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Android applications are usually obfuscated before release, making itdifficult to analyze them for malware presence or intellectual propertyviolations. Obfuscators might hide the true intent of code by renamingvariables and/or modifying program structures. It is challenging to search forexecutables relevant to an obfuscated application for developers to analyzeefficiently. Prior approaches toward obfuscation resilient search have reliedon certain structural parts of apps remaining as landmarks, un-touched byobfuscation. For instance, some prior approaches have assumed that thestructural relationships between identifiers are not broken by obfuscators;others have assumed that control flow graphs maintain their structures. Bothapproaches can be easily defeated by a motivated obfuscator. We present a newapproach,Macneto, to search for programs relevant to obfuscated executablesleveraging deep learning and principal components on instructions. Macnetomakes few assumptions about the kinds of modifications that an obfuscator mightperform. We show that it has high search precision for executables obfuscatedby a state-of-the-art obfuscator that changes control flow. Further, we alsodemonstrate the potential of Macneto to help developers understand executables,where Macneto infers keywords (which are from the relevant unobfuscatedprogram) for obfuscated executables.

Obfuscation Resilient Search through Executable Classification