Anomaly Detection Techniques for the Protection of Database Systems against Insider Threats
2019-05-15T15:21:08Z (GMT) by
The mitigation of insider threats against databases is a challenging problem since insiders often have legitimate privileges to access sensitive data. Conventional security mechanisms, such as authentication and access control, are thus insufficient for the protection of databases against insider threats; such mechanisms need to be complemented with real-time anomaly detection techniques. Since the malicious activities aiming at stealing data may consist of multiple steps executed across temporal intervals, database anomaly detection is required to track users' actions across time in order to detect correlated actions that collectively indicate the occurrence of anomalies. The existing real-time anomaly detection techniques for databases can detect anomalies in the patterns of referencing the database entities, i.e., tables and columns, but are unable to detect the increase in the sizes of data retrieved by queries; neither can they detect changes in the users' data access frequencies. According to recent security reports, such changes are indicators of potential data misuse and may be the result of malicious intents for stealing or corrupting the data. In this thesis, we present techniques for monitoring database accesses and detecting anomalies that are considered early signs of data misuse by insiders. Our techniques are able to track the data retrieved by queries and sequences of queries, the frequencies of execution of periodic queries and the frequencies of referencing the database tuples and tables. We provide detailed algorithms and data structures that support the implementation of our techniques and the results of the evaluation of their implementation.