Android Device Malware Classification Framework Using Multistep Image Feature Extraction and Multihead Deep Neural Ensemble
Bibliographic record
Abstract
The incidence of malicious threats to computer systems has increased with the increasing use of Android devices and high-speed Internet. Malware visualization mechanism can analyze a computer whenever a software or system crash occurs because of malicious activity. This paper presents a new malware classification approach to recognize such Android device malware families by capturing suspicious processes in the form of different size color images. Important local and global characteristics of color images are extracted through a combined local and global feature descriptor (structure based local and statistical based global combined texture analysis) to reduce the training complexity of neural networks. A multihead ensemble of neural networks is proposed to increase network classification performance by merging prediction results from weak learners (convolutional neural network + gated recurrent unit) and using them as learning input to a multi-layer perceptron meta learner. Two public datasets of Android device malware are used to evaluate the classification and detection performance of the proposed approach. A baseline is established to compare the classification performance of the proposed approach with those of state-of-the-art and previous malware detection approaches. The proposed multihead ensemble improved the malware classification performance, with up to 97.8%, accuracy with the R2-D2 dataset and 94.1% accuracy with the MalNet dataset. The overall results show that a multihead ensemble with multi-step feature extraction is a practical approach to classify and detect Android malware.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".