Scalable Binary Analysis for Security feat. Suman Jana | Stanford MLSys Seminar Episode 36

Episode 36 of the Stanford MLSys Seminar Series! Scalable, Accurate, Robust Binary Analysis with Transfer Learning Speaker: Suman Jana Abstract: Binary program analysis is a fundamental building block for a broad spectrum of security tasks. Essentially, binary analysis encapsulates a diverse set of tasks that aim to understand and analyze behaviors/semantics of binary programs. Existing approaches often tackle each analysis task independently and heavily employ ad-hoc task-specific brittle heuristics. While recent ML-based approaches have shown some early promise, they too tend to learn spurious features and overfit to specific tasks without understanding the underlying program semantics. In this talk, I will describe two of our recent projects that use transfer learning to learn binary program semantics and transfer the learned knowledge for different binary analysis tasks. Our key observation is that by designing a pretraining task that can learn binary code semantics, we can drastically boost the performance of binary analysis tasks. Our pretraining task is fully self-supervised -- it does not need expensive labeling effort and therefore can easily generalize across different architectures, operating systems, compilers, optimizations, and obfuscations. Extensive experiments show that our approach drastically improves the performance of popular tasks like binary disassembly and matching semantically similar binary functions. Bio: Suman Jana is an associate professor in the department of computer science and the data science institute at Columbia University. His primary research interest is at the intersections of computer security and machine learning. His research has received six best paper awards, a CACM research highlight, a Google faculty fellowship, a JPMorgan Chase Faculty Research Award, an NSF CAREER award, and an ARO young investigator award. -- 0:00 Starting soon 3:03 Presentation 39:18 Discussion The Stanford MLSys Seminar is hosted by Dan Fu, Karan Goel, Fiodar Kazhamiaka, and Piero Molino, Chris Ré, and Matei Zaharia. Twitter:   / realdanfu​     / krandiash​     / w4nderlus7   -- Check out our website for the schedule: http://mlsys.stanford.edu Join our mailing list to get weekly updates: https://groups.google.com/forum/#!for... #machinelearning #ai #artificialintelligence #systems #mlsys #computerscience #stanford #columbia