How Effective are pretrained Programming Language-based Language Models (PLLMs) in the Detection of Android Vulnerabilities?
Bibliographic record
Abstract
The pervasiveness of Android-based applications has led to the increase in the rate and complexity of cyber-attacks on end users in recent years. To mitigate this, various machine learning (ML)/deep learning (DL) techniques have been explored in various studies to detect vulnerabilities in this popular mobile operating system with varying results. In this work, we experimented with the use of pretrained Programming Language-based Language Models (PLLMs) in the detection of vulnerabilities in real life Android application package and source files. The recently released LVDAndro dataset was selected for this world because it is one of the most realistic Android-based datasets. Using this dataset, two selected PLLMs - CodeBERT and GraphCodeBERT-were trained in the downstream task of vulnerability detection. Overall, these transformer-based models achieved better performance (97% F1, 97% accuracy) in Android vulnerability detection compared to the AutoML (94% F1, 94% accuracy) model used in the previous study.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".