An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams
This work addresses the challenge of identifying fraudulent transactions in Bitcoin, which is crucial for cybersecurity and financial protection, though it appears incremental as it builds on existing LSTM methods with attention mechanisms.
The paper tackles the problem of detecting Bitcoin scams, particularly Ponzi schemes, by proposing an Attention-based Long Short-Term Memory (A-LSTM) model for multi-class classification, achieving an F1-score over 82% and outperforming existing methods like Random Forest and classical LSTM.
Bitcoin is the most common cryptocurrency involved in cyber scams. Cybercriminals often utilize pseudonymity and privacy protection mechanism associated with Bitcoin transactions to make their scams virtually untraceable. The Ponzi scheme has attracted particularly significant attention among Bitcoin fraudulent activities. This paper considers a multi-class classification problem to determine whether a transaction is involved in Ponzi schemes or other cyber scams, or is a non-scam transaction. We design a specifically designed crawler to collect data and propose a novel Attention-based Long Short-Term Memory (A-LSTM) method for the classification problem. The experimental results show that the proposed model has better efficiency and accuracy than existing approaches, including Random Forest, Extra Trees, Gradient Boosting, and classical LSTM. With correctly identified scam features, our proposed A-LSTM achieves an F1-score over 82% for the original data and outperforms the existing approaches.