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.
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Acknowledgments
Authors acknowledge support from the MEPhI Academic Excellence Project (Contract No. 02.a03.21.0005).
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Epishkina, A., Zapechnikov, S. (2018). Discovering and Clustering Hidden Time Patterns in Blockchain Ledger. In: Samsonovich, A., Klimov, V. (eds) Biologically Inspired Cognitive Architectures (BICA) for Young Scientists. BICA 2017. Advances in Intelligent Systems and Computing, vol 636. Springer, Cham. https://doi.org/10.1007/978-3-319-63940-6_35
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DOI: https://doi.org/10.1007/978-3-319-63940-6_35
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