Mitigating Data Migration Risks in the Cloud via GA-Optimized Hybrid Cryptography Mechanisms

Cloud computing has become one of the leading paradigms for bulk data storage and retrieval. However, ensuring data security remains a critical challenge, particularly during transmission when data is most vulnerable. Ensuring data security during transmission remains a critical challenge. The traditional algorithms focus mainly on reducing execution time and using static keys, which can create patterns in the ciphertext that an attacker can exploit. This study introduces an optimized ECC-AES-GA algorithm, which uses the Genetic Algorithm (GA) for generating an optimized parameter for ECC and bulk data is encrypted using the AES-256 algorithm. This algorithm provides security against man-in-the-middle, eavesdropping, replay, brute-force, impersonation, and forward secrecy attacks. The algorithm is also tested with the state-of-the-art algorithms and provides better results in terms of encryption/decryption and security parameters. Experimental analysis are performed based on avalanche tests, entropy level, throughput, execution time, and 10-times-run tests. Furthermore, the algorithm passed all necessary NIST STS tests, which confirms its cryptographic randomness and reliability. It offers security and efficiency, which improves computational overhead, thereby strengthening secure data migration in cloud environments.

@article{Dhaman2026,
  title     = {Mitigating Data Migration Risks in the Cloud via GA-Optimized Hybrid Cryptography Mechanisms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Anjali Dhaman and Ugrasen Suman},
  doi       = {10.14569/IJACSA.2026.0170686},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170686}
}