Extensible Android Malware Detection and Family Classification Using Network-Flows and API-Calls
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".