Attack detection and mitigation using optimal deep maxout network in cloud computing

Abstract

Summary Cloud computing is a promising technique and the majority of information technology based services function in this infrastructure, which addresses the need of today's data center. There involve privacy and security problems in the cloud, which affected the utility of the cloud. Various mitigation solutions are developed in existing, in which the attacks continue to elevate in frequency and severity. This calls for a model for addressing the needs of challenging security threats. This article devises novel attack detection and mitigation technology using an optimization‐driven deep learning model. Here, the system comprises five modules, like data, data owner, cloud server, cloud user, and thin provision virtual volume. Here, each entity performs its own operations. The proposed technique includes five stages, namely, authentication phase, data sharing and communication phase, recorded log file creation phase, attack detection phase, and attack mitigation phase. An initial phase is the authentication phase where an authentication protocol is modeled using different mathematical functionalities. Once the authentication is done, the data sharing and communication are initiated for sharing the sensitive data. Thereafter the log file creation phase is initiated for storing the log entries. Then the attack detection is performed utilizing deep maxout network that is tuned by the developed gray wolf political optimizer (GWPO). The GWPO is the integration of gray wolf optimizer (GWO) and political optimizer (PO). Finally, attack mitigation is performed by reducing the data rates of attack nodes. The developed GWPO‐enabled deep maxout network provided improved performance with the highest accuracy of 97.9%, true positive rate of 97.3%, and true negative rate of 97.7%.