Proactive TSCA Detection via Real-Time Thermal Signature Modeling on Low-Power IoT Devices
Bibliographic record
Abstract
In this paper, we introduce a real-time thermal signature modeling approach for embedded IoT systems as a proactive countermeasure against Thermal Side-Channel Attacks (TSCA). Rather than designing new sensing hardware, we exploit the ESP32-S3’s native on-die temperature sensor an 8 bit sigmadelta ADC with factory trimmed DAC that delivers $\simeq \mathbf{0. 2}{ }^{\circ} \mathrm{C} / \mathrm{LSB}$ resolution and $\pm 5{ }^{\circ} \mathrm{C}$ absolute accuracy across its $-40{ }^{\circ} \mathrm{C}$ to $+125{ }^{\circ} \mathrm{C}$ range to capture core junction temperature directly. Using a three stepper motor control case study, we record the chip’s thermal evolution under four operational loads (idle, one, two, and three motor scenarios) and log the data in structured CSV files. These profiles form a comprehensive dataset of normal operation signatures, laying the groundwork for lightweight, ondevice anomaly detection and future deployment of machine learning-based digital twin models. Our results demonstrate how existing, low cost thermal sensors can be harnessed for thermal profiling, integrating core level signature awareness into resource constrained IoT devices without external components or high overhead methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".