【Author】
Liang, Jiaqi; Li, Linjing; Chen, Weiyun; Zeng, Daniel
【Source】2019 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS (ISI)
【Abstract】The anonymity and decentralization of Bitcoin make it widely accepted in illegal transactions, such as money laundering, drug and weapon trafficking, gambling, to name a few, which has already caused significant security risk all around the world. The obvious de-anonymity approach that matches transaction addresses and users is not possible in practice due to limited annotated data set. In this paper, we divide addresses into four types, exchange, gambling, service, and general, and propose targeted addresses identification algorithms with high fault tolerance which may be employed in a wide range of applications. We use network representation learning to extract features and train imbalanced multi-classifiers. Experimental results validated the effectiveness of the proposed method.
【Keywords】Bitcoin; transaction address; network representation learning; imbalanced multi-classification
【摘要】比特币的匿名性和去中心化使得其在洗钱、毒品和武器走私、赌博等非法交易中被广泛接受，已经在全球范围内造成了重大的安全风险。由于带注释的数据集有限，匹配交易地址和用户的去匿名方法在实践中是不可能实现的。本文将地址分为交换型、赌博型、服务型和通用型四种类型，提出了容错性高的目标地址识别算法，可广泛应用。我们使用网络表示学习来提取特征并训练不平衡多分类器。实验结果验证了该方法的有效性。
【关键词】比特币;事务处理;网络学习代表;不平衡的智能化
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