About CipherLens
A machine learning system for automated classical cipher identification.
Classical cipher identification is the critical first step in any cryptanalysis workflow. Traditional manual methods are time-consuming and expertise-dependent.
CipherLens automates this process using machine learning. Given only ciphertext (no plaintext or keys), our models extract 15 statistical features and classify the text into one of 22 cipher types across 6 cryptographic families.
The system offers three model engines: a Hybrid CNN (79.24% acc) combining character-level patterns with statistical features, a CNN Deep Learning model (68.47% acc) reading raw character sequences, and an XGBoost hierarchical classifier using a two-stage family → cipher pipeline with soft-routing.
| Family | Ciphers | Count |
|---|---|---|
| Monoalphabetic Substitution | Caesar, Affine, Atbash | 3 |
| Polyalphabetic Substitution | Vigenere, Autokey, Beaufort, Porta | 4 |
| Transposition | Columnar Transposition | 1 |
| Polygraphic Substitution | Playfair, Hill, Four-Square | 3 |
| Fractionating | Bifid, Trifid, ADFGX, ADFGVX, Nihilist, Polybius | 6 |
| Modern Block | Lucifer, MISTY1, LOKI, TEA, XTEA | 5 |
| Total | 22 |