In the recent past, cloud computing system and service (CCSS) security plays vital role in data security and preservation to keep the confidential information safe. Cloud security is up-coming as hopeful information technology solution for facilitating everywhere; suitable, on-demand admittance to a spited pool of organizes resources of cloud computing system and service (CCSS). The various sectors such as military, health, biomedical, space research, etc are required high degree of security to store information in the cloud computing system and service (CCSS). The extensive acceptance of cloud is still being delayed by requirement of simplicity and responsibility that has conventionally been processed through auditing techniques for cloud computing system and service (CCSS). In this paper, the methodology is proposed as a cloud computing security and privacy auditing research work to focus on runtime on the various frequent admissions organize and providing authentication on information security. More significantly, cloud security and data preservation also has grave consequence on the various parameters performance such as performance of security correlation. The Markov Randon Field (MRF) is used to assess the various security of cloud computing that possesses various attacks such as malicious attacks security protection technique. The major novel devise towards minimizing the answer occasion to a sensible stage is to execute the expensive processes. The security data development approach based on Ant Colony Optimization (ACO) and multiple routine mechanism of optimization is applied. The experimental results show as graphical representation that the proposed system is efficient and accurate than existing system.
Volume 12 | 04-Special Issue
Pages: 1114-1124
DOI: 10.5373/JARDCS/V12SP4/20201585