OBA2: An Onion approach to Binary code 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
A critical aspect of malware forensics is authorship analysis. The successful outcome of \nsuch analysis is usually determined by the reverse engineer’s skills and by the volume and \ncomplexity of the code under analysis. To assist reverse engineers in such a tedious and \nerror-prone task, it is desirable to develop reliable and automated tools for supporting the \npractice of malware authorship attribution. In a recent work, machine learning was used to \nrank and select syntax-based features such as n-grams and flow graphs. The experimental \nresults showed that the top ranked features were unique for each author, which was \nregarded as an evidence that those features capture the author’s programming styles. In \nthis paper, however, we show that the uniqueness of features does not necessarily \ncorrespond to authorship. Specifically, our analysis demonstrates that many “unique” \nfeatures selected using this method are clearly unrelated to the authors’ programming \nstyles, for example, unique IDs or random but unique function names generated by the \ncompiler; furthermore, the overall accuracy is generally unsatisfactory. Motivated by this \ndiscovery, we propose a layered Onion Approach for Binary Authorship Attribution called \nOBA2. The novelty of our approach lies in the three complementary layers: preprocessing, \nsyntax-based attribution, and semantic-based attribution. Experiments show that our \nmethod produces results that not only are more accurate but have a meaningful connection \nto the authors’ styles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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