  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.
