A Novel Framework for Threat Analysis of Machine Learning-based Smart Healthcare Systems

• URL: http://arxiv.org/abs/2103.03472v1
• Date: Fri, 5 Mar 2021 04:51:02 GMT
• Title: A Novel Framework for Threat Analysis of Machine Learning-based Smart Healthcare Systems
• Authors: Nur Imtiazul Haque, Mohammad Ashiqur Rahman, Md Hasan Shahriar, Alvi Ataur Khalil and Selcuk Uluagac
• Abstract summary: We propose SHChecker, a novel threat analysis framework that integrates machine learning and formal analysis capabilities. We implement SHChecker on a synthetic and a real dataset, which affirms that our framework can reveal potential attack vectors in an IoMT system.
• Score: 2.519906683279153
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Smart healthcare systems (SHSs) are providing fast and efficient disease treatment leveraging wireless body sensor networks (WBSNs) and implantable medical devices (IMDs)-based internet of medical things (IoMT). In addition, IoMT-based SHSs are enabling automated medication, allowing communication among myriad healthcare sensor devices. However, adversaries can launch various attacks on the communication network and the hardware/firmware to introduce false data or cause data unavailability to the automatic medication system endangering the patient's life. In this paper, we propose SHChecker, a novel threat analysis framework that integrates machine learning and formal analysis capabilities to identify potential attacks and corresponding effects on an IoMT-based SHS. Our framework can provide us with all potential attack vectors, each representing a set of sensor measurements to be altered, for an SHS given a specific set of attack attributes, allowing us to realize the system's resiliency, thus the insight to enhance the robustness of the model. We implement SHChecker on a synthetic and a real dataset, which affirms that our framework can reveal potential attack vectors in an IoMT system. This is a novel effort to formally analyze supervised and unsupervised machine learning models for black-box SHS threat analysis.

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