Statistical integral attack on CAST-256 and IDEA

Autor: Meiqin Wang, Long Wen, Huaifeng Chen, Tingting Cui
Rok vydání: 2017
Předmět:
Zdroj: Cryptography and Communications. 10:195-209
ISSN: 1936-2455
1936-2447
Popis: Integral attack, as a powerful technique in the cryptanalysis field, has been widely utilized to evaluate the security of block ciphers. Integral distinguisher is based on balanced property on output with probability one. To obtain a distinguisher covering more rounds, an attacker will usually increase the data complexity by iterating through all values of more bits of plaintexts under the firm limitation that the data complexity should be less than the whole plaintext space. In order to release the limitation and reduce the data complexity, Wang et al. proposed a statistical integral distinguisher at FSE’16. In this paper, we exploit the statistical integral distinguisher to attack the IDEA and CAST-256 block ciphers. As a result, we manage to mount a key recovery attack on 29-round CAST-256 with 296.8 chosen plaintexts, 2219.4 encryptions and 273 bytes of memory. By making a trade-off between the time complexity and data complexity, the attack can be achieved by 283.9 chosen plaintexts, 2244.4 encryptions and 266 bytes of memory. As far as we know, these are the best attacks on CAST-256 in the single-key model without weak-key assumption so far. What’s more, we find an integral distinguisher of IDEA block cipher, which is the longest integral distinguisher known to now. By taking advantage of this distinguisher, we achieve a key recovery attack on 4.5-round IDEA with 258.5 known plaintexts, 2120.9 encryptions and 246.6 bytes of memory respectively. It is the best integral attack with respect to the number of rounds.
Databáze: OpenAIRE
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