Discovering and Clustering Hidden Time Patterns in Blockchain Ledger

  • Anna EpishkinaEmail author
  • Sergey Zapechnikov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 636)


Currently, immutable blockchain-based ledgers become important tools for cryptocurrency transactions, auditing, smart contracts, copyright registration and many other applications. In this regard, there is a need to analyze the typical, repetitive actions written to the ledger, for example, to identify suspicious cryptocurrency transactions, a chain of events that led to information security incident, or to predict recurrence of some situation in the future. We propose to use for these purposes the algorithms for T-patterns discovering and to cluster the identified behavioral patterns subsequently. In case of having labeled patterns, clustering might be replaced by classification.


Audit trails Blockchain Data mining Classification Clustering 



Authors acknowledge support from the MEPhI Academic Excellence Project (Contract No. 02.a03.21.0005).


  1. 1.
    Nakamoto, S.B.: A peer-to-peer electronic cash system (2008).
  2. 2.
    McConaghy, T., et al.: BigchainDB: a scalable blockchain database (2016).
  3. 3.
    Wood, G.E.: A secure decentralized generalized transaction ledger (2017).
  4. 4.
    Cosba, A., et al.: Hawk: the blockchain model of cryptography and privacy-preserving smart contracts (2015).
  5. 5.
    Zyskind, G., Nathan, O., Pentland, A.: Enigma: decentralized computation platform with guaranteed privacy (2016).
  6. 6.
    Hardjono, T., Smith, N., Pentland, A.: Anonymous identities for permissioned blockchains (2016).
  7. 7.
  8. 8.
    Matsumoto, S., Reischuk, R.: IKP: turning a PKI around with blockchains (2016).
  9. 9.
    Cucurull, J., Puiggali, J.: Distributed immutabilization of secure logs (2017).
  10. 10.
    Magnusson, M.: Discovering hidden time patterns in behavior: T-patterns and their detection. Behav. Res. Methods Instrum. Comput. 32(1), 93–110 (2000)CrossRefGoogle Scholar
  11. 11.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann Pubs, San Francisco (2012)CrossRefGoogle Scholar
  12. 12.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10(8), 707–710 (1966). (In Russian)MathSciNetGoogle Scholar
  13. 13.
    Wagner, R., Fischer, M.: The string-to-string correction problem. J. Assoc. Comput. Mach. 21(I), 168–173 (1974)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Lance, G., Williams, W.: A general theory of classificatory sorting strategies I hierarchical systems. Comput. J. 9(4), 373–380 (1967)CrossRefGoogle Scholar
  16. 16.
    Tao, Y., Xiao, X., Zhou, S.: Mining distance-based outliers from large databases in any metric space. In: Proceedings of 2006 ACM SIGKDD International Conference on Knowledge Discovery in Databases (KDD 2006), Philadelphia, PA, August 2006, pp. 394–403 (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)MoscowRussia

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