MaGnn: Binary-Source Code Matching by Modality-Sharing Graph Convolution for Binary Provenance Analysis
Bibliographic record
Abstract
The number and variety of binaries running on electrical devices, public clouds, and on-premise infrastructure have been increasing rapidly. Recent successful supply chain attacks indicate that even for binaries known to be developed by trustful developers, they can still contain malicious functionalities and copy-and-pasted vulnerabilities that pose security risks to operational systems and end users. By analyzing the origin of a target code, code provenance analysis helps to relieve such problem by revealing information about the origin of a binary sample such as the author or the included software bill-of-materials. Since in most cases source symbol information is removed during the compilation process, given a binary code sample, matching it to its corresponding source code could improve the accuracy and efficiency of the provenance analysis. Existing binary-source code matching methods focus on comparing manually selected code literals (e.g. the number of if/else statements). However, these methods suffer from the issue of generalizability and require significant manual efforts.Different from the previous methods, we propose a machine learning-based binary-source code matching system, MaGnn, which measures the consistency of an input binary-source code pair by automatically extracting high-dimensional feature representations of the input and calculating the functionality similarity. With the Siamese architecture that shares a unified encoder across two modalities, McGnn is able to calculate the similarity of the input binary-source code pair with the automatically-extracted functionality representations. With the graph convolution neural network as the representation encoder, MaGnn is able to learn and encode the functionality information of the input pairs from their graph features into high-dimensional representation vectors. We benchmark MaGnn with a state-of-the-art binary-source code matching method and two machine-learning models on six out-of-sample datasets collected from five real-world libraries. Our experiment results show that MaGnn outperforms the baselines on most out-of-sample datasets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".