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Function Embedding Generation Using Program Dependency Graph Based Neural Network


Analyzing software binaries can be helpful in tackling important problems such as plagiarism, malware or vulnerability detection. Detecting similarity between two binary functions coming from different sources can be done using binary code similarity detection. Existing approaches use Control-Flow graph information of binaries in some way or another i.e either graph matching or control-block embedding which is either slow or does not utilize all the information. In this work we propose novel way to use program dependency graph of functions to extract control and data dependency information and generate its embedding with help of Neural Network using this information. Measuring the distance between embedding of different binary functions can evaluate their similarity. Since this method does not rely on internal flow structure of the function it can be applied to more generally and is resilient to different compiler optimizations and heavy obfuscation techniques.

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