CipherSniffer: Classifying Cipher Types
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Abstract (arXiv)
Ciphers are a powerful tool for encrypting communication. There are many different cipher types, which makes it computationally expensive to solve a cipher using brute force. In this paper, we frame the decryption task as a classification problem. We first create a dataset of transpositions, substitutions, text reversals, word reversals, sentence shifts, and unencrypted text. Then, we evaluate the performance of various tokenizer-model combinations on this task.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 11 references that are in the map. First posted on arXiv 13 Jun 2023.
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References · 4
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,989
- 2016Robsut Wrod Reocginiton via semi-Character Recurrent Neural Networkcited by 18
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