Evaluating Dynamic Analysis Features for Android Malware Categorization
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
Tremendous increase and sophistication in Android applications is making malware detection a challenging task. The use of obfuscation has complicated the task of malware detection, as static analysis can be deceived by different obfuscation schemes. Recently, studies have focused on dynamic analysis of applications, as it is more resilient against obfuscation techniques. CCCS-CIC-AndMal-2020; published by Canadian institute for cybersecurity is a recent data set of extracted features of malicious Android applications. The dynamic features in this data set belong to six categories: memory, network, battery, logs, process and APIs. Previous studies have focused on classification of Android malware using dynamic features. However, the impact of individual categories of dynamic features for malware categorization has not been analyzed in length. In this study, a comprehensive analysis on the impact of all dynamic analysis categories and features on Android malware detection is conducted using different filter and wrapper methods. The most significant categories of dynamic features are reported and important features in those categories are also listed.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| 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