Optimized and Intelligent IDS for detecting the vulnerability of DDoS attacks in cloud environment

Abstract

The proliferation of computing power and wide range of cloud services has resulted in an increase in the frequency and severity of cyberattacks. DDoS attacks, which target multiple hosts at a time, are gradually increasing due to the decentralized nature of cloud-based service delivery systems. In this study, a novel Intrusion Detection System (IDS) is presented for improving network performance by the accurate detection of Distributed Denial-of-Service (DDoS) attacks in cloud environment. The proposed IDS framework is equipped with a novel genetic algorithm based arithmetic optimization algorithm for attack vector selection and an intelligent ensemble Voting Classifier based on Woodpecker based Flamingo Search optimization algorithm for DDOS attack detection and classification. The proposed IDS outperforms various existing approaches in terms of network performance, DDOS attack prediction and mitigation rate.