Abstract
Cloud computing has become a pivotal component of all digital organizations and its adoption poses many security concerns for organizations’ data and stakeholders. Data integrity and privacy is prominent security threats that demand the development of efficient security models based on exact algorithms, heuristics, and meta-heuristic algorithms. This paper proposes a genetic algorithm-based block cipher, XECryptoGA, to generate the 128-bit random keys to maximizing Shannon's entropy. The XECryptoGA employs the shift cipher, XOR-operation, the concept of elitism, and the roulette wheel selection method. In XECryptoGA, the shift cipher, XOR operation, GA, and application of crossover and mutation are used for the encryption and decryption process. The shift cipher, GA, and XOR operation create confusion, while the application of crossover and mutation is used to generate the diffusion in the model. The proposed XECryptoGA has been tested using several small and large datasets on various security parameters: Key generation time, brute-force attacks, avalanche effects, and encryption and decryption throughputs. The simulation study confirms that the proposed XECryptoGA has achieved security goals: confidentiality, integrity, and privacy. The experiment results show that the proposed model performs better than CryptoGA regarding Key generation time, Brute force attacks, avalanche effect, and security providence. While better than the state-of-art algorithms AES, DES, 3DES, and BLOWFISH in terms of encryption throughput and decryption throughput.
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Jawed, M.S., Sajid, M. XECryptoGA: a metaheuristic algorithm-based block cipher to enhance the security goals. Evolving Systems 14, 749–770 (2023). https://doi.org/10.1007/s12530-022-09462-0
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DOI: https://doi.org/10.1007/s12530-022-09462-0