A novel framework for intrusion detection in cloud

Abstract

One of the major security challenges in cloud computing is the detection and prevention of denial-of-service (DoS) attacks. In order to detect and prevent DoS attacks as well as other malicious activities at the network layer, we propose a framework which integrates a network intrusion detection system (NIDS) in the Cloud infrastructure. We use snort and decision tree (DT) classifier to implement this framework. It aims to detect network attacks in Cloud by monitoring network traffic, while maintaining performance and service quality. To validate our approach, we evaluate the performance and detection efficiency by using the freely available NSL-KDD and KDD experimental intrusion datasets. The results show that the proposed framework has a higher detection rate with low false positives at an affordable computational cost.