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Title: A dubiety-determining based model for database cumulated anomaly intrusion
Authors: Yi, J
Lu, G
Lü, K
Keywords: Database security; Intrusion detection; Anomaly intrusion
Issue Date: 2007
Publisher: ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)
Citation: Proceedings of the 2nd international conference on Scalable information systems, Suzhou, China, 2007
Abstract: The concept of Cumulated Anomaly (CA), which describes a new type of database anomalies, is addressed. A typical CA intrusion is that when a user who is authorized to modify data records under certain constraints deliberately hides his/her intentions to change data beyond constraints in different operations and different transactions. It happens when some appearing to be authorized and normal transactions lead to certain accumulated results out of given thresholds. The existing intrusion techniques are unable to deal with CAs. This paper proposes a detection model, Dubiety-Determining Model (DDM), for Cumulated Anomaly. This model is mainly based on statistical theories and fuzzy set theories. It measures the dubiety degree, which is presented by a real number between 0 and 1, for each database transaction, to show the likelihood of a transaction to be intrusive. The algorithms used in the DDM are introduced. A DDM-based software architecture has been designed and implemented for monitoring database transactions. The experimental results show that the DDM method is feasible and effective.
Appears in Collections:Business and Management
Brunel Business School Research Papers

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