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Contact Name
Andhika Rafi Hananto
Contact Email
andhikarh90@gmail.com
Phone
+62895422720524
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support@ijrm.net
Editorial Address
Puri Mersi Baru, Blok A2, Jl. Martadireja 2 Purwokerto, Kab. Banyumas,Jawa Tengah.
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Kab. banyumas,
Jawa tengah
INDONESIA
International Journal Research on Metaverse
Published by Meta Bright Indonesia
ISSN : -     EISSN : 30626927     DOI : https://doi.org/10.47738/ijrm
Core Subject : Science,
Virtual and augmented reality technologies Network infrastructure and architecture for the metaverse Digital economy and transactions in the metaverse Social and cultural aspects of virtual environments Development and design of content in the metaverse Impact of the metaverse on industries such as education, healthcare, entertainment, and business Regulation, policy, and ethics in the metaverse IJRM aims to foster interdisciplinary dialogue and collaboration, contributing to the body of knowledge that drives the adoption and evolution of metaverse technologies. Papers published in IJRM are grounded in rigorous research methods and are expected to articulate their implications for theory and practice clearly. Authors are encouraged to state their contributions to the state-of-the-art in the field explicitly. Subject Area and Category: The International Journal Research on Metaverse focuses on virtual and augmented reality, network infrastructure, digital economy, social and cultural impacts, content development, industry-specific applications, regulation and ethics, and practical case studies.
Articles 41 Documents
Hybrid Ensemble Learning for Anomaly Detection in Metaverse Transactions Using Isolation Forest, Autoencoder, and XGBoost Prakash, S.; Mary, S. Aruna; Sudhagar, G.; Batumalay, Malathy
International Journal Research on Metaverse Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i1.46

Abstract

The rapid expansion of metaverse platforms has increased the volume and complexity of digital transactions, creating a greater need for reliable anomaly detection systems. This study proposes a hybrid ensemble learning framework that integrates Isolation Forest, Autoencoder, and XGBoost using a meta learning approach to detect anomalous transactions in metaverse environments. The framework combines unsupervised and supervised learning to identify structural irregularities, behavioral deviations, and contextual patterns associated with high-risk activities. Using a transaction dataset containing behavioral, contextual, and numerical features, the hybrid model was evaluated against its individual components. The results show that the proposed framework achieves superior accuracy, precision, recall, and ROC AUC values compared to standalone models. The analysis of feature importance indicates that quantitative variables, including transaction amount, session duration, and risk score, provide the strongest predictive contribution, while contextual and behavioral factors improve model interpretability and generalization. Principal Component Analysis further visualizes the separation between normal and anomalous clusters, confirming that the hybrid ensemble effectively captures latent relationships within high-dimensional transaction data. Overall, the findings demonstrate that the proposed approach provides a robust and scalable solution for detecting irregular patterns in metaverse-based blockchain transactions. This model also offers practical implications for real-time financial risk assessment and digital security management in decentralized virtual economies.