Machine learning proposed approach for detecting database intrusions in RBAC enabled databases

• Published in: 2010 International Conference on Computing, Communication and Networking Technologies pp. 1 - 4
• Main Authors: Rao, Udai Pratap, Sahani, G J, Patel, Dhiren R
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2010
• Subjects: Correlation; Data mining; Database security; Database systems; Intrusion detection; Machine learning; Malicious transactions; Probability; RBAC mechanism
• ISBN: 1424465915; 9781424465910
• DOI: 10.1109/ICCCNT.2010.5591574

Information is valuable asset of any organization which is stored in databases. Data in such databases may contain credit card numbers, social security number or personal medical records etc. Failing to protect these databases from intrusions will result in loss of customer's confidence and might even result in lawsuits. Traditional database security mechanism does not design to detect anomalous behavior of database users. There are number of approaches to detect intrusions in network. But they cannot detect intrusions in database. There have been very few ID mechanisms specifically tailored to database systems. We propose transaction level approach to detect malicious behavior in database systems enabled with Role Based Access Control (RBAC) mechanism.