Topological Anomaly Detection in Dynamic Multilayer Blockchain Networks
This addresses the problem of identifying criminal activities in cross-cryptocurrency trades for blockchain security, representing a domain-specific incremental improvement.
The paper tackles the problem of detecting structural anomalies in dynamic multilayer blockchain networks by introducing a topological perspective, and demonstrates that their stacked persistence diagram approach substantially outperforms state-of-the-art techniques on datasets from Ethereum and Ripple.
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.