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<p>In the modern world, cloud computing has profoundly changed almost every aspect of our lives and organizational structures. It is without a doubt one of the strategic trajectories for many businesses as well as most prevalent infrastructure for businesses as well as end users. One of the most well-liked cloud computing services is data outsourcing, which is continuously done to public or hybrid clouds. It’s essential to provide the necessary safeguards and handle cloud data carefully. Security must be implemented at every stage of data transfer process, including data discovery, classification, cataloguing of mission-critical data access. Therefore, this research proposed model develops novel technique in secure data migration in cloud network based on machine learning (ML) with cryptography methods. Here data migration is carried out in secure cloud environment using federated adversarial reinforcement cryptography algorithm with data optimization by fuzzy logic based firefly whale meta-heuristic binary swarm algorithm. the experimental analysis has been carried out based on various cloud network security analysis dataset in terms of packet delivery ratio, latency, throughput, QoS, data integrity. Effectiveness of the suggested framework when compared to current methods yields robust outcomes and demonstrates its increased security. The proposed model achieved 98% throughput, 96% packet delivery ratio, 97% latency, 95% quality of service, and 98% data integrity. The existing <b>PBE-ENCR attained</b> Packet delivery ratio (PDR) of 84%, Data integrity of 81%, Throughput of 86%, LATENCY of 85%, QOS of 87%, <b>SVM-GA</b> PDR of 90%, Data integrity of 93%, Throughput of 91%, LATENCY of 88%, QOS of 92%.</p>

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Federated meta heuristic cryptography algorithm with fuzzy logic model in cloud secure data migration

  • 期刊论文

  • 发表日期：

    2025年10月9日

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  • Kannan Srinivasan,
  • Guman Singh Chauhan,
  • Rahul Jadon,
  • Rajababu Budda,
  • Venkata Surya Teja Gollapalli,
  • Joseph Bamidele Awotunde

摘要

In the modern world, cloud computing has profoundly changed almost every aspect of our lives and organizational structures. It is without a doubt one of the strategic trajectories for many businesses as well as most prevalent infrastructure for businesses as well as end users. One of the most well-liked cloud computing services is data outsourcing, which is continuously done to public or hybrid clouds. It’s essential to provide the necessary safeguards and handle cloud data carefully. Security must be implemented at every stage of data transfer process, including data discovery, classification, cataloguing of mission-critical data access. Therefore, this research proposed model develops novel technique in secure data migration in cloud network based on machine learning (ML) with cryptography methods. Here data migration is carried out in secure cloud environment using federated adversarial reinforcement cryptography algorithm with data optimization by fuzzy logic based firefly whale meta-heuristic binary swarm algorithm. the experimental analysis has been carried out based on various cloud network security analysis dataset in terms of packet delivery ratio, latency, throughput, QoS, data integrity. Effectiveness of the suggested framework when compared to current methods yields robust outcomes and demonstrates its increased security. The proposed model achieved 98% throughput, 96% packet delivery ratio, 97% latency, 95% quality of service, and 98% data integrity. The existing PBE-ENCR attained Packet delivery ratio (PDR) of 84%, Data integrity of 81%, Throughput of 86%, LATENCY of 85%, QOS of 87%, SVM-GA PDR of 90%, Data integrity of 93%, Throughput of 91%, LATENCY of 88%, QOS of 92%.