An Efficient DDoS Detection with Bloom Filter in SDN
Peng Xiao, Zhiyang Li, Heng Qi, Wenyu Qu, Haisheng Yu · 2016
Distributed Denial of Service (DDoS) attacks are becoming one of the major threats in the distributed data center networks which are loosely connected. Recent work has found new ways to attack network link instead of network servers. However, existing methods have limitations when detecting the link attacks, particularly in data store. To address this issue, in this paper, we propose an attack detection system that can deal with the link flooding attacks. Our method is based on Bloom Filter and Software-Defined Networking (SDN). We propose a real-time link attack detection system and define a two-module detection framework to detect the attacks. Then we apply our strategy in SDN. Extensive experiments on different settings have been performed, showing that our method is good at detecting the link flooding attack with high detection rates and low overhead.