| Title | Star-Bridge: a topological multidimensional subgraph analysis to detect fraudulent nodes and rings in telecom networks |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Fidalgo, Pedro, Lopes, Rui J., Faloutsos, Christos |
| Conference Name | 2022 IEEE International Conference on Big Data (Big Data) |
| Keywords | Big Data, big data security metrics, Bridges, Bridging Centrality, Fraud Enabler, Fraud Networks, Fraud Type, Industries, Measurement, network control, Network topology, pubcrawl, resilience, Resiliency, Scalability, Shape, Stars |
| Abstract | Fraud mechanisms have evolved from isolated actions performed by single individuals to complex criminal networks. This paper aims to contribute to the identification of potentially relevant nodes in fraud networks. Whilst traditional methods for fraud detection rely on identifying abnormal patterns, this paper proposes STARBRIDGE: a new linear and scalable, ranked out, parameter free method to identify fraudulent nodes and rings based on Bridging, Influence and Control metrics. This is applied to the telecommunications domain where fraudulent nodes form a star-bridge-star pattern. Over 75% of nodes involved in fraud denote control, bridging centrality and doubled the influence scores, when compared to non-fraudulent nodes in the same role, stars and bridges being chief positions. |
| DOI | 10.1109/BigData55660.2022.10020714 |
| Citation Key | fidalgo_star-bridge_2022 |
Star-Bridge: a topological multidimensional subgraph analysis to detect fraudulent nodes and rings in telecom networks