lac-dcc/yali
A framework to analyze a space formed by the combination of program encodings, obfuscation passes and stochastic classification models.
This project helps software security researchers and reverse engineers evaluate how well deep learning models can classify programs that have been intentionally obfuscated. You provide program code in various representations and obfuscation levels, and it outputs an analysis of how different classification models perform, particularly how their accuracy is affected by obfuscation. It's designed for those who need to understand the resilience of program classification systems against common code protection techniques.
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Use this if you are a researcher or security professional investigating the effectiveness of deep learning models in classifying obfuscated source code and want to systematically test various program representations and obfuscation strategies.
Not ideal if you are looking for a tool to obfuscate code, detect malware in live systems, or analyze the performance of general software without a focus on deep learning model resilience to obfuscation.
Stars
36
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3
Language
LLVM
License
GPL-3.0
Category
Last pushed
Aug 01, 2023
Commits (30d)
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