Deep belief network and support vector machine fusion for distributed denial of service and economical denial of service attack detection in cloud

Cloud computing is a progressive technology that offers computing resources as Internet-based services, revolutionized information, and communication technologies. From an economic standpoint, this transformation is beneficial because it allows them to streamline technology infrastructure and capital costs. However, economical denial of service (EDoS) potential is a crucial impediment to cloud computing success. Several improved ways to detect EDoS and distributed denial of service (DDoS) attacks in the cloud have been presented; nevertheless, these approaches still result in a considerable reduction in detection accuracy when employed in a cloud setting. Because selecting relevant features and precise classifiers for attack detection is a challenge. We recommend using an EDoS and DDoS attack identification framework in the cloud based on optimized deep learning techniques for higher detection accuracy. The experimental results reveal True Positive Rate (TPR) varies from 98.9% to 99.8% when using deep belief network with support vector machine as a learning mechanism, while True Negative Rate (TNR) from 99.6% to 99.9%. TPR and TNR were found to have average values of 99.32% and 99.67%, respectively. At 1600 requests/s, the maximum accuracy achieved and the overall accuracy of the proposed strategy was 99.78%.