An Enhanced Data Protection and Security based on Machine Learning: Deep Analysis on Threat Mitigation, Challenges in Internet of Medical Things (IoMTs)

Muhammad Atif Imtiaz; Kamran Razzaq; M. Aetsam Javed; Hajra Masood; Hoor Fatima Yousaf; Hira Siddique

Authors

Muhammad Atif Imtiaz School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, NSW2522, Australia Department of Electronics Engineering, University of Engineeringand Technology Taxila, 47050, Pakistan
Kamran Razzaq The University of Northumbria Newcastle, United Kingdom
M. Aetsam Javed Department of Computer Science, Faculty of Computer Science&IT Superior University Lahore, 54000, Pakistan
Hajra Masood Bahria University Lahore Campus
Hoor Fatima Yousaf Department of Computer Science, Bahria University, LahoreCampus, 54000, Pakistan
Hira Siddique School of Mathematics and Applied Statistics, University of Wollongong, NSW 2522, Australia

Abstract

The Internet of Medical Things (IoMT) plays a vital role with modern technologies and created a wide range of opportunities in numerous industries especially in the medical field. The opportunities not only last from patient empowerment, medical education and training, remote monitoring as well as healthcare collaboration along with customized treatment, and data sharing plans. Wearable health equipment, and quality improvementinitiatives are more enhanced with the adoption of IoMT but dueto advancement IoMT sometimes faces various challenges regarding interoperability, data privacy and security as well asenhanced infrastructure costs. Due to the sensitivity of the data in the healthcare domain security and privacy become the key issue of (IoMT). This paper aims to address the implications of data fusion in IoMT, as well as the associated security challenges and their potential solutions, which are lacking in the previous studiesas mentioned in the literature. Data collected from IoMT devices has a direct impact on the accuracy of predictions becauseof its quality, quantity, and relevance. Active and Passive attacks and data security breaches are not only disastrous for IoMT but affect the whole healthcare ecosystem. Modern Cloud computing system with IoMT-sensitive data requires more security as the data is stored in the cloud and physical databases require safety from collection to protection. The storage requires to be more enhanced and improved. In this article, numerous challenges are highlighted by implementing the IoMT to the renowned Machine Learning techniques and providing security mechanisms using SDNSDN-based ML model that protects data through the cloud. The standardization of architecture and security measures may improvethe detection of security threats and compromises. Detection of threats and malware in cross-platforms is also an important part of future research that can effectively tackle the heterogeneity of the IoMT systems. In advance, IoMT-based system Cryptography and blockchain-based technologies give promising results to increase security. The findings of this research can assist numerous stakeholders in the healthcare ecosystem. The research highlights the security and privacy concerns by providing a comprehensive list of current challenges and future research directions that must be considered while developing sustainable security solutions for the IoMT infrastructure.

Keywords Security, privacy, Internet of Medical Things, IOMT, MIOT, healthcare systems, survey