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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".