Artificial intelligence applied to the identification of Block Ciphers under CBC Mode
This research introduces a novel methodology for identifying symmetric cryptosystems operating in Cipher Block Chaining (CBC) mode based solely on encrypted texts. The approach combines statistical tests from NIST STS with machine learning algorithms, analyzing DES, 3DES, Blowfish, Camellia, and AES...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | Brasil |
| Institución: | Marinha do Brasil (MB) |
| Repositorio: | Repositório Institucional da Produção Científica da Marinha do Brasil (RI-MB) |
| Idioma: | inglés |
| OAI Identifier: | oai:www.repositorio.mar.mil.br:ripcmb/846594 |
| Acceso en línea: | https://www.repositorio.mar.mil.br/handle/ripcmb/846594 |
| Access Level: | acceso abierto |
| Palabra clave: | Criptografia Inteligência artificial Modo CBC Segurança da informação |
| Sumario: | This research introduces a novel methodology for identifying symmetric cryptosystems operating in Cipher Block Chaining (CBC) mode based solely on encrypted texts. The approach combines statistical tests from NIST STS with machine learning algorithms, analyzing DES, 3DES, Blowfish, Camellia, and AES. The experimental results demonstrate an 84% identification rate for multiclass identification using random keys and initialization vectors. These findings are valuable in the field of information security and aid in minimizing cryptanalytic efforts. |
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