On the Use of Mobile GPU for Accelerating Malware Detection Using Trace Analysis
Bibliographic record
Abstract
Malware detection on mobile phones involves analysing and matching large amount of data streams against a set of known malware signatures. Unfortunately, as the number of threats grows continuously, the number of malware signatures grows proportionally. This is time consuming and leads to expensive computation costs, especially for mobile devices where memory, power and computation capabilities are limited. As the security threat level is getting worse, parallel computation capabilities for mobile phones is getting better with the evolution of mobile graphical processing units (GPUs). In this paper, we discuss how we can benefit from the evolving parallel processing capabilities of mobile devices in order to accelerate malware detection on Android mobile phones. We have designed and implemented a parallel host-based anti-malware for mobile devices that exploits the computation capabilities of mobile GPUs. A series of computation and memory optimization techniques are proposed to increase the detection throughput. The results suggest that mobile graphic cards can be used effectively to accelerate malware detection for mobile phones.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".