Abstract

The concept of Cumulated Anomaly (CA), which describes a new type of database anomalies, is addressed. It happens when the submitting times of authorized transactions or the changed data is cumulated out of some threshold. A detection model, Dubiety-Determining Model (DDM), is proposed for Cumulated Anomaly. This model is based on statistical theories and fuzzy set theories. It measures the dubiety degree, which is a real number between 0 and 1, for each database transaction quantitatively. Thus, it can be directly and quantitatively known how a transaction is anomalous on the concept of Cumulated Anomaly. In the experiment, a practical case is simulated. As we expected, our approach detects Cumulated Anomaly in it. The experimental results show that the DDM method is feasible and effective.

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