Mapping the recent landscape of anomaly detection beyond cybersecurity and finance: a Web of Science bibliometric and topic-modeling review

Anomaly detection is widely used for monitoring complex systems, yet most surveys and benchmarks emphasize cybersecurity and financial fraud as common use cases, and leave other application areas under-mapped. This paper uses a bibliometric and topic modeling approach to chart recent anomaly detection research beyond these two dominant domains. We analyze 4,362 Web of Science articles published between 2022-2025 on anomaly detection, filtered to exclude finance- and security- related work. The Latent Dirichlet Allocation uncovers 12 topics, interpreted as nine application-oriented domains and three cross-domain, general topics. The application topics span behavioral and environmental pattern anomalies, energy systems faults and maintenance, process control and industrial operations, IoT edge–cloud and graph monitoring, industrial visual inspection, healthcare and biomedical diagnosis, hyperspectral remote sensing, and video surveillance, with the topic prevalence and temporal trends showing that behavioral and environmental patterns make up the largest share of work, while industrial visual inspection has grown most rapidly over the analyzed time frame. We also determine the main disciplinary anchors and publication venues by relating the topics to Web of Science categories and to clusters of strongly coupled journals.