Skip to content
Free planNo credit card requiredStart writing

article · SHILAP Revista de lepidopterología · 2023

Improved integral attack——random linear distinguish and key recovery attack

Shaoyu DU

Cited by 0Open access

Abstract

Based on the integral attack and collision attack of four rounds of AES, a random linear distinguish attack against four rounds of SP block ciphers was proposed, which took advantage of the non-uniformity of linear biases’ distribution between some blocks of plaintext and inner state.Combined with precomputation, a key recovery attack against four rounds of AES-like block ciphers was proposed.For LED-64, the results of distinguish attack and key recovery attack were given.Therein for LED-64 of 1-Step, the probability of successful distinguish attack is 85% under the condition that the data complexity is 2<sup>8</sup> and the computational complexity is 2<sup>16</sup> basic operation.For LED-64 of 2-Step, the calculation complexity of the key recovery attack under the condition of related key is 2<sup>14</sup> basic operation, the data complexity is 2<sup>8</sup>, and the precomputation storage complexity is 2<sup>38</sup> half bytes.

Cite this paper

APA 7

DU, S. (2023). Improved integral attack——random linear distinguish and key recovery attack. SHILAP Revista de lepidopterología.

MLA 9

DU, Shaoyu. “Improved integral attack——random linear distinguish and key recovery attack.” SHILAP Revista de lepidopterología, 2023.

Chicago

DU, Shaoyu. 2023. “Improved integral attack——random linear distinguish and key recovery attack.” SHILAP Revista de lepidopterología.

Harvard

DU, S. (2023) ‘Improved integral attack——random linear distinguish and key recovery attack’, SHILAP Revista de lepidopterología.

BibTeX
@article{du2023improved,
  title = {{Improved integral attack——random linear distinguish and key recovery attack}},
  author = {DU, Shaoyu},
  journal = {SHILAP Revista de lepidopterología},
  year = {2023}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.