Using Decision Trees to Improve Signature-Based Intrusion Detection

Abstract

Most deployed intrusion detection systems (IDSs) follow a signature-based approach where attacks are identified by matching each input event against predefined signatures that model malicious activity. This matching process accounts for the most resource intensive task of an IDS. Many systems perform the matching by comparing each input event to all rules sequentially. This is far from being optimal. Although sometimes ad-hoc optimizations are utilized, no general solution to this problem has been proposed so far.

This paper describes an approach where machine learning clustering techniques are applied to improve the matching process. Given a set of signatures (each dictating a number of constraints the input data must fulfill to trigger it) an algorithm generates a decision tree that is used to find malicious events using as few redundant comparisons as possible.

This general idea has been applied to a network-based IDS. In particular, a system has been implemented that replaces the detection engine of Snort [14,16]. Experimental evaluation shows that the speed of the detection process has been significantly improved, even compared to Snort’s recently released, fully revised detection engine.

This work has been supported by the FWF (Fonds zur Förderung der wissenschaftlichen Forschung), under contract number P13731-MAT. The views expressed in this article are those of the authors and do not necessarily reflect the opinions or positions of the FWF.