Keyword Security Implementation Based on Hill Cipher Optimized Using Genetic Algorithms

  • Mayang Arinda Yudantiar Informatics Department, Universitas Dian Nuswantoro
  • Purwanto Purwanto Informatics Department, Universitas Dian Nuswantoro
  • Sri Winarno Informatics Department, Universitas Dian Nuswantoro
Abstract views: 32 , PDF downloads: 53
Keywords: Cryptography, Symmetric Key, Hill Cipher, Matrix Optimization, Genetic Algorithm

Abstract

In the process of exchanging data and information, the most important task is to maintain data and information security and reach out to interested parties. One way this can be achieved is through encryption, a process better known as cryptography. Cryptography can scramble messages so that, even if intercepted, the message cannot be immediately read. One example of an encryption algorithm is the Hill Cipher. The Hill Cipher uses an m-by-m-sized matrix as the key for the encryption and decryption process, making it a challenging algorithm to crack. The key provided for the Hill Cipher encryption and decryption process cannot be arbitrary. The keys with mismatched determinants cannot be used, as they can prevent the encrypted message from being restored to its original form. Optimization can be carried out to overcome these obstacles using a genetic algorithm. Genetic algorithms can determine the keys to encrypt and decrypt the Hill Cipher. A key with the appropriate composition for the Hill Cipher will be obtained through the genetic algorithm's evaluation function. This research aims to enhance message security by using the correct composition to generate Hill Cipher encryption and decryption keys. The research results indicate that out of 10 tests conducted with different lengths of original text, eight succeeded, while two failed to complete the encryption and decryption process.

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Published
2023-11-30
How to Cite
Yudantiar, M. A., Purwanto, P., & Winarno, S. (2023). Keyword Security Implementation Based on Hill Cipher Optimized Using Genetic Algorithms. International Journal of Artificial Intelligence & Robotics (IJAIR), 5(2), 44-53. https://doi.org/10.25139/ijair.v5i2.6907