Targeted addresses identification for bitcoin with network representation learning

Jiaqi Liang, Linjing Li, Weiyun Chen, Daniel Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019
EditorsXiaolong Zheng, Ahmed Abbasi, Michael Chau, Alan Wang, Lina Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages158-160
Number of pages3
ISBN (Electronic)9781728125046
DOIs
StatePublished - Jul 2019
Externally publishedYes
Event17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019 - Shenzhen, China
Duration: Jul 1 2019Jul 3 2019

Publication series

Name2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019

Conference

Conference17th IEEE International Conference on Intelligence and Security Informatics, ISI 2019
CountryChina
CityShenzhen
Period7/1/197/3/19

Keywords

  • Bitcoin
  • Imbalanced multi-classification
  • Network representation learning
  • Transaction address

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Vision and Pattern Recognition
  • Information Systems and Management
  • Information Systems

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  • Cite this

    Liang, J., Li, L., Chen, W., & Zeng, D. (2019). Targeted addresses identification for bitcoin with network representation learning. In X. Zheng, A. Abbasi, M. Chau, A. Wang, & L. Zhou (Eds.), 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019 (pp. 158-160). [8823249] (2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISI.2019.8823249