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Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks

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Machine Learning and Knowledge Discovery in Databases. Research Track (ECML PKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12975))

Abstract

Motivated by the recent surge of criminal activities with cross-cryptocurrency trades, we introduce a new topological perspective to structural anomaly detection in dynamic multilayer networks. We postulate that anomalies in the underlying blockchain transaction graph that are composed of multiple layers are likely to also be manifested in anomalous patterns of the network shape properties. As such, we invoke the machinery of clique persistent homology on graphs to systematically and efficiently track evolution of the network shape and, as a result, to detect changes in the underlying network topology and geometry. We develop a new persistence summary for multilayer networks, called stacked persistence diagram, and prove its stability under input data perturbations. We validate our new topological anomaly detection framework in application to dynamic multilayer networks from the Ethereum Blockchain and the Ripple Credit Network, and demonstrate that our stacked PD approach substantially outperforms state-of-art techniques.

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Acknowledgements

This work is supported in part by NSF Grants No. ECCS 2039701, DMS 1925346, CNS 1837627, OAC 1828467, IIS 1939728, CNS 2029661 and Canadian NSERC Discovery Grant RGPIN-2020-05665. The authors would like to thank Baris Coskunuzer for insightful discussions.

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Authors and Affiliations

  1. Portland State University, Portland, USA

    D. Ofori-Boateng

  2. University of Texas at Dallas, Richardson, USA

    I. Segovia Dominguez, M. Kantarcioglu & Y. R. Gel

  3. University of Manitoba, Winnipeg, Canada

    C. Akcora

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  1. D. Ofori-Boateng
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  2. I. Segovia Dominguez
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  3. C. Akcora
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  4. M. Kantarcioglu
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  5. Y. R. Gel
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Correspondence to D. Ofori-Boateng .

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Editors and Affiliations

  1. ELLIS - The European Laboratory for Learning and Intelligent Systems, Alicante, Spain

    Nuria Oliver

  2. ETHZ and EPFL, Zürich, Switzerland

    Fernando Pérez-Cruz

  3. Johannes Gutenberg University of Mainz, Mainz, Germany

    Stefan Kramer

  4. École Polytechnique, Palaiseau, France

    Jesse Read

  5. Basque Center for Applied Mathematics, Bilbao, Spain

    Jose A. Lozano

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Ofori-Boateng, D., Dominguez, I.S., Akcora, C., Kantarcioglu, M., Gel, Y.R. (2021). Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks. In: Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12975. Springer, Cham. https://doi.org/10.1007/978-3-030-86486-6_48

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