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SigTran: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks

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Advances in Knowledge Discovery and Data Mining (PAKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12712))

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Abstract

Cryptocurrency networks have evolved into multi-billion-dollar havens for a variety of disputable financial activities, including phishing, ponzi schemes, money-laundering, and ransomware. In this paper, we propose an efficient graph-based method, SigTran, for detecting illicit nodes on blockchain networks. SigTran first generates a graph based on the transaction records from blockchain. It then represents the nodes based on their structural and transactional characteristics. These node representations accurately differentiate nodes involved in illicit activities. SigTran is generic and can be applied to records extracted from different networks. SigTran achieves an \(F_1\) score of 0.92 on Bitcoin and 0.94 on Ethereum, which outperforms the state-of-the-art performance on these benchmarks obtained by much more complex, platform-dependent models.

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Reproducibility

The code and data are available at https://github.com/fpour/SigTran.

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Author information

Authors and Affiliations

  1. ECE, McGill University, Montreal, Canada

    Farimah Poursafaei & Zeljko Zilic

  2. CSC, McGill University, Montreal, Canada

    Reihaneh Rabbany

  3. Mila, Montreal, Canada

    Farimah Poursafaei & Reihaneh Rabbany

Authors
  1. Farimah Poursafaei
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  2. Reihaneh Rabbany
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  3. Zeljko Zilic
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Corresponding author

Correspondence to Farimah Poursafaei .

Editor information

Editors and Affiliations

  1. IIIT, Hyderabad, Hyderabad, India

    Kamal Karlapalem

  2. Chinese University of Hong Kong, Shatin, Hong Kong

    Hong Cheng

  3. Virginia Tech, Arlington, VA, USA

    Naren Ramakrishnan

  4. Jawaharlal Nehru University, New Delhi, India

    R. K. Agrawal

  5. IIIT Hyderabad, Hyderabad, India

    P. Krishna Reddy

  6. University of Minnesota, Minneapolis, MN, USA

    Jaideep Srivastava

  7. IIIT Delhi, New Delhi, India

    Tanmoy Chakraborty

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Poursafaei, F., Rabbany, R., Zilic, Z. (2021). SigTran: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12712. Springer, Cham. https://doi.org/10.1007/978-3-030-75762-5_3

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  • DOI: https://doi.org/10.1007/978-3-030-75762-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75761-8

  • Online ISBN: 978-3-030-75762-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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