ISSN : 1796-203X
Volume : 1    Issue : 4    Date : July 2006

Database Intrusion Detection using Weighted Sequence Mining
Abhinav Srivastava, Shamik Sural and A.K. Majumdar
Page(s): 8-17
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Data mining is widely used to identify interesting, potentially useful and understandable patterns
from a large data repository. With many organizations focusing on webbased 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.

Index Terms
Data dependency, Weighted Sequence mining, Intrusion detection, E-R Model