Toward Detecting Illegal Transactions on Bitcoin Using Machine-Learning Methods

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1156)


As an emergent electronic payment system, Bitcoin has attracted attention for its desirable features such as disintermediation, decentralization, and tamper-proof recording of data. The Bitcoin network also employs public key cryptography to prevent the disclosure of information related to participating users. Although the public key cryptography ensures the privacy and hides the true identity of users in the Bitcoin network, it has recently been abused for illegal activities that have tarnished the charm of this novel technology. Detecting the illegal transactions associated with illicit activities in Bitcoin is therefore imperative. This paper proposes a machine-learning based approach that classifies Bitcoin transactions as illegal or legal. The detected illegal transactions can be excluded from the subsequent block, promoting user acceptance and adoption of the Bitcoin technology.


Bitcoin Illegal transaction detection Classification Bitcoin transaction analysis Transaction feature extraction 



This work was supported by the ICT R&D program of MSIT/IITP. [No. 2018-0-00539, Development of Blockchain Transaction Monitoring and Analysis Technology] This work was also supported by the ICT R&D program of MSIT/IITP. [No. 2018-0-00749, Development of virtual network management technology based on artificial intelligence].

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2017- 0-01633) supervised by the IITP (Institute for Information & communications Technology Promotion).


  1. 1.
    Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2008)Google Scholar
  2. 2.
    Swan, M.: Blockchain: Blueprint for a New Economy. O’Reilly Media Inc., Sebastopol (2015)Google Scholar
  3. 3.
    Harvey, C.R.: Bitcoin myths and facts (2014)Google Scholar
  4. 4.
  5. 5.
    Pal, M.: Random forest classifier for remote sensing classification. Int. J. Remote. Sens. 26(1), 217–222 (2005)CrossRefGoogle Scholar
  6. 6.
    Zurada, J.M.: Introduction to Artificial Neural Systems, vol. 8. West Publishing Company, St. Paul (1992)Google Scholar
  7. 7.
    Zambre, D., Shah, A.: Analysis of Bitcoin network dataset for fraud. Unpublished Report (2013)Google Scholar
  8. 8.
    Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a k-means clustering algorithm. J. R. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)zbMATHGoogle Scholar
  9. 9.
    Toyoda, K., Ohtsuki, T., Mathiopoulos, P.T.: Identification of high yielding investment programs in Bitcoin via transactions pattern analysis. In: 2017 IEEE Global Communications Conference, GLOBECOM 2017, Singapore, pp. 1–6 (2017)Google Scholar
  10. 10.
  11. 11.
  12. 12.
  13. 13.
  14. 14.
  15. 15.
    Btcoin core json apis.
  16. 16.
  17. 17.
  18. 18.
    Goutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 345–359. Springer, Heidelberg (2005). Scholar
  19. 19.
    Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 67(2), 301–320 (2005)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Srivastava, N., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPOSTECHPohangKorea

Personalised recommendations