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Data-Driven Consensus Protocol Classification Using Machine Learning

Author

Listed:
  • Marco Marcozzi

    (Institute of Data Science and Digital Technologies, Vilnius University, Akademijos Str. 4, LT-08412 Vilnius, Lithuania
    Computer Science Division, University of Camerino, via Madonna delle Carceri 7, I-62032 Camerino, Italy)

  • Ernestas Filatovas

    (Institute of Data Science and Digital Technologies, Vilnius University, Akademijos Str. 4, LT-08412 Vilnius, Lithuania)

  • Linas Stripinis

    (Institute of Data Science and Digital Technologies, Vilnius University, Akademijos Str. 4, LT-08412 Vilnius, Lithuania)

  • Remigijus Paulavičius

    (Institute of Data Science and Digital Technologies, Vilnius University, Akademijos Str. 4, LT-08412 Vilnius, Lithuania)

Abstract

The consensus protocol plays a vital role in the performance and security of a specific Distributed Ledger Technology (DLT) solution. Currently, the traditional classification of consensus algorithms relies on subjective criteria, such as protocol families (Proof of Work, Proof of Stake, etc.) or other protocol features. However, such classifications often result in representatives with strongly different characteristics belonging to the same category. To address this challenge, a quantitative data-driven classification methodology that leverages machine learning—specifically, clustering—is introduced here to achieve unbiased grouping of analyzed consensus protocols implemented in various platforms. When different clustering techniques were used on the analyzed DLT dataset, an average consistency of 78 % was achieved, while some instances exhibited a match of 100 % , and the lowest consistency observed was 55 % .

Suggested Citation

  • Marco Marcozzi & Ernestas Filatovas & Linas Stripinis & Remigijus Paulavičius, 2024. "Data-Driven Consensus Protocol Classification Using Machine Learning," Mathematics, MDPI, vol. 12(2), pages 1-18, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:221-:d:1315947
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