Fraud Detection in the Open Metaverse: Machine Learning Approaches for Secure Connected Communities
Document Type
Conference Proceeding
Source of Publication
Lecture Notes in Computer Science
Publication Date
8-2-2026
Abstract
This study aims to enhance the safety and trustworthiness of decentralized digital ecosystems by developing a machine learning framework for fraud detection in the Open Metaverse. Blockchain transactions—including sales, purchases, transfers, scams, and phishing—were classified using Random Forest, Gradient Boosting, K-Nearest Neighbors (KNN), Gaussian Naive Bayes, Decision Tree, and AdaBoost. Models were evaluated with accuracy, precision, recall, and F1-score, with Random Forest and Gradient Boosting achieving the highest accuracy of 97.9%, followed closely by KNN at 97.7%. To mitigate the imbalance between legitimate and fraudulent transactions, preprocessing steps such as SMOTE oversampling, information gain–based feature selection, and normalization were applied. The results demonstrate that both ensemble and lightweight models can effectively detect fraudulent activities while balancing predictive performance with computational efficiency. By integrating accuracy with fairness and efficiency, the study contributes to secure and responsible fraud detection practices for connected communities in the evolving digital economy.
DOI Link
ISBN
[9783032199805]
ISSN
Publisher
Springer Nature Switzerland
Volume
16462 LNCS
First Page
160
Last Page
172
Disciplines
Computer Sciences
Keywords
AI, Blockchain, Fraud Detection, Machine Learning, Metaverse
Scopus ID
Recommended Citation
Ahmed, Omnia Osama; Hamdan, Sara Imad; Soukieh, Nour Bashar; and Ismail, Heba, "Fraud Detection in the Open Metaverse: Machine Learning Approaches for Secure Connected Communities" (2026). All Works. 8021.
https://zuscholars.zu.ac.ae/works/8021
Indexed in Scopus
yes
Open Access
no