A Novel Graph-Based Framework for Cryptocurrency Fraud Detection

DOI

10.2991/978-94-6463-811-0_104How to use a DOI?

Keywords

Cryptocurrency; Blockchain; Data Mining; Fraud Detection

Abstract

Cryptocurrencies have been widely applied in in-dustries such as finance and physical trade, with Bitcoin and Ethereum becoming the mainstream cryptocurrencies. However, cryptocurrency transactions face numerous security issues, such as money laundering, Ponzi schemes, and high-investment-plan scams. As a product of blockchain, cryptocurrency transactions possess the characteristics of anonymity and immutability. While this anonymity protects the privacy of the parties involved in a transaction, it significantly increases the difficulty for security agencies and government institutions to monitor and regulate these transactions. Although existing methods for fraud detection achieve high accuracy, they often lack interpretability and fail to help security teams identify the entities linked to fraudulent transactions or offer insights into similar fraudulent patterns. To address these issues and improve the interpretability of fraud detection, we propose an innovative graph-based framework. By gathering multi-dimensional data, we can provide a more detailed and holistic view of each transaction record. We develop a heterogeneous graph to model transaction entities, their related transaction records, and transaction flows. Using graph fusion and reasoning techniques, this model aids in analyzing market and entity behaviors and supports heuristic exploration by experts. Finally, a pre-trained Graph Neural Network (GNN) is utilized to quickly pinpoint fraudulent entities and their associated transactions within the graph.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rongyu Yang
PY  - 2025
DA  - 2025/08/14
TI  - A Novel Graph-Based Framework for Cryptocurrency Fraud Detection
BT  - Proceedings of the 2025 5th International Conference on Enterprise Management and Economic Development (ICEMED 2025)
PB  - Atlantis Press
SP  - 964
EP  - 972
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-811-0_104
DO  - 10.2991/978-94-6463-811-0_104
ID  - Yang2025
ER  -