PHASE: Security Analyzer for Next-Generation Smart Personalized Smart Healthcare System

With the advent of the connected healthcare systems, the contemporary healthcare system is going through a swift transformation to handle the ever-growing healthcare needs. The internet of medical things (IoMT) network and implantable medical devices (IMDs) are progressively being adopted in healthcare facilities for increasing efficiency and reducing treatment latency, thus giving rise to a smart healthcare system (SHS). Moreover, the acquisition of the personalized healthcare concept with SHS is boosting precise medication in real-time. However, the open network communication of IoMT sensor measurements collected from body sensor devices (BSDs) is vulnerable to measurement manipulation attacks since they are primarily encrypted or enciphered with lightweight cryptographic algorithms due to computational constraints. Hence, it is crucial to analyze the robustness of the SHS and real-time sensor measurements' vulnerability analysis to prevent mistreatment. This paper presents PHASE, a novel real-time security analysis framework for personalized rule-based SHS. Our framework can synthesize optimal attack vectors for measurement alteration attacks, each representing minimal required alterations to misinform the SHS controller with wrong patients' health status. The identified attack vectors can assess the vulnerability of the measurements in real-time with variable attacker's capability. We verify the effectiveness of the proposed framework using Pima Indians Diabetes, AIM-94, and Harvard Dataverse datasets.