Author:
Leierzopf Ernst,Mikhalev Vasily,Kopal Nils,Esslinger Bernhard,Lampesberger Harald,Hermann Eckehard
Reference19 articles.
1. Abd, A., Al-Janabi, S.: Classification and identification of classical cipher type using artificial neural networks. J. Eng. Appl. Sci. 14, 3549–3556 (2019)
2. American Cryptogram Association: Cryptogram (2005). https://www.cryptogram.org/. Visited 14 April 2021
3. Beaulieu, R., Shors, D., Smith, J., Treatman-Clark, S., Weeks, B., Wingers, L.: The SIMON and SPECK lightweight block ciphers. In: Proceedings of the 52nd Annual Design Automation Conference, pp. 1–6. Association for Computing Machinery, San Francisco California, June 2015
4. Brownlee, J.: Why use ensemble learning? October 2020. https://machinelearningmastery.com/why-use-ensemble-learning/. Visited 14 April 2021
5. Lecture Notes in Computer Science;Aron Gohr,2019
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Pigpen Cipher 9-Grid Characters Classification Using Enhanced CNN Deep Learning Architecture;2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS);2024-06-28
2. The Cryptographic Algorithm Identification: Using Deep Learning to Empower Smart Grids;2024 International Conference on Machine Intelligence and Digital Applications;2024-05-30
3. Classifying World War II era ciphers with machine learning;Cryptologia;2024-03-14
4. A New Hybrid Cipher based on Prime Numbers Generation Complexity: Application in Securing 5G Networks;2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA);2023-12-04
5. Detection of Cipher Types Using Machine Learning Techniques;Computational Intelligence in Pattern Recognition;2023