Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Abstract

In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-Term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. Further, we include how both the technologies can be applied in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a conclusion.