Anomaly Detection in Blockchain Transactions: A Machine Learning Approach within the Open Metaverse

Abstract

This study investigates the application of machine learning models for anomaly detection and fraud analysis in blockchain transactions within the Open Metaverse, amid the growing complexity of digital transactions in virtual spaces. Utilizing a dataset of 78,600 transactions that reflect a broad spectrum of user behaviors and transaction types, we evaluated the efficacy of several predictive models, including RandomForest, LinearRegression, SVR, DecisionTree, KNeighbors, GradientBoosting, AdaBoost, Bagging, XGB, and LightGBM, based on their Mean Cross-Validation Mean Squared Error (Mean CV MSE). Our analysis revealed that ensemble methods, particularly RandomForest and Bagging, demonstrated superior performance with Mean CV MSEs of -0.00445 and -0.00415, respectively, thereby highlighting their robustness in the complex transaction dataset. In contrast, LinearRegression and SVR were among the least effective, with Mean CV MSEs of -224.67 and -468.57, indicating a potential misalignment with the dataset's characteristics. This research underlines the importance of selecting appropriate machine learning strategies in the context of blockchain transactions within the Open Metaverse, showcasing the need for advanced, adaptable approaches. The findings contribute significantly to the financial technology field, particularly in enhancing security and integrity within virtual economic systems, and advocate for a nuanced approach to anomaly detection and fraud analysis in blockchain environments.