Learning from sequential data for anomaly detection
Esra YOLAÇAN · 2014
Anomaly detection has been used in a wide range of real world problems and has received significant attention in a number of research fields over the last decades. Anomaly detection attempts to identify events, activities, or observations which are measurably different than an expected behavior or pattern present in a dataset. This thesis focuses on a specific set of techniques targeting the detection of anomalous behavior in a discrete, symbolic, and sequential dataset. Since profiling complex sequential data is still an open problem in anomaly detection, and given that the rate of production of sequential data in fields ranging from finance to homeland security is exploding, there is a pressing need to develop effective detection algorithms that can handle patterns in sequential information flows.