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- W4386198531 abstract "The identification of a cryptosystem has been a challenge for decades. This paper’s main objective is to identify the type of cryptosystem used to encrypt a particular text. We have explored the realm of machine learning to recognize a pattern among complex classical ciphertexts that generally have a simple representation in plaintext. We have modeled our objective as a sequence-to-sequence learning task that we have tried to solve using Convolution Neural Networks (CNNs) and state-of-the-art Transformer models. With only a tiny dataset (130 k) consisting of ciphertexts and the corresponding cryptosystem used to encrypt the same, our model has shown a good accuracy of 96.72 % which proves a significantly steep learning curve compared to other sequence-to-sequence models. Here we show the enormous potential of these models and how they can perform even better if the barrier of resources and computation time is lifted." @default.
- W4386198531 created "2023-08-27" @default.
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- W4386198531 date "2023-01-01" @default.
- W4386198531 modified "2023-09-25" @default.
- W4386198531 title "Detection of Cipher Types Using Machine Learning Techniques" @default.
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- W4386198531 doi "https://doi.org/10.1007/978-981-99-3734-9_25" @default.
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