Using Deep Learning to Classify Power Consumption Signals of Wireless Devices: An Application to Cybersecurity
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
The problem of detecting malware in mobile devices is becoming increasingly important. While most of the mobile devices run on very limited resources, having anti-viruses installed on-board is not very practical, especially in IoT devices. Even if such tools exist, malware could hide or manipulate their fingerprint, making them not easy to detect. Thus, having effective countermeasures for after malware intrusion is paramount. In this work, we utilize deep learning ability to learn multiple levels of representations from raw data to classify power consumption signals obtained from smartphones. The objective is to build a framework that can intelligently tell if the smartphone has a malware or not by only monitoring its power consumption. Validation tests confirm that the proposed framework show that information contained in the measured power consumption of smartphones can in principle be used to identify malware existence and further can tell how active malware is with very high accuracy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it