MalDuoNet: A DualNet Framework to Detect Android Malware
Bibliographic record
Abstract
Today mobile phones provide a wide range of applications that make our daily life easy. With popularity, smartphones have become a target for cybercrime where malicious apps are developed to acquire sensitive information or corrupt data. To mitigate this issue and to improve the security in mobile devices, different techniques have been used. These techniques can be broadly classified as static, dynamic and hybrid approaches. In this paper, a static-based model MalDuoNet is proposed to detect Android malwares, which uses a DualNet framework to analyze the features from the API calls. In the MalDuoNet model, one sub-network is focused to learn the features relevant to malicious behavior and the other sub-network is focused to learn the features in general. Thus it enables the model to learn complementary features which in turn helps get richer features for analysis. Then the features from the two sub-networks are combined in the final fused classifier for the final classification. In addition, each of the feature extractors has a separate classifier so that each sub-network can optimize its performance separately. The experimental results demonstrate that the MalDuoNet model outperforms the two baseline models with single network.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".