Deep Ensemble Classification with Self Improved Optimization for Attack Detection Towards Secured Virtualization in Cloud

Abstract

Due to the extraordinary advances in tech companies and development in cloud computing, it has turned out to be ever more familiar for companies to integrate virtualization in data centers to entirely exploit their hardware sources. Accordingly, virtualization and security have gone under varied changes recently. Virtualization and its exceptional design have numerous advantages and characteristics over conventional non-virtualized technologies. Nevertheless, these novel characters form novel vulnerabilities and probable attacks in virtualized systems. Many recent studies have developed to detect those attacks, but they still have challenges like, need to invest more, if it must be done fast, data security could be at danger and scalability could be difficult. As a result, the primary goal of this study is to detect attack for getting secured virtualization in cloud. Therefore, the purpose of this research is to suggest a new, two-stage, protected virtualization paradigm for the cloud. At first, the “higher-order statistical features, flow-based features, mutual information (MI) based features and improved correlation features” are derived. Deep convolutional neural network (DCNN) 1 and 2 are then used to classify the derived features. To detect network assaults, LSTM 1 and 2 are applied to the outputs from DCNN 1 and 2. Here, the Self Customized Aquila Optimizer is used to adjust the DCNN weights to their optimum level (SC-AO). Additionally, analysis is performed using a range of metrics. Particularly, the proposed HC + SC-AO scheme attained improved accuracy of 0.938108 and 0.958249 for both the datasets respectively.