Misuse-based intrusion detection systems rely on models of attacks to identify the manifestation of intrusive behavior. Therefore, the ability of these systems to reliably detect attacks is strongly affected by the quality of their models, which are often called “signatures.” A perfect model would be able to detect all the instances of an attack without making mistakes, that is, it would produce a 100% detection rate with 0 false alarms. Unfortunately, writing good models (or good signatures) is hard. Attacks that exploit a specific vulnerability may do so in completely different ways, and writing models that take into account all possible variations is very difficult. For this reason, it would be beneficial to have testing tools that are able to evaluate the “goodness” of detection signatures.
This work describes a technique to test and evaluate misuse detection models in the case of network-based intrusion detection systems. The testing technique is based on a mechanism that generates a large number of variations of an exploit by applying mutant operators to an exploit template. These mutant exploits are then run against a victim host protected by a network-based intrusion detection system. The results of the systems in detecting these variations provide a quantitative basis for the evaluation of the quality of the corresponding detection model.