A Power Signal Based Dynamic Approach to Detecting Anomalous Behavior in Wireless Devices
Bibliographic record
Abstract
The health and security of wireless devices are fast gaining importance, and these are vital for effective implementation of sensor networks and Internet of Things (IoT). Any device, wired or wireless, needs a power source, and the power consumed is a consequence of its usage and functionality. In this context, this paper proposes a methodology to detect anomalous behavior of wireless devices by monitoring their power consumption patterns. The proposed methodology utilizes Independent Component Analysis (ICA) to extract information from the current power consumption of the device and generates features of the state of the device by calculating the degree of similarity of the extracted information with the known normal behavior of the device. Then, Recursive Feature Elimination (RFE) is used to select features from the generated feature vector. Finally, Classification algorithms are used to classify and detect the anomalous behavior. We have validated the methodology by emulating anomalous behavior on smartphones through a custom designed app that runs in the background while the main app is being used. Validation results indicate that the proposed methodology can be used to identify even a sparsely active malware existence with very high accuracy. The proposed model has an accuracy of 88% for a malware active for 1% of the total time and accuracy of almost 100% for malware active for 12% of the time.
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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 | 0.001 |
| 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 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".