Obfuscation Resilient Search through Executable Classification
Fang-Hsiang Su, Jonathan Bell, Gail Kaiser, and Baishakhi Ray

TL;DR
This paper introduces Macneto, a deep learning-based method for searching obfuscated executables that remains effective despite structural modifications, aiding malware detection and intellectual property protection.
Contribution
Macneto is a novel approach that leverages deep learning and instruction analysis to perform obfuscation-resilient executable search without relying on structural landmarks.
Findings
High search precision against state-of-the-art obfuscators
Effective in identifying relevant executables despite control flow modifications
Assists developers in understanding obfuscated code by inferring keywords
Abstract
Android applications are usually obfuscated before release, making it difficult to analyze them for malware presence or intellectual property violations. Obfuscators might hide the true intent of code by renaming variables and/or modifying program structures. It is challenging to search for executables relevant to an obfuscated application for developers to analyze efficiently. Prior approaches toward obfuscation resilient search have relied on certain structural parts of apps remaining as landmarks, un-touched by obfuscation. For instance, some prior approaches have assumed that the structural relationships between identifiers are not broken by obfuscators; others have assumed that control flow graphs maintain their structures. Both approaches can be easily defeated by a motivated obfuscator. We present a new approach,Macneto, to search for programs relevant to obfuscated executables…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Internet Traffic Analysis and Secure E-voting · Software Testing and Debugging Techniques
