Android authorship attribution through string analysis
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
With the rising popularity of Android mobile devices, the amount of malicious applications targeting the Android platform has been increasing tremendously. To mitigate the risk of malicious apps, there is a need for an automated system to detect these applications. Current detection techniques rely on the signatures of well-documented malware, and hence may not be able to detect new malware samples. Instead of generating signatures for malware samples themselves, in this work, we propose to develop a lightweight system that can generate signatures of malware writers by leveraging the string components present in their Android binaries. Using these author signatures, we can effectively detect a wide range of existing, as well as any new, malware samples generated by particular authors. The proposed system achieved 98%, 96%, and 71% accuracy over datasets of 1559 benign, 262 malicious, and 96 obfuscated Android applications, respectively. The string-based approach achieved 71% of accuracy compared to only 50% obtained with the existing Ding and Samadzadeh's system.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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