In plain words: By turning compiled programs back into readable code and reading habits in the machine instructions, a tool can match a binary to its writer. It picked the correct writer 96% of the time among 100 candidates, and kept working after obfuscation and compiler cleanup.
Abstract · 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. While recent work found that source code can be attributed to authors with high accuracy, attribution of executable binaries appears to be much more difficult. Many 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 programmer de-anonymization 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 adapt a powerful set of techniques from the domain of source code authorship attribution along with stylistic representations embedded in assembly, resulting in successful de-anonymization of a large set of programmers. We evaluate our approach on data from the Google Code Jam, obtaining attribution accuracy of up to 96% with 100 and 83% with 600 candidate programmers. We present an executable binary authorship attribution approach, for the first time, that is robust to basic obfuscations, a range of compiler optimization settings, and binaries that have been stripped of their symbol tables. We perform programmer de-anonymization using both obfuscated binaries, and real-world code found "in the wild" in single-author GitHub repositories and the recently leaked Nulled.IO hacker forum. We show that programmers who would like to remain anonymous need to take extreme countermeasures to protect their privacy.
Aylin Caliskan, Fabian Yamaguchi, Edwin Dauber, Richard Harang, Konrad Rieck, Rachel Greenstadt, Arvind Narayanan
arXiv:1512.08546 · cs.CR · submitted Dec 28, 2015 · updated Dec 18, 2017
abstract · pdf · html · 15 pages
De-anonymizing programmers from executable binaries - https://news.ycombinator.com/item?id=16598962 - March 2018 (39 comments)
When coding style survives compilation: De-anonymizing programmers from binaries - https://news.ycombinator.com/item?id=10806956 - Dec 2015 (67 comments)