Real-Time Switched Capacitor Based Power Side-Channel Attack Detection
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
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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