Improving GNN-Based Methods for Scam Detection in Bitcoin Transactions - A Practical Case Study

Improving GNN-Based Methods for Scam Detection in Bitcoin Transactions - A Practical Case Study

The Bitcoin generator scam is one example of existing deceptive schemes enticing users with promises of free or effortless Bitcoin generation. These scams predominantly exploit individuals unfamiliar with cryptocurrency seeking low-effort avenues to obtain Bitcoin without financial investment. In th...

Full description

Saved in:
Bibliographic Details
Published in:2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA) pp. 1 - 10
Main Authors: Saggu, Karanjot Singh, Branco, Paula, Jourdan, Guy-Vincent
Format: Conference Proceeding
Language:English
Published: IEEE 06-10-2024
Subjects:
Bitcoin
Cryptocurrency
Focusing
Fraud Detection
Generators
Graph neural networks
Graph Sampling
Investment
Measurement
Optimization
Pipelines
Radio frequency
Sampling methods
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The Bitcoin generator scam is one example of existing deceptive schemes enticing users with promises of free or effortless Bitcoin generation. These scams predominantly exploit individuals unfamiliar with cryptocurrency seeking low-effort avenues to obtain Bitcoin without financial investment. In this paper, we propose and analyze methods to improve the performance of Graph Neural Networks (GNNs) in detecting fraudulent cases within Bitcoin transactional data. We explore multiple GNN variants, alongside various graph sampling methodologies. To overcome the shortcomings of these sampling methods, we propose a new sampling method BFRON-a hybrid approach mixing Breadth-First Search and Frontier Sampling. Additionally, we introduce an enhanced optimization pipeline and a new metric to improve fraudulent node detection. Evaluation metrics, including Instance Information Gain and Group Distance Ratio, are employed to analyze the challenges of over-smoothing in Graph Neural Networks and the efficacy of diverse graph sampling techniques. Our results show that overall BFRON is the best solution and RGGCN is the best-performing GNN. Moreover, we show that our enhanced pipeline and the usage of graph normalization have important advantages.
ISSN:2766-4112
DOI:10.1109/DSAA61799.2024.10722808