{rfName}
Fr

Indexed in

License and use

Citations

Altmetrics

Grant support

This research has been partially supported by the Center for Research in Forensic and Security Sciences of the Universidad Autonoma de Madrid (ICFS-UAM) (in Spanish Centro de Investigacion en Ciencias Forenses y de la Seguridad de la Universidad Autonoma de Madrid, ICFS-UAM).

Analysis of institutional authors

Jurado, FranciscoCorresponding Author
Share
Publications
>
Article

Fraud Detection in Cryptocurrency Networks-An Exploration Using Anomaly Detection and Heterogeneous Graph Transformers

Publicated to:Future Internet. 17 (1): 44- - 2025-01-01 17(1), DOI: 10.3390/fi17010044

Authors: Perez-Cano, Victor; Jurado, Francisco

Affiliations

Univ Autonoma Madrid, Dept Comp Engn, Madrid 28049, Spain - Author

Abstract

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.

Keywords

Quality index

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-04-30:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 5.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 4 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.85.
  • The number of mentions on the social network X (formerly Twitter): 2 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Perez-Cano, Victor) and Last Author (JURADO MONROY, FRANCISCO).

the author responsible for correspondence tasks has been JURADO MONROY, FRANCISCO.