Identifying Modeling Errors in Signatures by Model Checking

Most intrusion detection systems deployed today apply misuse detection as analysis method. Misuse detection searches for attack traces in the recorded audit data using predefined patterns. The matching rules are called signatures. The definition of signatures is up to now an empirical process based on expert knowledge and experience. The analysis success and accordingly the acceptance of intrusion detection systems in general depend essentially on the topicality of the deployed signatures. Methods for a systematic development of signatures have scarcely been reported yet, so the modeling of a new signature is a time-consuming, cumbersome, and errorprone process. The modeled signatures have to be validated and corrected to improve their quality. So far only signature testing is applied for this. Signature testing is still a rather empirical and time-consuming process to detect modeling errors. In this paper we present the first approach for verifying signature specifications using the SPIN model checker. The signatures are modeled in the specification language EDL which leans on colored Petri nets. We show how the signature specification is transformed into a PROMELA model and how characteristic specification errors can be found by SPIN.