Collaborative 'many to many' DDoS detection in cloud. (2016) - Ma, Siqi; Lo, David; Xi, Ning
Cloud computing provides a scalable and cost-effective environment for users to store and process data through the internet. However, it also causes distributed denial-of-service (DDoS) attacks. DDoS attacks risk systems outage and intend to disable the service to legitimate users. In this paper, due to the nature of its large-scale and coordinated attacks, we propose a collaborative prediction approach for detecting DDoS. Our approach provides a clean and direct solution to attack defense. The DDoS attacks follow certain patterns when employing a large number of compromised machines to request for service from the servers in the victim system. So we construct an attacker-server utility matrix by the number of packets and adopt matrix factorisation to detect potential attackers collaboratively. We derive the latent attacker vectors and latent server vectors to predict the unknown entries in the matrix. Experimental results on the NS-2 simulation networks demonstrate the superiority of our approach.