A cooperative DDoS attack detection scheme based on entropy and ensemble learning in SDN

EURASIP Journal on Wireless Communications and Networking

Jicheng Zhang¹  Yafeng Li¹  Ju Liu¹  Xiaoqing Zhang¹  Tianfeng Xu¹  Shanshan Yu¹ 

[1] School of Information Science and Engineering, Shandong University;

关键词: Software-defined network;    Distributed denial of service;    Edge switch;    Entropy;    Ensemble learning;   

DOI  :  10.1186/s13638-021-01957-9

来源: DOAJ

【 摘 要 】

Abstract In order to solve the problem of distributed denial of service (DDoS) attack detection in software-defined network, we proposed a cooperative DDoS attack detection scheme based on entropy and ensemble learning. This method sets up a coarse-grained preliminary detection module based on entropy in the edge switch to monitor the network status in real time and report to the controller if any abnormality is found. Simultaneously, a fine-grained precise attack detection module is designed in the controller, and a ensemble learning-based algorithm is utilized to further identify abnormal traffic accurately. In this framework, the idle computing capability of edge switches is fully utilized with the design idea of edge computing to offload part of the detection task from the control plane to the data plane innovatively. Simulation results of two common DDoS attack methods, ICMP and SYN, show that the system can effectively detect DDoS attacks and greatly reduce the southbound communication overhead and the burden of the controller as well as the detection delay of the attacks.

【 授权许可】

Unknown