LAUSR

In the traditional distributed control network, due to the difficulty in detection and the ambiguous defense responsibility, it is not efficient and effective to detect Distributed Denial of Service (DDoS) attacks in the network where they are launched, which is so-called source-based defense mechanism. Moreover, with the development of cloud computing, Internet of Things (IoT), and mobile Internet, the number of terminals and the communication bandwidth in a single autonomous domain have increased significantly, providing much more easy conditions for organizing large-scale botnets to launch a threatening DDoS attack. Therefore, there is an urgent need for source-based defense against DDoS attacks. The emerging Software-Defined Networking (SDN) provides some new ideas and advantages to solve this problem, such as centralized control and network programmability. In this paper, we proposed a defense method based on sFlow and improved Self-Organizing Map (SOM) model in SDN. This method consists of an sFlow-based macro-detection, which could cover the entire network to perceive DDoS attacks, a SOM-based micro-detection, which is used to recognize the attack traffic, and a response strategy based on the global view given by the controller. The experimental results under open data and simulated attack scenarios have proved the effectiveness of the proposed method, and it also has better overall detection performance than k-means and k-medoids.