Abstract

Data fragmentation operates to build pieces based on the available virtual machines (Vm) of financial cloud security. The replication process tends to enhance the security of data from one network to another network with fewer replications. An advanced financial cloud security system that utilizes AI to divide and copy data improves incursion security by distributing information over several locations and employing complex algorithms to handle and preserve data distribution. The disadvantage of this strategy is an overreliance on AI, which can result in flaws, raising the danger of data breaches and undermining system security and integrity. To overcome this issue, this study proposes an improved butterfly optimized finite elliptic curve cryptography (IBO-FECC) for autonomous technique and advanced encryption method that safeguard valuable financial information across cloud settings which strengthens data security and privacy. The system architecture employs networks to conduct cloud-based tests with successful results. The system efficiency is determined using task frequency and Vm capability. The findings are evaluated according to the unit count and virtual machine configurations, response time and throughput fluctuate with data size, but Vm=15 consistently outperformed Vm=5. Memory utilization is developed as the data becomes more complicated. Data fragmentation increases the speed of processing at a higher velocity. The study concluded that the suggested IBO-FECC identifies the optimal network pathway in financial cloud safety systems regarding system resiliency and security.