dAEMD: Deep Autoencoder based Malware Detection from Android Network Flows
Bibliographic record
Abstract
Android OS is an enticing target for attacks due to its popularity. Malware attacks are prevalent and growing. Further, the attack pattern is changing rapidly to avoid intrusion detection. Thus, effective malware detection that can adapt to rapid structure and behaviour changes is in demand. We provide a two-layer mobile malware detection method in this research. Deep learning represents the feature set into a latent feature space in the first layer, while the second layer is a straightforward multi-layer perceptron classifier. Elastic Weight Consolidation was added to the neural network classifier to enable continuous malware learning. We ran trials to evaluate performance. We compared our model’s accuracy to other machine learning models. We used cutting-edge methods to build our framework. Performance comparison with the state-of-the-art approaches shows the efficacy of the proposed framework. It can also learn new threats while maintaining detection performance. The testing findings show that our framework can detect intrusion with 98.8% Precision and 98.7% Recall. Additionally, the continuous learning system can accurately learn malware.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".