In this paper we proposed a data mining approach for detecting malicious transactions in a Database System. Our approach concentrates on mining data dependencies among data items in the database. A data dependency miner is designed for mining data correlations from the database log. The transactions not compliant to the data dependencies mined are identified as malicious transactions. The experiment illustrates that the proposed method works effectively for detecting malicious transactions provided certain data dependencies exist in the database.

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One of the difficulties in evaluating the trustworthiness of an object in a virtual organization is the lack of sufficient information to study how the object was formed and to what level its components should be trusted. If a subject could be provided with detailed information about the ingredients of a compound object, then the subject would be able to evaluate the trust level of that compound object with higher confidence. This paper introduces a scheme using labels associated with each object within the domain of a virtual organization to facilitate trust management. Each label supplies certain information regarding the originality of the associated object. Thus, partial trust (also called component trust) can be integrated to evaluate the composite trust of the compound object. Re-labeling enables object information update to accommodate the dynamic nature of a virtual organization. Indirect trust between two subjects can be calculated based on a trust network. Different subjects may view the same object with different trust values because they trust the components of the object to different degrees. This model uses recommendations supplied by other subjects to provide a dynamic and flexible way to adjust the trustworthiness of an object for a certain subject.

This paper investigates the problem of knowledge acquisition by an unauthorized insider using dependencies between objects in relational databases. It defines various types of knowledge. In addition, it introduces the Neural Dependency and Inference Graph (NDIG), which shows dependencies among objects and the amount of knowledge that can be inferred about them using dependency relationships. Moreover, it introduces an algorithm to determine the knowledgebase of an insider and explains how insiders can broaden their knowledge about various relational database objects to which they lack appropriate access privileges. In addition, it demonstrates how NDIGs and knowledge graphs help in assessment of insider threats and what security officers can do to avoid such threats.