Toward Developing a Systematic Approach to Generate Benchmark Android Malware Datasets and Classification
Bibliographic record
Abstract
Malware detection is one of the most important factors in the security of smartphones. Academic researchers have extensively studied Android malware detection problems. Machine learning methods proposed in previous work typically reported high detection performance and fast prediction times on fixed and defective datasets. Therefore, based on these shortcomings most datasets are not suitable for real-world deployment. The main goal of this paper is to propose a systematic approach to generate Android malware datasets using real smartphones instead of emulators and develop a new dataset, namely CI-CAndMal2017, which covers all the shortcomings and limitations of previous datasets. Also, we offer 80 traffic features to select the best feature sets for detecting and classifying the malicious families just by traffic analysis. The proposed method showed an average precision of 85% and recall of 88% for three classifiers, namely Random Forest(RF), K-Nearest Neighbor (KNN), and Decision Tree (DT).
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".