SpyDroid: A Framework for Employing Multiple Real-Time Malware Detectors on Android
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
Android has become the leading operating system for next-generation smart devices. Consequently, the number of Android malware has also skyrocketed. Many dynamic analysis techniques have been proposed to detect Android malware. However, very few of these techniques use real-time monitoring on user devices as Android does not provide low-level information to third-party apps. Moreover, some techniques detect a specific malware class more effectively than others. Therefore, end users can be benefited by installing multiple malware detection techniques. In this paper, we propose SpyDroid, a real-time malware detection framework that can accommodate multiple detectors from third-parties (e.g., researchers and antivirus vendors) and allows efficient and controlled real-time monitoring. SpyDroid consists of two operating system modules (monitoring and detection) and supports application layer sub-detectors. Sub-detectors are regular Android applications that monitor and analyze different runtime information using the monitoring module and they report the detection module about their findings. The detection module decides when to mark an app as malware. Researchers and antivirus vendors can now publish their techniques via app markets and end users can install any number of sub-detectors as they require. We have implemented SpyDroid using the Android Open Source Project (AOSP) and our experiments with a dataset containing 4,965 apps show that decisions from multiple sub-detectors can increase the malware detection rate significantly on a real device.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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