Detecting Cumulated Anomaly by a Dubiety Degree based detection Model
Gang Lü, Junkai Yi, Kevin Lü · 2007
The concept of cumulated anomaly is addressed in this paper, which describes a new type of database anomalies. A detection model, dubiety-determining model (DDM), for cumulated anomaly, is proposed. This model is based on statistical theories and fuzzy set theories. The DDM can measure the dubiety degree of each database transaction quantitatively. We designed software system architecture to support the DDM for monitoring database transactions. We also implemented the system and tested it. Our experimental results show that the DDM method is feasible and effective.