Enhanced related-key differential neural distinguishers for SIMON and SIMECK block ciphers
Gao Wang, Gaoli Wang · 2024
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Description
Gao Wang and Gaoli Wang published a cryptanalysis study in twenty twenty-four teaching neural networks to attack the SIMON and SIMECK lightweight ciphers. Since twenty nineteen, deep learning distinguishers have probed how many cipher rounds leak patterns. This work improves the related-key variant, where the attacker also varies the key.
A weighted scoring method first ranks which input differences are worth feeding the network. An enhanced design then combines two input differences at once. Accuracy rose measurably over prior work, including gains of about three and two percent at twelve and thirteen rounds of one cipher variant. Stronger distinguishers at more rounds for several variants followed. Each accuracy point translates into a sharper measure of a cipher's safety margin.
Catalog record
| CREATOR | Gao Wang, Gaoli Wang |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 148 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |