Biblio

This paper presents, PriDe, a model to measure the deviation of an analyst's (user) querying behaviour from normal querying behaviour. The deviation is measured in terms of privacy, that is to say, how much of the privacy loss has incurred due to this shift in querying behaviour. The shift is represented in terms of a score - a privacy-loss score, the higher the score the more the loss in privacy. Querying behaviour of analysts are modelled using n-grams of SQL query and subsequently, behavioural profiles are constructed. Profiles are then compared in terms of privacy resulting in a quantified score indicating the privacy loss.

The heart of any enterprise is its databases where the application data is stored. Organizations frequently place certain access control mechanisms to prevent access by unauthorized employees. However, there is persistent concern about malicious insiders. Anomaly-based intrusion detection systems are known to have the potential to detect insider attacks. Accurate modelling of insiders behaviour within the framework of Relational Database Management Systems (RDBMS) requires attention. The majority of past research considers SQL queries in isolation when modelling insiders behaviour. However, a query in isolation can be safe, while a sequence of queries might result in malicious access. In this work, we consider sequences of SQL queries when modelling behaviours to detect malicious RDBMS accesses using frequent and rare item-sets mining. Preliminary results demonstrate that the proposed approach has the potential to detect malicious RDBMS accesses by insiders.