Differential Power Analysis of CAST-128
Bibliographic record
Abstract
Power analysis is used to reveal the secret key of security devices by monitoring the power consumption of certain cryptographic algorithm operations through a statistical analysis approach known as Differential Power Analysis (DPA). Whilst this has been applied extensively to attacks on FPGA devices, there has been little research into attacks on ASIC devices. Although standard DPAs are essentially independent of the block cipher that they target, some are less susceptible than others due to algorithm's structure, and therefore more difficult to attack such as the CAST-128. In this paper, we outline the first reported power analysis attack of CAST-128 as it falls into the category just outlined and it is the only algorithm that has not been practically broken either on FPGA or ASIC, it is also a common block cipher used in Canada. The paper outlines an approach that reveals all 128 bits of the secret key within 300,500 power traces, highlighting insights on attacking the registers rather than the Sbox. Finally, the effect of applying the Hamming weight power model on different widths of the target register under attack in ASIC device is evaluated.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".