Machine Learning Approaches in Blockchain Technology-Based IoT Security: An Investigation on Current Developments and Open Challenges

Abstract

In recent times, the implementation of blockchain technology has gained wide popularity in various applications such as healthcare, Internet of Things (IoT), and business, due to its critical security features. However, the rapid advancement of Internet of Things (IoT) platforms has also attracted cyber attackers, leading to numerous vulnerabilities. To address this issue, researchers have explored the combination of blockchain technology with machine learning algorithms. While there have been significant breakthroughs in this area, it is evident that certain limitations exist, making the approach unsuitable for all case studies. This book chapter explores the intersection of machine learning, blockchain technology, and the Internet of Things (IoT) in the context of security. Beginning with an overview of blockchain, IoT, and the role of machine learning techniques in blockchain technology, the chapter then delves into an investigation of relevant articles that employ machine learning-based blockchain technology in IoT security. The chapter also recognizes the current developments and open challenges in this field, highlighting potential areas for improvement and further study. By combining these two powerful technologies, the chapter aims to enhance security in IoT systems built on blockchain technology, while also addressing the limitations and exploring avenues for future research.