Survey of Machine Learning Methods for Database Security


Application of machine learning techniques to database security is an emerging area of research. In this chapter, we present a survey of various approaches that use machine learning/data mining techniques to enhance the traditional security mechanisms of databases. There are two key database security areas in which these techniques have found applications, namely, detection of SQL Injection attacks and anomaly detection for defending against insider threats. Apart from the research prototypes and tools, various third-party commercial products are also available that provide database activity monitoring solutions by profiling database users and applications. We present a survey of such products. We end the chapter with a primer on mechanisms for responding to database anomalies.


Intrusion Detection Anomaly Detection Machine Learn Method Role Base Access Control Database Security 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  1. 1.Ashish Kamra Purdue UniversityWest LafayetteUSA
  2. 2.Elisa Bertino Purdue UniversityWest LafayetteUSA

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