Topological Anomaly Detection on Complex Networks

• Activity Number: 124 - Algorithms for Threat Detection
• Type: Topic-Contributed
• Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Section on Statistics in Defense and National Security
• Author(s): Dorcas Ofori-Boateng* and Ignacio Segovia Dominguez and Yulia R. Gel and Murat R. Kantargioclu and Cuneyt G Akcora
• Companies: Portland State University and UNIVERSITY OF TEXAS AT DALLAS and UNIVERSITY OF TEXAS AT DALLAS and UNIVERSITY OF TEXAS AT DALLAS and University of Manitoba
• Keywords: Anomaly detection; Persistent homology; Clique complex; Dynamic multilayer networks

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 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.