Paper 2022/513
Characteristic Automated Search of Cryptographic Algorithms for Distinguishing Attacks (CASCADA)
, imec-COSIC KU LeuvenAbstract
Automated search methods based on Satisfiability Modulo Theories (SMT) problems are being widely used to evaluate the security of block ciphers against distinguishing attacks. While these methods provide a systematic and generic methodology, most of their software implementations are limited to a small set of ciphers and attacks, and extending these implementations requires significant effort and expertise. In this work we present CASCADA, an open-source Python library to evaluate the security of cryptographic primitives, specially block ciphers, against distinguishing attacks with bit-vector SMT solvers. The tool CASCADA implements the bit-vector property framework herein proposed and several SMT-based automated search methods to evaluate the security of ciphers against differential, related-key differential, rotational-XOR, impossible-differential, impossible-rotational-XOR, related-key impossible-differential, linear and zero-correlation cryptanalysis. The library CASCADA is the result of a huge engineering effort, and it provides many functionalities, a modular design, an extensive documentation and a complete suite of tests.
Note: Fixed typo in the definition of RX difference
Metadata
- Available format(s)
-
PDF
- Category
- Secret-key cryptography
- Publication info
- Published elsewhere. IET Information Security
- DOI
- 10.1049/ise2.12077
- Keywords
- cryptanalysis automated search SMT bit-vector theory
- Contact author(s)
- adrian ranea @ esat kuleuven be
- History
- 2022-11-22: last of 2 revisions
- 2022-05-02: received
- See all versions
- Short URL
- https://ia.cr/2022/513
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2022/513,
author = {Adrián Ranea and Vincent Rijmen},
title = {Characteristic Automated Search of Cryptographic Algorithms for Distinguishing Attacks ({CASCADA})},
howpublished = {Cryptology {ePrint} Archive, Paper 2022/513},
year = {2022},
doi = {10.1049/ise2.12077},
url = {https://eprint.iacr.org/2022/513}
}
