A novel Bayesian optimizable ensemble bagged trees model for cryptocurrency fraud prediction approach

Nowadays, the prediction of cryptocurrency side effects on the critical aspects of the exchange rates in intelligent business is one of the main challenges in the financial market. Cryptocurrency is defined as a set of digital information concerning internal financial protocols of digital marketing, such as blockchain, which operates according to a decentralized architecture. On the other hand, fraud activities in Ethereum transfer and management of cryptocurrency now increase and affect safe transactional processes. This article presents a new machine‐learning approach to Ethereum fraud Detection based on Bayesian Optimizable Ensemble Bagged Trees (BOEBT) algorithm. Moreover, the main goal of this study is to derive the accuracy of the cryptocurrency prediction model using different machine‐learning algorithms and compare their evaluation parameters together. The performance of the proposed prediction model using the machine learning algorithms was evaluated by the MATLAB tool. The experimental results show that the proposed BOEBT algorithm merits achieving 99.21% accuracy and 99.14% F1‐Score to other machine learning algorithms for cryptocurrency fraud prediction.