Bibliographic record
Abstract
ABSTRACT \n \nAndroSAT: Security Analysis Tool for Android Applications \n \nWith about 1.5 million Android device activations per day and billions of applications installation from Google Play, Android is becoming one of the most widely used operating systems for smartphones and tablets. \n \nBesides typical personal usages, Android mobile devices are also being integrated into enterprises, government organizations, and military networks. Consequently, these devices hold valuable sensitive information which makes them face the same level of malicious attacks that have targeted the desktop environments over the past three decades. \n \nIn this thesis, we present AndroSAT, a Security Analysis Tool for Android applications. The developed framework allows us to efficiently experiment with different security aspects of Android apps through the integration of (i) a static analysis module that scans Android apps for malicious patterns. The static analysis process involves several steps such as n-gram analysis of dex files, de-compilation of the app, pattern search, and analysis of the AndroidManifest file; (ii) a dynamic analysis sandbox that executes Android apps in a controlled virtual environment which logs low-level interactions with the operating system. \nThe effectiveness of the developed framework is confirmed by testing it on popular apps collected from F-Droid, and malware samples obtained from a third party and the Android Malware Genome Project dataset. As a case study, we show how the analysis reports obtained from AndroSAT can be used for studying the frequency of use of different Android permissions and dynamic operations and detection of Android malware.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".