Enhanced Multiclass Android Malware Detection Using a Modified Dwarf Mongoose Algorithm
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
The Android operating system has the most market share due to its easy handling and numerous advantages to Android users, which have attracted malicious actors. Android malware detection (AMD) systems based on machine learning (ML) are progressively being developed. However, these systems frequently struggle with high-dimensional datasets, increasing computation time, and lower accuracy. This study proposes a novel method for identifying malware in Android applications that employs a modified Dwarf Mongoose Optimization Algorithm (DMOA) for feature selection. The modified DMOA uses adaptive strategies, including crossover and mutation, to explore the search space more effectively, avoiding local optima and revealing higher-quality feature subsets that increase detection performance. The proposed modified DMOA model is trained and evaluated using the CICAndMal2017 dataset. The results show that it significantly outperforms existing techniques, achieving an accuracy of 100%.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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