A Strongly Non-Intrusive Methodology to Monitor and Detect Anomalous Behaviour of Wireless Devices
Bibliographic record
Abstract
With the growing popularity and usage of smartphone devices, safeguarding it against malware becomes increasingly essential. In this paper, we define and present a strongly non-intrusive observation method that monitors network traffic data of the device to detect the presence of malware. The proposed method is advantageous as it neither requires any modification to the device, nor it needs any explicit connection between the device and the observing tool. We have evaluated the performance of two anomaly detection techniques, namely, changepoint detection and HOG+CNN, on the observed data. We compared the performance of the two detection techniques using both ordinary non-intrusive power signal data and strongly nonintrusive network traffic data. We also ran experiments to detect once-activated simulated malware and real malware. Validation tests confirm the effectiveness of the methodology in detecting the presence of malware.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".