2020/09/14 by Alexander T. M. Fisch, Fisch, Alexander T. M., Lawrence Bardwell +3 · 2 citations
Computer Science · #Anomaly Detection Techniques and Applications #Applications (stat.AP) #Computation (stat.CO) #Data Stream Mining Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Network Security and Intrusion Detection
paper · pdf · doi:10.48550/arxiv.2009.06670
openalex publication_date 2020/09/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The ability to quickly and accurately detect anomalous structure within data sequences is an inference challenge of growing importance. This work extends recently proposed post-hoc (offline) anomaly detection methodology to the sequential setting. The resultant procedure is capable of real-time analysis and categorisation between baseline and two forms of anomalous structure: point and collective anomalies. Various theoretical properties of the procedure are derived. These, together with an extensive simulation study, highlight that the average run length to false alarm and the average detection delay of the proposed online algorithm are very close to that of the offline version. Experiments on simulated and real data are provided to demonstrate the benefits of the proposed method.