Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks

• Published in: Machine Learning and Knowledge Discovery in Databases. Research Track Vol. 12975; pp. 788 - 804
• Main Authors: Ofori-Boateng, D., Dominguez, I. Segovia, Akcora, C., Kantarcioglu, M., Gel, Y. R.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Anomaly detection; Blockchain transaction; Clique persistent homology; Dynamic multilayer network; Topological data analysis
• ISBN: 3030864855; 9783030864859
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-86486-6_48

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.