Deep Learning for Anomaly Detection and Fraud Analysis in Blockchain Transactions of the Open Metaverse - Gregorius Airlangga (Jurnal Informatika Ekonomi Bisnis, 2024)
This study investigates the application of deep learning models for anomaly detection and fraud analysis within blockchain transactions of the Open Metaverse. Given the burgeoning complexity and scale of virtual environments, ensuring the integrity and security of blockchain transactions is paramount. We employed three deep learning architectures: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), to analyze and predict transactional anomalies. Using a dataset comprising 78,600 records of metaverse transactions, each model was rigorously evaluated through a 5-fold cross-validation approach, focusing on the Mean Squared Error (MSE) as the primary performance metric. The MLP model demonstrated superior predictive accuracy with the lowest average CV MSE, suggesting its effectiveness in capturing the intricate patterns of blockchain transactions. The study's findings highlight the nuanced capabilities of each model in addressing the specific challenges of fraud analysis and anomaly detection in the metaverse's blockchain environment. By providing a comparative analysis of these deep learning approaches, this research contributes to the strategic development of security measures in the Open Metaverse, promoting a secure and trustworthy digital economy.