to distinguish benign and malicious activities. HealthGuard utilizes four different machine learning-based detection techniques (Artificial Neural Network, Decision Tree, Random Forest, k-Nearest Neighbor) to detect malicious activities in a SHS. We trained HealthGuard with data collected for eight different smart medical devices for twelve benign events including seven normal user activities and five disease-affected events. Furthermore, we evaluated the performance of HealthGuard against three different malicious threats. Our extensive evaluation shows that HealthGuard is an effective security framework for SHS with an accuracy of 91 % and an F1 score of 90 %. |
| DOI | 10.1109/SNAMS.2019.8931716 |
| Citation Key | newaz_healthguard_2019 |
HealthGuard: A Machine Learning-Based Security Framework for Smart Healthcare Systems