Automatic Generation and Analysis of NIDS Attacks
Shai Rubin, Somesh Jha, and Barton P. Miller.
In 20th Annual Computer Security Applications Conference (ACSAC).
Tucson, Arizona, December 2004.
Received best paper and best student paper awards.
A common way to elude a signature-based NIDS is to transform an attack instance that the NIDS recognizes into another instance that it misses. For example, to avoid matching the attack payload to a NIDS signature, attackers split the payload into several TCP packets or hide it between benign messages. We observe that different attack instances can be derived from each other using simple transformations. We model these transformations as inference rules in a natural-deduction system. Starting from an exemplary attack instance, we use an inference engine to automatically generate all possible instances derived by a set of rules. The result is a simple yet powerful tool capable of both generating attack instances for NIDS testing and determining whether a given sequence of packets is an attack.
In several testing phases using different sets of rules, our tool exposed serious vulnerabilities in Snort—a widely deployed NIDS. Attackers acquainted with these vulnerabilities would have been able to construct instances that elude Snort for any TCP-based attack, any Web-CGI attack, and any attack whose signature is a certain type of regular expression.
