Decoupling coding habits from functionality for effective binary authorship attribution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Binary authorship attribution refers to the process of identifying the author of a given anonymous binary file based on stylistic characteristics. It aims to automate the laborious and error-prone reverse engineering task of discovering information related to the author(s) of a binary code. Existing works typically employ machine learning methods to extract features that are unique for each author and subsequently match them against a given binary to identify the author. However, most existing works share a common critical limitation, i.e., they cannot distinguish between features representing program functionality and those representing authorship (e.g., authors’ coding habits). Such distinction is crucial for effective authorship attribution because what is unique in a particular binary may be attributed to either author, compiler, or function. In this study, we present BinAuthor a system capable of decoupling program functionality from authors’ coding habits in binary code. To capture coding habits, BinAuthor leverages a set of features that are based on collections of functionality-independent choices made by authors during coding. Our evaluation demonstrates that BinAuthor outperforms existing methods in several aspects. First, it successfully attributes a larger number of authors with a significantly higher accuracy (around [Formula: see text]) based on the large datasets extracted from selected open-source C[Formula: see text] projects in GitHub, Google Code Jam events, Planet Source Code contests, and several programming projects. Second, BinAuthor is more robust than previous methods; there is no significant drop in accuracy when the code is subjected to refactoring techniques, simple obfuscation, and processed with different compilers. Finally, decoupling authorship from functionality allows us to apply BinAuthor to real malware binaries (Citadel, Zeus, Stuxnet, Flame, Bunny, and Babar) to automatically generate evidence on similar coding habits.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it