Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks

Autor:	I. Segovia Dominguez, Yulia R. Gel, Murat Kantarcioglu, Cuneyt Gurcan Akcora, Dorcas Ofori-Boateng
Rok vydání:	2021
Předmět:	Clique Persistent homology Computer science Perspective (graphical) Ripple Stability (learning theory) Topological data analysis Anomaly detection Network topology Topology
Zdroj:	Machine Learning and Knowledge Discovery in Databases. Research Track ISBN: 9783030864859 ECML/PKDD (1)
DOI:	10.1007/978-3-030-86486-6_48
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d22989ee548fe4ff98856759efb71228 https://doi.org/10.1007/978-3-030-86486-6_48 Zobrazit plný text záznamu