An Ensemble based Fraudulent Blockchain Account Detection System

This paper addresses the issue of fraud in blockchain transactions, which can significantly undermine trust and financial stability within blockchain networks. By leveraging advanced ensemble learning techniques, it aims to detect and prevent fraudulent activities effectively. The analysis focuses on blockchain networks, utilizing a combination of sophisticated machine learning algorithms such as Support Vector Machine (SVM), random forests, Tabnet, XGBoost, and the deep learning model MLP. These algorithms meticulously analyze transaction data for unusual patterns indicating fraud. The ensemble approach distinguishes between legitimate and fraudulent accounts by accurately analyzing transaction patterns. Notably, the ensemble model achieved a testing accuracy of 93.6%, thereby aiming to reduce the potential financial and reputational risks associated with fraud in blockchain transactions.