论文标题
基于注意的长期记忆框架检测比特币骗局
An Attention-based Long Short-Term Memory Framework for Detection of Bitcoin Scams
论文作者
论文摘要
比特币是网络骗局中最常见的加密货币。网络犯罪分子通常会利用与比特币交易相关的化名和隐私保护机制，以使其骗局几乎无法追踪。庞氏骗局在比特币欺诈活动中引起了特别的关注。本文考虑了一个多级分类问题，以确定庞氏骗局或其他网络骗局的交易是否涉及交易，还是非SCAM交易。我们设计了一种专门设计的轨道来收集数据，并提出了一种针对分类问题的新型基于注意力的长期记忆（A-LSTM）方法。实验结果表明，所提出的模型比现有方法具有更好的效率和准确性，包括随机森林，额外的树木，梯度增强和经典的LSTM。有了正确识别的骗局功能，我们提出的A-LSTM可为原始数据实现超过82％的F1得分，并胜过现有方法。
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.
