Bayesian Classifier and Snort based network intrusion detection system in cloud computing

Abstract

One of the major security issues in cloud computing is to protect against network intrusions that affect confidentiality, availability and integrity of Cloud resources and offered services. To address this issue, we design and integrate Bayesian Classifier and Snort based network intrusion detection system (NIDS) in Cloud. This framework aims to detect network intrusions in Cloud environment with low false positives and affordable computational cost. To ensure feasibility of our NIDS module in Cloud, we evaluate performance and quality results on KDD'99 experimental dataset.