An efficient EDoS-DOME system in cloud computing using obfuscated IP spoofing technique and RCDH-ENN detection technique

Abstract

Cloud computing (CC) permits the end-users to access the network via a shared resources field. The vulnerabilities on the service providers will augment with the augmentation of the demand for CC. Economical denial of service (EDoS) attacks take over the supplier financially affecting the disparate organizations that utilize the cloud data. It is not possible to identify the hackers subsequent to EDoS attacks; however, their passage can well be detected and blocked. Nevertheless, loads of challenges are there to surmount. The work renders an effectual EDoS-Dome system in CC. Secured user authentication is initiated here with the instigation of the secret question key technique in the user registration together with the verification phase. After that, for the effective tracing back of the hacked data, an effectual Obfuscation technique is developed aimed at IP spoofing. To attain fast response time (RT) along with block the passage of attackers, a CI-RDA load balancer is developed. Lastly, the developed regression coefficients deer hunting-deep Elman neural network classifies the user data into a blacklist or white list centered on particular conditions. The experimentation's outcomes exhibit that the proposed work is effectual with 97.01% accuracy and 97.05% recalls when weighed against prevailing methods to classify the attacks. It also encompasses lower cost as well as fast RT with the equivalent web services, which signifies a safe model in opposition to the EDoS attack. KeywordsCloud computing • Distributed denial of service (DDoS) • Economical denial of service (EDoS) • EDoS Dome system • Internet protocol (IP) • IP spoofing • Confidence interval-red deer algorithm (CI-RDA) • And regression coefficients deer hunting-deep Elman neural network (RCDH-ENN)