Fraud Detection in Cryptocurrency Networks—An Exploration Using Anomaly Detection and Heterogeneous Graph Transformers
Blockchains are the backbone behind cryptocurrency networks, which have developed rapidly in the last two decades. However, this growth has brought several challenges due to the features of these networks, specifically anonymity and decentralization. One of these challenges is the fight against frau...
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MDPI AG
2025-01-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/1/44 |
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| author | Víctor Pérez-Cano Francisco Jurado |
| author_facet | Víctor Pérez-Cano Francisco Jurado |
| author_sort | Víctor Pérez-Cano |
| collection | DOAJ |
| description | Blockchains are the backbone behind cryptocurrency networks, which have developed rapidly in the last two decades. However, this growth has brought several challenges due to the features of these networks, specifically anonymity and decentralization. One of these challenges is the fight against fraudulent activities performed in these networks, which, among other things, involve financial schemes, phishing attacks or money laundering. This article will address the problem of identifying fraud cases among a large set of transactions extracted from the Bitcoin network. More specifically, our study’s goal was to find reliable techniques to label Bitcoin transactions, taking into account their features. The approach followed involved two kinds of Machine Learning methods. On the one hand, anomaly detection algorithms were applied to determine whether fraudulent activities tend to show anomalous behaviour without resorting to manually obtained labels. On the other hand, Heterogeneous Graph Transformers were used to leverage the heterogeneous relational nature of the cryptocurrency information. As a result, the article will provide reasonable conclusions to acknowledge that unsupervised approaches can be useful for fraud detection on blockchain networks. Furthermore, the effectiveness of supervised graph methods was revalidated, emphasizing the importance of data heterogeneity. |
| format | Article |
| id | doaj-art-e34d6f6b89b140048d8e9713652d1a75 |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-e34d6f6b89b140048d8e9713652d1a752025-01-24T13:33:40ZengMDPI AGFuture Internet1999-59032025-01-011714410.3390/fi17010044Fraud Detection in Cryptocurrency Networks—An Exploration Using Anomaly Detection and Heterogeneous Graph TransformersVíctor Pérez-Cano0Francisco Jurado1Department of Computer Engineering, Universidad Autónoma de Madrid, 28049 Madrid, SpainDepartment of Computer Engineering, Universidad Autónoma de Madrid, 28049 Madrid, SpainBlockchains are the backbone behind cryptocurrency networks, which have developed rapidly in the last two decades. However, this growth has brought several challenges due to the features of these networks, specifically anonymity and decentralization. One of these challenges is the fight against fraudulent activities performed in these networks, which, among other things, involve financial schemes, phishing attacks or money laundering. This article will address the problem of identifying fraud cases among a large set of transactions extracted from the Bitcoin network. More specifically, our study’s goal was to find reliable techniques to label Bitcoin transactions, taking into account their features. The approach followed involved two kinds of Machine Learning methods. On the one hand, anomaly detection algorithms were applied to determine whether fraudulent activities tend to show anomalous behaviour without resorting to manually obtained labels. On the other hand, Heterogeneous Graph Transformers were used to leverage the heterogeneous relational nature of the cryptocurrency information. As a result, the article will provide reasonable conclusions to acknowledge that unsupervised approaches can be useful for fraud detection on blockchain networks. Furthermore, the effectiveness of supervised graph methods was revalidated, emphasizing the importance of data heterogeneity.https://www.mdpi.com/1999-5903/17/1/44blockchaincryptocurrencyfraudulent activity detectionmachine learning |
| spellingShingle | Víctor Pérez-Cano Francisco Jurado Fraud Detection in Cryptocurrency Networks—An Exploration Using Anomaly Detection and Heterogeneous Graph Transformers Future Internet blockchain cryptocurrency fraudulent activity detection machine learning |
| title | Fraud Detection in Cryptocurrency Networks—An Exploration Using Anomaly Detection and Heterogeneous Graph Transformers |
| title_full | Fraud Detection in Cryptocurrency Networks—An Exploration Using Anomaly Detection and Heterogeneous Graph Transformers |
| title_fullStr | Fraud Detection in Cryptocurrency Networks—An Exploration Using Anomaly Detection and Heterogeneous Graph Transformers |
| title_full_unstemmed | Fraud Detection in Cryptocurrency Networks—An Exploration Using Anomaly Detection and Heterogeneous Graph Transformers |
| title_short | Fraud Detection in Cryptocurrency Networks—An Exploration Using Anomaly Detection and Heterogeneous Graph Transformers |
| title_sort | fraud detection in cryptocurrency networks an exploration using anomaly detection and heterogeneous graph transformers |
| topic | blockchain cryptocurrency fraudulent activity detection machine learning |
| url | https://www.mdpi.com/1999-5903/17/1/44 |
| work_keys_str_mv | AT victorperezcano frauddetectionincryptocurrencynetworksanexplorationusinganomalydetectionandheterogeneousgraphtransformers AT franciscojurado frauddetectionincryptocurrencynetworksanexplorationusinganomalydetectionandheterogeneousgraphtransformers |