Extensible Android Malware Detection and Family Classification Using Network-Flows and API-Calls
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Android OS-based mobile devices have attracted numerous end-users since they are convenient to work with and offer a variety of features. As a result, Android has become one of the most important targets for attackers to launch their malicious intentions. Every year, researchers propose a novel Android malware analyzer framework to defend against real-world Android malware Apps. The researchers require an inclusive Android dataset to assess their Android analyzers. However, generating a comprehensive Android malware dataset is a challenging concept in malware scrutiny fields. In 2018, we made the first part of our Android malware dataset, CICAndMal2017 [16], publicly available while performing dynamic analyses on real smartphones. In this paper, we provide the second part of the CICAndMal2017 dataset [16] publicly available which includes permissions and intents as static features, and API calls as dynamic features. Besides, we examine these features with our two-layer Android malware analyzer. According to our analyses, we succeeded in achieving 95.3% precision in Static-Based Malware Binary Classification at the first layer, 83.3% precision in Dynamic-Based Malware Category Classification and 59.7% precision in Dynamic-Based Malware Family Classification at the second layer.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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