A subset feature selection based DDOS detection using cascade correlation optimal neural network for improving network resources in virtualized cloud environment

Abstract

Cloud computing offers a technological revolution to the end-users need less infrastructure costs with virtualizes resources, and storage remains the insecure to delivers the scalability. The most common type of Distributed Denial of Service DDoS attack, (denial of service), is a serious damage measure that affects virtual cloud users and Internet Service Providers (ISPs) are predominantly affects ongoing service attacks. I'm the recipient. These legacy of machine learning approach used to detect vulnerabilities to the attacker's leading network traffic intervention opening the door. By concentrating feature selection and classification approach with optimized neural network model to detect the DDoS type monitoring. This presents a deep neural network based DDoS detection system using Subset Feature Selection based Cascade Correlation Optimal Neural Network (SFS-C2ONN). The proposed approach is based on assumptions based on flow rate which is collected as dataset previously extracted from a model for network traffic. The test results shows that the sensitivity and specify based calcification approach which is suitable for the detection of neural network architecture and hyper parameters, and the optimizer DDoS attack. The results are obtained by calculating the accuracy of the attack detection.