Security and Privacy in IoT Enabled Modern Applications Using Deep/Machine Learning and Blockchain Technology

Dear Colleagues,

The security and privacy of users have become significant concerns due to the involvement of the Internet of Things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace, making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a potential victim of hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases. The generated results can be used to predict and detect vulnerabilities in IoT-based systems.

Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on recent years' efforts to address both security and privacy issues in vulnerable IoT devices using ML algorithms and BC techniques.

In this Special Issue proposal, we have summarized the research areas to focus on and the title related to cutting-edge topics, addressing security and privacy issues using ML algorithms and BC techniques in the IoT domain. The Special Issue emphasizes the operational elements of the IoT using machine learning techniques that facilitate intelligence in security and privacy challenges in the real world and in cyberspace. Additionally, we will invite authors to submit case studies based on categorizing various security and privacy threats reported in the past few years (especially during the COVID-19 pandemic) in the IoT domain, along with original research papers as well as survey papers.

Dr. Piyush Kumar Shukla
Dr. Manoj Kumar
Dr. Xiaochun Cheng
Dr. Prashant Kumar Shukla
Guest Editors

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