A Pre-Activation, Golden IC Free, Hardware Trojan Detection Approach
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 increasing concern about the security and reliability of abroad manufactured integrated circuits (ICs) has attracted academia and industries to develop hardware Trojan (HT) detection approaches. This article presents an efficient integrated HT detection technique based on evaluating changes in the integrated parasitic capacitors. The HT detection circuit consists of a capacitively coupled, low-power, low-noise, operational transconductance amplifier (OTA), which can detect capacitance fluctuations in the range of 10 aF. The HT detection circuit consumes <inline-formula> <tex-math notation="LaTeX">$5.88~\mu \text {W}$ </tex-math></inline-formula> from 1.8-V power supply in 180-nm CMOS technology. The detection method is based on clustering the IC and monitoring each cluster’s flag. The flag set circuit is designed to sense parasitic capacitance and change its status based on it. The proposed technique can detect the HT circuit before the activation of the IC. Moreover, this technique shows very promising results in detecting HTs with zero-delay effect, which is a challenging issue in the conventional delay-based side-channel signal analysis method. More significantly, the proposed method does not require a golden IC for HT detection and can detect the HT using simulation-based data. The proposed method creates a recognizable difference detection signal between the capacitive behavior of an infected and a pure IC. This results in a high confidence level in the proposed detection method. The proposed idea is implemented on ISCAS’85 benchmark circuits, and the detection outcomes and the statistical simulations are presented.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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