2013 Symposium Posters

Project Members
Gaspar Modelo-Howard, Fahad Arshad, Chris Gutierrez, Saurabh Bagchi, Yuan Qi

Intrusion detection systems (IDS) are an important component to effectively protect computer systems. Misuse detection is the most popular approach to detect intrusions, using a library of signatures to find attacks. The accuracy of the signatures is paramount for an effective IDS, still today’s practitioners rely on manual techniques to improve and update those signatures. We present a system, called pSigene, for the automatic generation of intrusion signatures by mining the vast amount of public data available on attacks. It follows a four-step process to generate the signatures, by first crawling attack samples from multiple public cybersecurity web portals. Then, a feature set is created from existing detection signatures to model the samples, which are then grouped using a biclustering algorithm which also gives the distinctive features of each cluster. Finally the system automatically creates a set of signatures using regular expressions, one for each cluster. We tested our architecture for the prevalent class of SQL injection attacks and found our signatures to have a True and False Positive Rates of over 86% and 0.03%, respectively and compared our findings to other SQL injection signature sets from popular IDS and web application firewalls. Results show our system to be very competitive to existing signature sets.