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The rapid growth in stored time-oriented data necessitates the development ofnew methods for handling, processing, and interpreting large amounts oftemporal data. One important example of such processing is detecting anomaliesin time-oriented data. The Knowledge-Based Temporal Abstraction method waspreviously proposed for intelligent interpretation of temporal data based onpredefined domain knowledge. In this study we propose a framework thatintegrates the KBTA method with a temporal pattern mining process for anomalydetection. According to the proposed method a temporal pattern mining processis applied on a dataset of basic temporal abstraction database in order toextract patterns representing normal behavior. These patterns are then analyzedin order to identify abnormal time periods characterized by a significantlysmall number of normal patterns. The proposed approach was demonstrated using adataset collected from a real server.

Anomaly Detection Using the Knowledge-based Temporal Abstraction Method