A Hardware Trojan Detection Method for IoT Sensors Using Side-Channel Activity Magnifier
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
As the Internet of Things (IoT) technology matures and becomes widely adopted, security for IoT networks remains the main concern for users and organizations across the globe. The full potential of IoT technology will not be realized without a robust security solution. IoT sensors are considered weak links in IoT networks that can be utilized to access key information or to attack IoT networks. In this work, a new technique is presented to detect hardware Trojans. It is shown how to amplify the side-channel leakage of a Trojan to increase its observability and the detection rate. In the proposed solution, the effect of adding a Trojan on the supply path is observed to detect the Trojan. Experimental measurement results on a prototype demonstrate that the proposed method can increase the observability for a Trojan by more than tenfold without increasing the signal-to-noise ratio. Small size Trojans, which remain undetected using conventional methods, can be identified with the proposed solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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