Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks. (arXiv:2106.01806v1 [cs.CR])

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 be also 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 show that our stacked PD approach
substantially outperforms the state-of-art techniques, yielding up to 40% gains
in precision.