Adaptive firefly algorithm for resource allocation and modified advanced encryption standard algorithm for hypervisor attack detection on cloud computing

Abstract

Introduction: the advantages and wide-ranging applications of cloud computing have made it a prominent attention for scholars these days. The dispersed structure of cloud and its whole dependence on the internet for service delivery extant security problems. Method: hypervisor attack detection is carried out in this study using Modified Advanced Encryption Standard (MAES) system which ensures security prominently. It finds and detects the attacks earlier for secured VM migration. The proposed system includes main phases including system framework, load balancing, resource allocation and hypervisor attack detection via MAES algorithm. Initially, Over the duration of cloud computing, consider the quantity of tasks, VM, and cloud users. In this research, the MMH system is employed for load balancing to balance the total burden across the cloud. Tasks are moved from overloaded to underloaded nodes to attain load balancing. Next, the allocation of resources is carried out utilizing Adaptive Firefly (AF) optimization system which is used to select best resources optimally. It generates the best fitness values to choose the best resources. Results: it is also focused to improve the cost metric, computational complexity, throughput and VM performance in cloud. Then, to detect hypervisor attacks, the MAES method is employed. It specializes on offering enhanced security for cloud data and is employed to identify hypervisor and VM attackers. Conclusions: the findings produced the conclusion that the suggested MAES method superior to the current approaches according to throughput, computation cost, Mean Square Error (MSE) rate, and energy use