An effective behavior‐based Android malware detection system
Why this work is in the frame
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Bibliographic record
Abstract
Abstract With the rapid growth of Android applications and malware, it has become a challenge to distinguish malware from a huge number of applications. The use of behavioral analytics is one of the most promising approaches because of its accuracy and resilience to malware variants. In this paper, we propose a behavior‐based malware detection system. Firstly, it uses Android APIs and libc (Bionic libc) function calls along with their arguments to describe sensitive application behaviors. Secondly, it conducts behavior analysis and malware detection using machine learning techniques, including Support Vector Machine, Naïve Bayes, and Decision Tree. The experiments are conducted with 1136 real‐world samples that are composed of various types of malware and benign applications. The evaluation results show that our system can effectively detect Android malware. In addition, we compare our system with the other behavior‐based malware detection system, and the comparison results show the advantage of our system on malware detection. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.001 |
| 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 it