A novel hybrid-network intrusion detection system (H-NIDS) in cloud computing

Abstract

To detect and prevent network intrusions in Cloud computing environment, we propose a novel security framework hybrid-network intrusion detection system (H-NIDS). We use different classifiers (Bayesian, Associative and Decision tree) and Snort to implement this framework. This framework aims to detect network attacks in Cloud by monitoring network traffic, while ensuring performance and service quality. We evaluate the performance and detection efficiency of H-NIDS for ensuring its feasibility in Cloud. The results show that the proposed framework has higher detection rate and low false positives at an affordable computational cost.