Real-Time Switched Capacitor Based Power Side-Channel Attack Detection
Bibliographic record
Abstract
Side-channel attacks (SCAs) are regarded as significant risks to the hardware implementation of cryptographic systems. Side-channel information, such as timing, power, and electromagnetic radiation, is leaked through the system and can be exploited for secret key extraction. This work proposes a real-time and compatible detection method for power SCAs. The technique utilizes a switched capacitor DC-DC (SC-DCDC) converter in conjunction with a lightweight artificial intelligence engine for power SCA detection. The proposed system, referred to as EoH, possesses the capability to perform dynamic voltage scaling and learn the behaviors of the cryptographic system to identify potential attacks. The switching activities of the SC-DCDC converter can be viewed as measurements of the cryptographic function. Therefore, a recurrent neural network was chosen as it processes time-series data most effectively. The technique is system-specific, meaning that during the enrollment phase, the normal operation of the system is learned. Furthermore, the technique can be expanded to include other types of SCAs and is not limited to power.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".