When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries

The ability to identify authors of computer programs based on their coding style is a direct threat to the privacy and anonymity of programmers. Previous work has examined attribution of authors from both source code and compiled binaries, and found that while source code can be attributed with very high accuracy, the attribution of executable binary appears to be much more difficult. Many potentially distinguishing features present in source code, e.g. variable names, are removed in the compilation process, and compiler optimization may alter the structure of a program, further obscuring features that are known to be useful in determining authorship. We examine executable binary authorship attribution from the standpoint of machine learning, using a novel set of features that include ones obtained by decompiling the executable binary to source code. We show that many syntactical features present in source code do in fact survive compilation and can be recovered from decompiled executable binary. This allows us to add a powerful set of techniques from the domain of source code authorship attribution to the existing ones used for binaries, resulting in significant improvements to accuracy and scalability. We demonstrate this improvement on data from the Google Code Jam, obtaining attribution accuracy of up to 92% with 100 candidate programmers. We also demonstrate that our approach is robust to basic obfuscations, a range of compiler optimization settings, and binaries that have been stripped of their symbol tables. Finally, for the first time we are aware of, we demonstrate that authorship attribution can be performed on both obfuscated binaries, and real world code found “in the wild” by performing attribution on single-author GitHub repos-