Deep Learning Based Approach for Classifying Power Signals and Detecting Anomalous Behavior of Wireless Devices
Bibliographic record
Abstract
The problem of extracting insights from signals is a very interesting and challenging task. This problem finds its way into the task of detecting malware in wireless devices by considering their power consumption signals. Relying on the fact that every single action on-board (whether hardware or software driven actions) will be reflected as a change in the device's power consumption; consequently, leaving a trace (by malware) in the power consumed by the device is something inevitable. Motivated by the powerful capabilities of deep learning in extracting features unsupervisedly, this paper proposes deep learning based detection methodology. The methodology makes use of time-frequency representation (TFR) of signals to resemble informative visual textures. The assumption is that TFRs (2-D images) construct textures that capture valuable information out of 1-D signals. Following that, Histograms of Oriented Gradients (HOG) of TFR images are computed. The HOG information is treated as images that contain better discriminative features. Finally, a convolutional neural network (CNN) model is trained to accurately classify these signals and detect the anomalous behavior. We have validated the effectiveness of the proposed methodology on a cybersecurity application in the domain of wireless devices. The experimental results confirm that proposed methods can be used to detect the presence of malwares in smartphones with high accuracy, and can also outperform previously reported methods with ~9% to 17% detection performance gain.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".