Classification Model with High Deviation for Intrusion Detection on System Call Traces

A new classification model for host intrusion detection based on the unidentified short sequences and RIPPER algorithm is proposed. The concepts of different short sequences on the system call traces are strictly defined on the basis of in-depth analysis of completeness and correctness of pattern databases. Labels of short sequences are predicted by learned RIPPER rule set and the nature of the unidentified short sequences is confirmed by statistical method. Experiment results indicate that the classification model increases clearly the deviation between the attack and the normal traces and improves detection capability against known and unknown attacks.