The broader impact/commercial potential of this I-Corps project is to defeat cyber-adversaries. The proposed technology will use machine learning techniques to automate and discover current unknown malware to provide reliable pre- and post-breach intelligence. The goal for these products is to capable of integration into any existing cybersecurity infrastructure. This technology may be used to gather data from other products to make predictions about the threat landscape. This project will explore the information needs regarding the specific features embedded in the malware code.
This I-Corps project is based on the development of a large malware analysis platform that performs static analysis of files to produce state-of-the-art malware characterizations, including representing malware as graphs. These malware detection and classification models have become extremely accurate, thanks to vast amounts of data generated from malware datasets used in the development. After analyzing thousands of different malware families in many ways, these deep learning models have been trained to produce accurate and generalizable models able to detect incoming threats in large organizations and reduce false positives to help combat alert fatigue. This holistic view allows the user to catch novel and heavily obfuscated threats that evade models trained on traditional indicators.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
