Doctorate thesis defense of Sondes KSIBI

Doctorate thesis defense on May 11^th 2024 at 09H00 AM ,in Amphitheater Ibn Rochd, SUP'COM 2.

Entitled :Modeling the Interaction of Electromagnetic Wave Radiation with Human Body by Using Method of Moment

Presented by :Sondes KSIBI

Committee

Abstract

The emergence of the concept of ubiquitous computing and the Internet of Things (IoT) paradigm allowed to create and automate a wide range of applications in different fields, healthcare is no except. E-health applications, particularly those based on connected medical devices, commonly known as the Internet of Medical Things (IoMT), are among the trends these days. However, given the complexity of the architecture of IoMT systems, their ubiquitous nature and their resource limitations, protecting the user’s data in such systems remains an issue of concern to security professionals and the research community. Controlling and adequately managing the emerging security risks are crucial aspects to address the urgent desire to ensure a maximum and a reliable security within e-health systems. A deep study of security and cyber-security risk management for online health systems have been carried out in this thesis. The analysis of well-established research efforts reveals that traditional risk management frameworks cannot be directly applied to the studied context. To this end, we propose a novel framework for managing security risks for e-health applications. The proposal considers end-to-end security risks and allows a hybrid (qualitative and quantitative) risk assessment. The framework relies on the segmentation of the risk zone into three sub-zones according to a 3-tier IoMT architecture, the data acquisition area, data collection and transmission area and the data processing and storage area, each presenting particular risk factors.

To do so, we propose a new model that relies on the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate the security attributes of connected medical devices and compare them to a defined security profile according to user needs. To deal with the risks associated with the network layer, we define a new approach based on machine learning and risk assessment techniques to analyze IoMT communications and evaluate their inherent security risks. As for the processing and storage of medical data, we focus on managing the risks of unauthorized access control issues to the medical databases. Finally, we present an automated tool box called IoMT-CySAM that allows managing security risks within IoMT. The tool could enhance security awareness among patients and medical professionals since it allows involving them in the security assessment process.

Keywords

e-Health; Internet of Medical Things (IoMT); Cyber-Security; Risk Management.