A considerable effort has been recently devoted to the development of database management systems (DBMS) which guarantee high assurance security and privacy. Organizations spend a significant amount of resources securing their servers and network perimeters. An important component of any strong security solution is represented by intrusion detection systems, able to detect anomalous behavior by applications and users. To date, however, there have been very few intrusion detection mechanisms specifically tailored to database systems. We have proposed a novel solution called Log mining approach. The approach we propose to intrusion detection is based on mining database traces stored in log files. In this Paper, we present a new technique for identifying malicious database transactions, are ideal for profiling data correlations for identifying malicious database activities. The result of the mining process is used to form user profiles that can model normal behavior and identify intruders.