ProvSpider: A Robust and Universal Toolkit for Binary Provenance Analysis Using Deep Learning
Bibliographic record
Abstract
Binary provenance analysis recovers essential information, such as architecture, structure, and toolchain, from executables lacking reliable metadata. This is crucial for reverse engineering. However, provenance recovery from binaries is highly challenging, due to three key factors: (1) binaries span diverse CPU architectures; (2) Raw byte sequences are often extremely long without clear boundaries; and (3) Compilation alters control flow, register usage, and memory layout, obscuring the original code structure and complicating analysis. To address these challenges, we propose a novel and robust analysis toolset, namely ProvSpider, to identify segment boundaries, types of segments as well as target CPU architectures, bitness, and endianness based on code-only sections. ProvSpider is built based on a convolutional neural network (CNN) to learn local execution patterns. We embed byte sequences into eight-dimensional vectors to capture bytes’ global dependencies. The gating mechanism after convolutional layers filters out noise and keeps most representative features. At last, the sliding window divides lengthy byte sequences into fixedlength processable chunks. Our model achieves high accuracy in all five analysis tasks, significantly outperforming the state-of-the-art models. By providing a universal and robust approach, ProvSpider lays the foundation for advancing binary provenance analysis, facilitating future improvements in binary analysis and reverse engineering.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".