Graph embedding-based money laundering detection for Ethereum

J Liu, C Yin, H Wang, X Wu, D Lan, L Zhou, C Ge - Electronics, 2023 - mdpi.com
The number of money laundering crimes for Ethereum and the amount involved have grown
exponentially in recent years. However, previous studies related to anomaly detection for …

Ethereum fraud behavior detection based on graph neural networks

R Tan, Q Tan, Q Zhang, P Zhang, Y Xie, Z Li - Computing, 2023 - Springer
Since Bitcoin was first conceived in 2008, blockchain technology has attracted a large
amount of researchers' attention. At the same time, it has also facilitated a variety of …

Graph neural network for ethereum fraud detection

R Tan, Q Tan, P Zhang, Z Li - 2021 IEEE international …, 2021 - ieeexplore.ieee.org
Currently, the blockchain technology has been widely applied to various industries, and has
attracted wide attention. However, because of its unique anonymity, digital currency has …

Does money laundering on ethereum have traditional traits?

Q Fu, D Lint, Y Cao, J Wu - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
As the largest blockchain platform that supports smart contracts, Ethereum has developed
with an incredible speed. Yet due to the anonymity of blockchain, the popularity of Ethereum …

Ethereum fraud detection with heterogeneous graph neural networks

H Kanezashi, T Suzumura, X Liu, T Hirofuchi - arXiv preprint arXiv …, 2022 - arxiv.org
While transactions with cryptocurrencies such as Ethereum are becoming more prevalent,
fraud and other criminal transactions are not uncommon. Graph analysis algorithms and …

Early-stage phishing detection on the Ethereum transaction network

Y Wan, F Xiao, D Zhang - Soft Computing, 2023 - Springer
As cryptocurrency is widely accepted and used, attendant illegal activities have attracted
extensive attention, especially phishing scams, which bring great losses to both customers …

Anti-money laundering by group-aware deep graph learning

D Cheng, Y Ye, S Xiang, Z Ma… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Anti-money laundering (AML) is a classical data mining problem in finance applications. As
well known, money laundering (ML) is critical to the effective operation of transnational and …

TTAGN: Temporal transaction aggregation graph network for ethereum phishing scams detection

S Li, G Gou, C Liu, C Hou, Z Li, G Xiong - Proceedings of the ACM Web …, 2022 - dl.acm.org
In recent years, phishing scams have become the most serious type of crime involved in
Ethereum, the second-largest blockchain platform. The existing phishing scams detection …

Inspection-L: self-supervised GNN node embeddings for money laundering detection in bitcoin

WW Lo, GK Kulatilleke, M Sarhan, S Layeghy… - Applied …, 2023 - Springer
Criminals have become increasingly experienced in using cryptocurrencies, such as Bitcoin,
for money laundering. The use of cryptocurrencies can hide criminal identities and transfer …

Bitcoin Money Laundering Detection via Subgraph Contrastive Learning

S Ouyang, Q Bai, H Feng, B Hu - Entropy, 2024 - mdpi.com
The rapid development of cryptocurrencies has led to an increasing severity of money
laundering activities. In recent years, leveraging graph neural networks for cryptocurrency …