Database Intrusion Detection using Weighted Sequence Mining

Data mining is widely used to identify interesting, potentially useful and understandable patterns from a large data repository. With many organizations focusing on web-based on-line transactions, the threat of security violations has also increased. Since a database stores valuable information of an application, its security has started getting attention. An intrusion detection system (IDS) is used to detect potential violations in database security. In every database, some of the attributes are considered more sensitive to malicious modifications compared to others. We propose an algorithm for finding dependencies among important data items in a relational database management system. Any transaction that does not follow these dependency rules are identified as malicious. We show that this algorithm can detect modification of sensitive attributes quite accurately. We also suggest an extension to the Entity-Relationship (E-R) model to syntactically capture the sensitivity levels of the attributes.