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Bitcoin is the most common cryptocurrency involved in cyber scams.Cybercriminals often utilize pseudonymity and privacy protection mechanismassociated with Bitcoin transactions to make their scams virtually untraceable.The Ponzi scheme has attracted particularly significant attention among Bitcoinfraudulent activities. This paper considers a multi-class classificationproblem to determine whether a transaction is involved in Ponzi schemes orother cyber scams, or is a non-scam transaction. We design a specificallydesigned crawler to collect data and propose a novel Attention-based LongShort-Term Memory (A-LSTM) method for the classification problem. Theexperimental results show that the proposed model has better efficiency andaccuracy 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 andoutperforms the existing approaches.

An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams