An artificial neural network approach to finding the key length of the Vigenère cipher
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866914776199725056 |
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| author | Millichap, Christian Yau, Yeeka |
| author_facet | Millichap, Christian Yau, Yeeka |
| contents | In this article, we create an artificial neural network (ANN) that combines both classical and modern techniques for determining the key length of a Vigenère cipher. We provide experimental evidence supporting the accuracy of our model for a wide range of parameters. We also discuss the creation and features of this ANN along with a comparative analysis between our ANN, the index of coincidence, and the twist-based algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_09956 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | An artificial neural network approach to finding the key length of the Vigenère cipher Millichap, Christian Yau, Yeeka Cryptography and Security In this article, we create an artificial neural network (ANN) that combines both classical and modern techniques for determining the key length of a Vigenère cipher. We provide experimental evidence supporting the accuracy of our model for a wide range of parameters. We also discuss the creation and features of this ANN along with a comparative analysis between our ANN, the index of coincidence, and the twist-based algorithms. |
| title | An artificial neural network approach to finding the key length of the Vigenère cipher |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2312.09956 |