ASCP-IoMT: AI-Enabled Lightweight Secure Communication Protocol for Internet of Medical Things

The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be very helpful for the prediction of some phenomena, such as chances of getting a heart attack and possibility of a tumor, from the collected and analysed healthcare data. To mitigate these issues, in this paper, a new AI-enabled lightweight, secure communication scheme for an IoMT environment has been designed and named as ASCP-IoMT, in short. The security analysis of ASCP-IoMT is performed in different ways, such as an informal way and a formal way (through the random oracle model). ASCP-IoMT performs better than other similar schemes and provides superior security with extra functionality features as compared those for the existing state of art solutions. A practical implementation of ASCP-IoMT is also performed in order to measure its impact on various network performance parameters. The end to end delay values of ASCP-IoMT are 0.01587, 0.07440 and 0.17097 seconds and the throughput values of ASCP-IoMT are 5.05, 10.88 and 16.41 bits per second (bps) under the different considered cases, respectively. For AI-based Big data analytics phase, the values of computation time (seconds) for decision tree, support vector machine (SVM), and logistic regression are measured as 0.19, 0.23, and 0.27, respectively. Moreover, the different values of accuracy for decision tree, SVM and logistic regression are 84.24%, 87.57%, and 85.20%, respectively. From these values, it is clear that decision tree method requires less time than the other considered techniques, whereas accuracy is high in case of SVM

Authentication schemes for Smart Mobile Devices: Threat Models, Countermeasures, and Open Research Issues

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a comprehensive investigation of authentication schemes for smart mobile devices. We start by providing an overview of existing survey articles published in the recent years that deal with security for mobile devices. Then, we give a classification of threat models in smart mobile devices in five categories, including, identity-based attacks, eavesdropping-based attacks, combined eavesdropping and identity-based attacks, manipulation-based attacks, and service-based attacks. This is followed by a description of multiple existing threat models. We also provide a classification of countermeasures into four types of categories, including, cryptographic functions, personal identification, classification algorithms, and channel characteristics. According to the characteristics of the countermeasure along with the authentication model iteself, we categorize the authentication schemes for smart mobile devices in four categories, namely, 1) biometric-based authentication schemes, 2) channel-based authentication schemes, 3) factors-based authentication schemes, and 4) ID-based authentication schemes. In addition, we provide a taxonomy and comparison of authentication schemes for smart mobile devices in form of tables. Finally, we identify open challenges and future research directions