A Novel Similar Temporal System Call Pattern Mining for Efficient Intrusion Detection

Software security pattern mining is the recent research interest among researchers working in the areas of security and data mining. When an application runs, several process and system calls associated are invoked in background. In this paper, the major objective is to identify the intrusion using temporal pattern mining. The idea is to find normal temporal system call patterns and use these patterns to identify abnormal temporal system call patterns. For finding normal system call patterns, we use the concept of temporal association patterns. The reference sequence is used to obtain temporal association system call patterns satisfying specified dissimilarity threshold. To find similar (normal) temporal system call patterns, we apply our novel method which performs only a single database scan, reducing unnecessary extra overhead incurred when multiple scans are performed thus achieving space and time efficiency. The importance of the approach coins from the fact that this is first single database scan approach in the literature. To find if a given process is normal or abnormal, it is just sufficient to verify if there exists a temporal system call pattern which is not similar to the reference system call support sequence for specified threshold. This eliminates the need for finding decision rules by constructing decision table. The approach is efficient as it eliminates the need for finding decision rules (2(n) is usually very large for even small value of n) and thus aims at efficient dimensionality reduction as we consider only similar temporal system call sequence for deciding on intrusion.