Harnessing Broadcast Receivers for Classification of Android Malware Threats
Bibliographic record
Abstract
With the increasing number of malicious attacks, the way how to detect and classify malicious apps has drawn attention in mobile technology market. In this paper, we proposed a classification model to seek and track malware Apps broadcast receivers in such devices. To identify the family of apps, static features of each app was extracted and a novel deterministic classifier is employed to categorize malware apps. With such, we can act against malware of known family, since we understand its functions, and prevent it from spreading out in larger scale, affecting extensively our society. Detailed description of the classification model is provided, as well the core technologies of this novel malicious android applications’ model are presented. From experiments performed on a set of Android-based malware apps, we observe that the proposed classification model achieves highest accuracy, true-positive rate, false-positive rate, precision, recall, f-measure in comparison to other methods implemented in published experiments. The proposed classification model is promising since the average accuracy reaches an average of 97.31% and can effectively be applied to Android malware categorization, providing early detection of the capabilities of malware and the prospect of warning users of threatens ahead.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".