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Contact Name
Andhika Rafi Hananto
Contact Email
andhikarh90@gmail.com
Phone
+62895422720524
Journal Mail Official
support@ijrm.net
Editorial Address
Puri Mersi Baru, Blok A2, Jl. Martadireja 2 Purwokerto, Kab. Banyumas,Jawa Tengah.
Location
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 51 Documents
AI-Driven Unsupervised Framework for Detecting and Analyzing Anomalous Cryptocurrency Transactions Across Blockchain Networks Using Multi-Feature Analysis Eissa M. Ali Qhal
International Journal Research on Metaverse Vol. 3 No. 3 (2026): Regular Issue September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v3i3.78

Abstract

The rapid growth of cryptocurrency networks has increased the risk of fraudulent transactions, necessitating effective and scalable detection methods. This study presents an AI-driven framework for detecting anomalous transactions in cryptocurrency datasets using unsupervised learning techniques, specifically the Isolation Forest algorithm. A dataset of 50,000 transactions was analyzed, with 500 anomalies successfully identified based on multiple features including transaction amount, fees, sender and receiver addresses, mining pools, and currency type. The results show that anomalous transactions frequently exhibit extreme values and irregular patterns that are difficult to detect using traditional rule-based approaches. Multi-feature analysis enhances detection accuracy, enabling the prioritization of high-risk transactions for manual review or automated mitigation. These findings demonstrate that unsupervised AI methods provide a robust, adaptive, and scalable solution for proactive fraud detection in blockchain networks, with the potential to integrate deeper learning models for further improvement in detecting complex fraudulent behaviors.