CipherSniffer: Classifying Cipher Types
This work addresses the challenge of efficient cipher analysis for cryptography and security applications, but it appears incremental as it applies existing methods to a new dataset.
The paper tackled the problem of computationally expensive cipher decryption by framing it as a classification task, evaluating various tokenizer-model combinations on a dataset of cipher types and unencrypted text.
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.