A Resilient Cloud-based DDoS Attack Detection and Prevention System

Cloud service providers generally rely on firewall and IDS targeting on volume-based detection applied to all subscribers. However, there are various data processing requirements especially the different transaction volume and data format supported by different web applications or web services. The general volume-based rule is incapable to address the fine-grained DDoS attack detection for web applications required resilient detection based on the statistical policy of individual cloud client. This paper proposes a design and implementation of a Cloud-based DDoS Attack Detection and Prevention System called CloudGuard system that offers a more fine-grained detection based on the integration of our proposed volume-based analysis and statistical web profile-based approach. Specifically, we proposed a tree-based DDoS detection model to efficiently detect and give response to DDoS attacks happening in the cloud environment. Furthermore, our proposed system entails a preventive mechanism based on the preventive policy to handle the case detected. Finally, we conducted the experiments to substantiate that our proposed scheme is functionally correct and efficient in practice.