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A Massive Machine-Learning Approach For Classical Cipher Type Detection Using Feature Engineering

Leierzopf, Kopal, Esslinger, Lampesberger and Hermann · 2021 · International Conference on Historical Cryptology

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Methods survey: cryptanalysis	SRC0D8NJBCE48D0
Methods survey: linguistics and datasets	SRC0D8NJBM8396W
Survey: inductive methods from fourteen fields	SRC0D8NJDAY13ME

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Leierzopf, E., Kopal, N., Esslinger, B. et al. 2021. A Massive Machine-Learning Approach For Classical Cipher Type Detection Using Feature Engineering. International Conference on Historical Cryptology.

@article{leierzopf2021massive,
  author = {Leierzopf, Ernst and Kopal, Nils and Esslinger, Bernhard and Lampesberger, Harald and Hermann, Eckehard},
  title = {A Massive Machine-Learning Approach For Classical Cipher Type Detection Using Feature Engineering},
  journal = {International Conference on Historical Cryptology},
  year = {2021},
  doi = {10.3384/ecp183164},
  url = {https://doi.org/10.3384/ecp183164}
}

DOI: 10.3384/ecp183164
Web: https://doi.org/10.3384/ecp183164
Licence: CC-BY-4.0
Cite key: leierzopf2021massive
SHA-256: 72882b67bcdbb4931cba4dddaf6e0471706b5316643a7a3306c07afcceb96613

In the research Session 6: Phase 2: registered tests, brute force, five surveys; book v1.1ConsultedSession 9: The inquiry framework, the inductive battery, generated hypotheses and readingsConsulted

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v1 · 25 Oct 2026 · Voynich Manuscript: sync from the research ledger