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

TL;DR

This paper introduces SHChecker, a framework combining machine learning and formal analysis to identify potential security threats in IoMT-based smart healthcare systems, enhancing their robustness against cyber-attacks.

Contribution

It presents a novel threat analysis framework that integrates ML and formal methods to detect attack vectors in IoMT-based healthcare systems, including black-box models.

Findings

Successfully implemented on synthetic and real datasets.

Revealed potential attack vectors in IoMT systems.

Provides insights to improve system resilience.

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.