Abstract—Fast progression of digital data exchange in electronic way, information security is becoming much more important in data storage and transmission on public communication networks. Cryptography has come up as a solution which plays a vital role in information security system against malicious attacks. In cryptography, there are various cipher techniques such as monoalphabetic cipher, polyalphabetic cipher, etc. to support data confidentiality as security mechanisms. They are methods of encrypting plain text message into cipher text protecting it from adversaries. The process of encryption of alphabets is the converting original message into non readable form. One of the most popular cipher techniques is the Vigenère cipher. It is a polyalphabetic cipher technique which uses the Vigenère table for the process of encryption of alphabets. As the Vigenère cipher does not have the properties of diffusion and confusion, it is longer vulnerable to Kasiski and Friedman attacks based on letter frequency analysis. Thus, in this paper we propose a polyalphabetic cipher that is a new encryption and decryption technique with diffusion and confusion properties based on the concept of the complex cipher used by combining of Vigenère cipher with Affine cipher for the increase of data security in data storage and transmission on public communication networks. Our proposed technique can also be considered as a complex transformation technique from Affine cipher known as a monoalphabetic cipher technique to a new polyalphabetic cipher technique that is called Vigenère-Affine cipher.
Index Terms—Affine cipher, monoalphabetic cipher, polyalphabetic cipher, Vigenère cipher, complex transformation.
The authors are with the University of Computer Studies, Yangon, (UCSY), Myanmar (e-mail: tunmyataung@ucsy.edu.mm, htet2naing.h@gmail.com, ni2hla@ucsy.edu.mm).
Cite: Tun Myat Aung, Htet Htet Naing, and Ni Ni Hla, "A Complex Transformation of Monoalphabetic Cipher to Polyalphabetic Cipher: (Vigenère-Affine Cipher)," International Journal of Machine Learning and Computing vol. 9, no. 3, pp. 296-303, 2019.