On MILP-Based Automatic Search for Differential Trails Through Modular Additions with Application to Bel-T.
Bibliographic record
Abstract
Using modular addition as a source of nonlinearity is frequently used in many symmetric-key structures such as ARX and Lai–Massey schemes. At FSE’16, Fu et al. proposed a Mixed Integer Linear Programming (MILP)-based method to handle the propagation of differential trails through modular additions assuming that the two inputs to the modular addition and the consecutive rounds are independent. However, this assumption does not necessarily hold. In this paper, we study the propagation of the XOR difference through the modular addition at the bit level and show the effect of the carry bit. Then, we propose a more accurate MILP model to describe the differential propagation through the modular addition taking into account the dependency between the consecutive modular additions. The proposed MILP model is utilized to launch a differential attack against Bel-T-256, which is a member of the Bel-T block cipher family that has been adopted recently as a national standard of the Republic of Belarus. In particular, we employ the concept of partial Differential Distribution Table to model the 8-bit S-Box of Bel-T using a MILP approach in order to automate finding a differential characteristic of the cipher. Then, we present a \(4\frac{1}{7}\)-round (out of 8) differential attack which utilizes a 3-round differential characteristic that holds with probability \(2^{-111}\). The data, time and memory complexities of the attack are \(2^{114}\) chosen plaintexts, \( 2^{237.14} \) \(4\frac{1}{7}\)-round encryptions, and \(2^{224}\) 128-bit blocks, respectively.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".