Visible to the public Targeted Addresses Identification for Bitcoin with Network Representation Learning

TitleTargeted Addresses Identification for Bitcoin with Network Representation Learning
Publication TypeConference Paper
Year of Publication2019
AuthorsLiang, Jiaqi, Li, Linjing, Chen, Weiyun, Zeng, Daniel
Conference Name2019 IEEE International Conference on Intelligence and Security Informatics (ISI)
Date PublishedJuly 2019
ISBN Number978-1-7281-2504-6
Keywordsaddresses identification algorithms, annotated data, bitcoin, bitcoin security, cryptocurrencies, data mining, de-anonymity approach, Decision trees, drugs, Fault tolerance, feature extraction, Human Behavior, illegal transactions, imbalanced multi-classification, learning (artificial intelligence), network representation learning, pattern classification, pubcrawl, Scalability, security of data, security risk, transaction address, transaction addresses

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.

Citation Keyliang_targeted_2019