Digital Forensics for Medical Internet of Things

Abstract

Medical Internet of Things (MIoT) Forensics is an important subject that has received little attention. With the advancement of the Internet of Things (IoT), Machine Learning (ML), and Artificial Intelligence (AI) applications, a large amount of data gets generated with increased complexity. Consequently, it gives rise to cyber-attacks and data theft. The consequences of these malicious activities can be life-threatening. Therefore, organizations must identify these threats to implement proactive and reactive measures. Digital Forensics of medical devices focuses on reactive measures to generate evidence of unprecedented events presented in court. Digital forensics, in combination with ML, might aid in the development of a secure MIoT system that can detect cyber-attacks and provide evidence for the legal process. Implementing digital forensics in medical systems is challenging, given their complex functioning. This paper presents a novel methodology for developing a forensic-ready MIoT system with two distinct approaches: an intrusion detection system and modeling of the physiological data. In addition, we cover various digital forensic processes for MIoT systems to log evidence of malicious activity at various system levels. This research highlights the importance of digital forensics for MIoT privacy assurance.