Bitcoin Fraud Detection: A Study with Dimensionality Reduction and Machine Learning Techniques

Nuno Gomes, Artur Ferreira, Artur Ferreira

2025

Abstract

The use of cryptocurrencies corresponds to a remarkable moment in global financial markets. The nature of cryptocurrency transactions, done between cryptographic addresses, poses many challenges to identify fraudulent activities, since malicious transactions may appear as legitimate. Using data with these transactions, one may learn machine learning models targeted to identify the fraudulent ones. The transaction datasets are typically imbalanced, holding a few illicit examples, which is challenging for machine learning techniques to identify fraudulent transactions. In this paper, we investigate the use of a machine learning pipeline with dimensionality reduction techniques over Bitcoin transaction data. The experimental results show that XGBoost is the best performing method among a large set of competitors. The dimensionality reduction techniques are able to identify adequate subsets suitable for explainability purposes on the classification decision.

Paper Citation

in Harvard Style

Gomes N. and Ferreira A. (2025). Bitcoin Fraud Detection: A Study with Dimensionality Reduction and Machine Learning Techniques. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-758-0, SciTePress, pages 716-723. DOI: 10.5220/0013647400003967

in Bibtex Style

@conference{data25,
author={Nuno Gomes and Artur Ferreira},
title={Bitcoin Fraud Detection: A Study with Dimensionality Reduction and Machine Learning Techniques},
booktitle={Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2025},
pages={716-723},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013647400003967},
isbn={978-989-758-758-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Bitcoin Fraud Detection: A Study with Dimensionality Reduction and Machine Learning Techniques
SN - 978-989-758-758-0
AU - Gomes N.
AU - Ferreira A.
PY - 2025
SP - 716
EP - 723
DO - 10.5220/0013647400003967
PB - SciTePress