Legalities in Metaverse – Ethereum Fraud Detection

Abstract

The spread of the Metaverse has created new moral and legal challenges, especially when it comes to protecting against fraud. This study explores the legal complexities surrounding Ethereum-specific metaverse transactions and provides guidance on how to identify and address fraudulent activity. The focus of this investigation is the field of fraud detection in the Ethereum environment. Current methods of detecting fraud, such as Blockchain analysis and machine learning-based algorithms, are carefully examined. Next, a new approach to fraud detection is put out, which is predicated on a collection of seven machine learning algorithms: K-Nearest Neighbours (KNN), Decision Tree, Support Vector Machine (SVM), Random Forest, KNN, XGBoost (XGB), and Artificial Neural Network (ANN). The results of this study are carefully outlined, demonstrating the accuracy, recall, F1 score, and precision that each of the previously listed algorithms demonstrated. Moreover, this article lays out possible directions for further research, including the incorporation of group approaches and the investigation of creating characteristics to strengthen fraud detection abilities. This academic paper provides a significant and novel insight into the identification of fraudulent activity in Ethereum transactions, highlighting the potential benefits of using machine learning algorithms for this kind of discernment.