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.
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Mukherjee, A. et al. (2023). Detection of Cipher Types Using Machine Learning Techniques. In: Das, A.K., Nayak, J., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. CIPR 2022. Lecture Notes in Networks and Systems, vol 725. Springer, Singapore. https://doi.org/10.1007/978-981-99-3734-9_25
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DOI: https://doi.org/10.1007/978-981-99-3734-9_25
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