Lo, Tzu-HsiangTzu-HsiangLoLin, Tsung-NanTsung-NanLin2025-11-212025-11-212025-10-28https://www.scopus.com/record/display.uri?eid=2-s2.0-105020456134&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/733925Online services face increasing threats from various anomalous activities. Although regulatory organizations have identified multiple red-flag behaviors, many existing detection methods focus primarily on groups of related accounts. However, these methods are not able to distinguish whether an account that does not relate to others is anomalous or not, because an individual account shows no clustering characteristics on the graph. We name this kind of the lack of detecting vision as “single anomaly problem”. To address this issue, we propose ASIAN—a unified anomaly detection framework. First, drawing from regulatory reports, we define an account or a group sharing an unusually high number of identifiers as abnormal. Unlike traditional approaches that rely on account-to-account relationships, our framework models the connections between accounts and their associated identifiers, formulating an objective function for our nonnegative matrix factorization (NMF)-based method. This low-rank approximation technique filters out low-significance data during loss minimization, enabling the extraction of high-volume patterns of identifier usage that indicate abnormal behavior. Finally, we define the involved identifiers as anomalous identifiers and trace back the accounts associated with them, whether they belong to individuals or groups, marking these accounts as anomalous. Experimental results show that ASIAN can indeed detect individual and group anomalies, while achieving a higher F1-score than alternative methods. This design not only improves the accuracy of the detection, but also clearly interprets how each component contributes to overall performance, thereby providing valuable insights into online service anomaly detection.trueAccount identity relationdata clusteringfraud detectionorthogonal nonnegative matrix factorization (ONMF)[SDGs]SDG16journal article10.1109/tcss.2025.36177852-s2.0-105020456134