Obfuscated Clone Search in JavaScript based on Reinforcement Subsequence Learning
Bibliographic record
Abstract
Finding similar code is important for software engineering, defense of intellectual property, and security, and one of the increasingly common ways adversaries use to defeat the detection of similar code is through obfuscations such as code transformation and scattering the code they wish to hide among long sequences. Moving code far enough apart poses a specific challenge for solutions with localized features (e.g., n-grams), or attention mechanisms as the code parts are distributed beyond the local context window. We introduce a neural network solution pattern called “Cybertron” that addresses this problem by utilizing reinforcement learning to train a code abstraction and summarization function; this converts arbitrarily long code into fixed-length real vectors in a way that is optimized for similarity search. The key to the design is the smart selection of important elements of the code and abstraction to preserve semantic function while minimizing syntactic feature information. We evaluated the approach on a three-challenge benchmark of obfuscated JavaScript, a scripting language that is commonly obfuscated and for which code-mixing is a rising challenge. The evaluation shows our approach identifies obfuscated code within even large scripts with an AUC of 78%, which outperforms current state-of-the-art sequence models by 7–35%.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".