Database intrusion detection using sequential data mining approaches

The procedure of detecting any violation or trespass on the level of information in a database depends on placing the normal behaviors and practices of operations done by a transaction Afterwards, any identified pattern or behavior other than those normal patterns could be of high potential of being considered as an intrusion or violation. One of the known problems in this process is that, the accuracy of the process of detecting the frequent patterns in the database, as the algorithm applied may not detect all the patterns and this would affect in two ways. First, the database of the normal patterns would be missing. Second, some new patterns would be missed in the detection process. This paper studies and implements different sequential data mining techniques, and then proposes a new enhanced algorithm. The proposed algorithm increases the accuracy of the process and the number of detected patterns. Finally, the paper proposes a model for database intrusion detection based on the modified algorithm. The paper uses a realistic huge database for evaluating the performance and the accuracy.