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Binary similarity analysis determines if two binary executables are from thesame source program. Existing techniques leverage static and dynamic programfeatures and may utilize advanced Deep Learning techniques. Although they havedemonstrated great potential, the community believes that a more effectiverepresentation of program semantics can further improve similarity analysis. Inthis paper, we propose a new method to represent binary program semantics. Itis based on a novel probabilistic execution engine that can effectively samplethe input space and the program path space of subject binaries. Moreimportantly, it ensures that the collected samples are comparable acrossbinaries, addressing the substantial variations of input specifications. Ourevaluation on 9 real-world projects with 35k functions, and comparison with 6state-of-the-art techniques show that PEM can achieve a precision of 96% withcommon settings, outperforming the baselines by 10-20%.

PEM: Representing Binary Program Semantics for Similarity Analysis via a Probabilistic Execution Model