Measuring code reuse in Android apps
Bibliographic record
Abstract
The appearance of the Android platform and its popularity has resulted in a sharp rise in the number of reported vulnerabilities and consequently in the number of mobile threats. Leveraging openness of Android app markets and the lack of security testing, malware authors commonly plagiarize Android applications through code reuse, boosting the amount of malware on the markets and consequently the infection rate. In the last few years the number of studies focused on detection of mobile app code reuse has drastically increased. Ranging from lightweight detection of suspicious signs to more sophisticated and computationally expensive methods assessing apps' similarity, the studies treated the presence of code reuse as a sign of plagiarized apps and maliciousness. In this work, we revisit this assumption and investigate code reuse in legitimate and malicious mobile apps. The main questions that this study aims to answer are what it is that is being reused, what we can learn from this reuse and consequently how we can use this knowledge. To answer these questions we measure code uniqueness and identify common components originating from third-party sources. We further analyze and correlate reused code extracted from over 60,000 apps from ten markets around the world and commonly used app repositories. As our analysis shows, understanding code reuse can shed some light on app origin and evolution.
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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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".