Analysis of Blockchain Transaction Patterns and Risk Assessment in the Open Metaverse

Title

Analysis of Blockchain Transaction Patterns and Risk Assessment in the Open Metaverse

Creator

Akash, Madanu; Ancy, P.R.

Description

This research explores the use of an ensemble comprising Random Forest, XG-Boost, and Isolation Forest algorithms for anomaly detection and risk analysis of blockchain transactions in the Open Metaverse. We created a multi-phase strategy using temporal feature engineering, risk assessment frameworks, and behavioral pattern analysis using an extensive dataset of 78,600 transactions. The ensemble model achieved 99% classification accuracy and zero false positives for valid transactions, while effectively detecting phishing (84% precision, 68% recall) and scam activities (81% precision, 91% recall). With 56.19% of the categorization, risk assessment measures were the most important predictive indicators, followed by transaction patterns and behavioral attributes. Our cross-validation analysis resulted exceptional stability in performance, with standard deviation of 0.0004 across diverse transaction scenarios. This confirms that our methodology can reliably detect intricate risk patterns, playing a crucial role in strengthening transaction security as the Open Metaverse continues to evolve. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Source

Lecture Notes in Networks and Systems;Volume;1722 LNNS;pp.255-266

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Anomaly Detection; Blockchain Security; Metaverse Transactions

Coverage

Akash M., Computer Science and Engineering Department, School of Engineering and Technology, CHRIST (Deemed to Be University), Bangalore, India; Ancy P.R., Computer Science and Engineering Department, School of Engineering and Technology, CHRIST (Deemed to Be University), Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-303210755-8;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-032-10756-5_20

https://www.scopus.com/pages/publications/105028358272?origin=resultslist