Understanding the Service Life Cycle of Android Apps
Bibliographic record
Abstract
The fast growing use of the Android platform has been accompanied with an increase of malwares in Android applications. A popular way in distributing malwares in the mobile world is through repackaging legitimate apps, embedding malicious code in them, and publishing them in app stores. Therefore, examining the similarity between the behavior of malicious and normal apps can help detect malwares due to repacking. Malicious apps operate by keeping their operations invisible to the user. They also run long enough to perform their malicious tasks. One way to detect malicious apps is to examine their service life cycle. In this paper, we examine the service life cycle of apps. We extract various features of app services. We use these features to classify over 250 normal and malicious apps. Our findings show that malicious apps tend to use services to do their malicious operation and have no communication with the other components of the app, whereas the services in normal apps are usually bound to other components and send messages to notify users about the operations they perform. The results of this exploratory study can be used in the future to design techniques for detecting malicious apps using the classification of their service features.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".