SDN中DDoS攻击检测研究综述

doi:10.3778/j.issn.1002-8331.2405-0367

摘要/Abstract

摘要： 软件定义网络（SDN）的出现弥补了传统网络的不足并为网络管理带来技术革新。分布式拒绝服务（DDoS）攻击作为网络安全领域的主要威胁之一，严重影响着SDN这一新兴网络架构。随着SDN技术的部署及发展，如何在SDN中检测DDoS攻击成为当前研究领域的热点与难点。为了对相关研究工作进行合理综述，根据所使用的核心技术或理论的不同，将DDoS攻击检测方法划分为基于信息熵、基于机器学习、基于深度学习三大类。介绍SDN体系架构并分析SDN中的DDoS攻击，同时介绍一些常用的公开数据集和评估指标，从四个角度归纳和分析近年来相关研究人员提出的模型或算法，总结了SDN中的DDoS攻击检测研究领域的未来研究方向并进行展望，为该领域的相关研究人员提供研究思路。

关键词: 软件定义网络, 分布式拒绝服务攻击, 信息熵, 机器学习, 深度学习

Abstract: The emergence of software defined networking (SDN) makes up for the shortcomings of traditional networks and brings technological innovation to network management. Distributed denial-of-service (DDoS) attacks, as one of the major threats in the field of network security, seriously affect the emerging network architecture of SDN. With the deployment and development of SDN technology, how to detect DDoS attacks in SDN has become a hot and difficult point in the current research field. In order to provide a reasonable overview of related research work, DDoS attack detection methods are divided into three categories：information entropy-based, machine learning-based, and deep learning-based, according to the different core technologies or theories used. This paper introduces the SDN architecture and analyzes DDoS attacks within SDN, along with presenting some commonly used public datasets and evaluation indicators, then it summarizes and analyzes models or algorithms proposed by relevant researchers in recent years from four perspectives, and finally, it summarizes the future research directions and prospects in the field of DDoS attack detection in SDN, provide research ideas for relevant researchers in this field.

Key words: software-defined network, distributed denial-of-service attack, information entropy, machine learning, deep learning

郑承蔚, 王海凤, 刘瑞. SDN中DDoS攻击检测研究综述[J]. 计算机工程与应用, 2024, 60(24): 79-96.

ZHENG Chengwei, WANG Haifeng, LIU Rui. Review of Research on DDoS Attack Detection in SDN[J]. Computer Engineering and Applications, 2024, 60(24): 79-96.

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