Enhancing Cryptographic Resilience through Symmetric Encryption Algorithm Utilizing Variable Length Chromosomes Genetic Algorithm

Ahmed Jobaer; Nur Hafiza Zakaria; Farida Ridzuan; A H Azni

doi:10.33102/mjosht.522

Authors

Ahmed Jobaer Faculty of Science and Technology, Universiti Sains Islam Malaysia, Nilai 71800, Negeri Sembilan, Malaysia.
Nur Hafiza Zakaria CyberSecurity and Systems Research Unit, Faculty of Science and Technology, Universiti Sains Islam Malaysia, Nilai 71800, Negeri Sembilan, Malaysia.
Farida Ridzuan Faculty of Science and Technology, Universiti Sains Islam Malaysia, Nilai 71800, Negeri Sembilan, Malaysia.
A H Azni CyberSecurity and Systems Research Unit, Faculty of Science and Technology, Universiti Sains Islam Malaysia, Nilai 71800, Negeri Sembilan, Malaysia.

DOI:

https://doi.org/10.33102/mjosht.522

Keywords:

symmetric cryptography, advanced encryption standard, genetic algorithm, randomness, Enhanced Encryption

Abstract

Symmetric cryptography, particularly the Advanced Encryption Standard (AES), is widely used for secure data transmission due to its computational efficiency and strong encryption structure. However, limited internal randomness in AES makes it susceptible to advanced cryptanalytic attacks. To address this limitation, this research proposes an enhanced encryption approach that integrates Genetic Algorithm (GA) techniques into the AES framework to enhance randomness. The GA employs variable-length chromosomes and entropy-based fitness evaluation to evolve dynamic binary outputs through selection, crossover, and mutation operations. These evolved outputs are used to introduce controlled randomness into the encryption process, resulting in unpredictable and robust ciphertext. The expected outcome includes improved increased randomness, and stronger resistance to differential and linear attacks. In conclusion, the proposed GA-AES enhanced model offers a mechanism to strengthen symmetric encryption by introducing evolutionary randomness, making it more secure for modern data protection needs.

Downloads

Download data is not yet available.

References

[1] J. N. Gaithuru and M. M. Bakhtiari, “Statistical analysis of S-Box in Rijndael-AES algorithm and formulation of an enhanced S-Box,” Journal of Information Assurance and Security, vol. 9, pp. 213–221, 2014.

[2] A. Msolli, I. Hagui and A. Helali, “Dynamic S-boxes generation for IoT security enhancement: A genetic algorithm approach,” Measurement: Sensors, vol. 30, p. 100923, 2024, doi: 10.1016/j.measen.2023.100923.

[3] D. James and T. L. Priya, “An innovative approach for dynamic key-dependent S-Box to enhance security of IoT systems,” Measurement: Sensors, vol. 30, p. 100923, 2023, doi: 10.1016/j.measen.2023.100923.

[4] M. Bilal, G. Murtaza, B. Demir, M. D. Bustamante and U. Hayat, “An efficient algorithm to generate dynamic substitution-boxes and its applications in image encryption,” Alexandria Engineering Journal, vol. 116, pp. 214–231, 2024, doi: 10.1016/j.aej.2024.11.014.

[5] A. Z. Zakaria, S. N. Ramli, C. W. Chuah, C. F. Mohd Foozy, P. S. S. Palaniappan and N. F. Othman, “Enhancing the randomness of symmetric key using genetic algorithm,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 8S, pp. 327–330, 2019.

[6] K. B. Sudeepa, G. Aithal, V. Rajinikanth and S. C. Satapathy, “Genetic algorithm based key sequence generation for cipher system,” Pattern Recognition Letters, vol. 133, pp. 341–348, 2020, doi: 10.1016/j.patrec.2020.03.015.

[7] J. Rodríguez, B. Corredor and C. Suárez, “Genetic operators applied to symmetric cryptography,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 5, no. 7, pp. 39–49, 2019, doi: 10.9781/ijimai.2019.07.006.

[8] S. Kumar and D. Sharma, “Key generation in cryptography using elliptic-curve cryptography and genetic algorithm,” Engineering Proceedings, vol. 59, no. 59, pp. 1–10, 2023, doi: 10.3390/engproc2023059059.

[9] B. T. Hammad, A. M. Sagheer, I. T. Ahmed and N. Jamil, “A comparative review on symmetric and asymmetric DNA-based cryptography,” Bulletin of Electrical Engineering and Informatics, vol. 9, no. 6, pp. 2484–2491, 2020, doi: 10.11591/eei.v9i6.2470.

[10] R. Jhingran, V. Thada and S. Dhaka, “A study on cryptography using genetic algorithm,” International Journal of Computer Applications, vol. 118, no. 20, pp. 10–15, 2015, doi: 10.5120/20860-3559.

[11] P. Pekarčík, E. Chovancová, M. Chovanec and M. Štancel, “Application of genetic algorithms in cryptography,” in Proc. IEEE Int. Conf. Intelligent Engineering Systems (INES), 2024, doi: 10.1109/INES63318.2024.10629081.

[12] M. Tahir, M. Sardaraz, Z. Mehmood and S. Muhammad, “CryptoGA: A cryptosystem based on genetic algorithm for cloud data security,” Cluster Computing, 2020, doi: 10.1007/s10586-020-03157-4.

[13] Y. Salami, V. Khajehvand and E. Zeinali, “Cryptographic algorithms: A review of the literature, weaknesses, and open challenges,” Journal of Computer & Robotics, vol. 16, no. 2, pp. 63–115, 2023, doi: 10.22094/JCR.2023.1983496.1298.

[14] S. N. H. Alsweedy and S. S. M. Aldabbagh, “Enhancing AES security through advanced S-Box design: Strategies and solutions,” International Research Journal of Innovations in Engineering and Technology, vol. 8, no. 8, pp. 182–192, 2024, doi: 10.47001/IRJIET/2024.808020.