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We present a new approach that bridges binary analysis techniques withmachine learning classification for the purpose of providing a static andgeneric evaluation technique for opaque predicates, regardless of theirconstructions. We use this technique as a static automated deobfuscation toolto remove the opaque predicates introduced by obfuscation mechanisms. Accordingto our experimental results, our models have up to 98% accuracy at detectingand deob-fuscating state-of-the-art opaque predicates patterns. By contrast,the leading edge deobfuscation methods based on symbolic execution show lessaccuracy mostly due to the SMT solvers constraints and the lack of scalabilityof dynamic symbolic analyses. Our approach underlines the efficiency of hybridsymbolic analysis and machine learning techniques for a static and genericdeobfuscation methodology.

Defeating Opaque Predicates Statically through Machine Learning and Binary Analysis