Machine Learning Adoption in Blockchain-Based Smart Applications

Abstract

The development of blockchain technology (BT) in recent years has made it a distinctive, revolutionary, and popular innovation. Information security and confidentiality are prioritised by the decentralised database in BT. Additionally, the consensus process in it ensures the validity and security of the data. However, it brings up fresh security concerns including majority assault and the double expenditures. Data analytics using cryptocurrency sensitive data are needed to address the aforementioned problems. These dataset' analytics highlight the value of recently developed techniques such as machine learning (ML). ML uses a reasonable quantity of data to generate accurate predictions. In ML, data exchange and dependability are essential to enhancing the precision of outcomes. Results from the fusion of these two technologies (ML and BT) may be quite exact. In this research, we give a thorough investigation into the use of machine learning (ML) to strengthen the security of BT-based intelligent systems. The assaults on a blockchain-based network may be analysed using a variety of classic machine learning (ML) approaches, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Clustering, Bagging, and Support Vector Machines (SVM) (LSTM).We also discuss how the two technologies may be used together in a number of advanced areas, including smart urban, the national grid, medicine, and autonomous aerial vehicles (UAVs). The difficulties and concerns facing future research are then examined. Finally, a study based with a thorough analysis is offered.