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Discovering and Clustering Hidden Time Patterns in Blockchain Ledger

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

Abstract

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.

Keywords

Audit trails Blockchain Data mining Classification Clustering 

Notes

Acknowledgments

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

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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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