SDN环境下基于机器学习算法的DDoS攻击检测模型

马乐乐; 束永安

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SDN环境下基于机器学习算法的DDoS攻击检测模型

更新时间：2026-09-04

- SDN环境下基于机器学习算法的DDoS攻击检测模型
- A DDoS Attack Detection Model Based on Machine Learning Algorithm in SDN Environment
- 微电子学与计算机 2018年35卷第5期页码：15-20
- 作者机构：
  
  安徽大学计算机科学与技术学院,安徽,合肥,230601
- 作者简介：
- 基金信息：
  
  安徽省自然科学基金 1408085MF125
- DOI：
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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马乐乐, 束永安. SDN环境下基于机器学习算法的DDoS攻击检测模型[J]. 微电子学与计算机, 2018, 35(5): 15-20.

MA Le-le, SHU Yong-an. A DDoS Attack Detection Model Based on Machine Learning Algorithm in SDN Environment[J]. 2018, 35(5): 15-20.
马乐乐, 束永安. SDN环境下基于机器学习算法的DDoS攻击检测模型[J]. 微电子学与计算机, 2018, 35(5): 15-20. DOI：

MA Le-le, SHU Yong-an. A DDoS Attack Detection Model Based on Machine Learning Algorithm in SDN Environment[J]. 2018, 35(5): 15-20. DOI：

摘要

软件定义网络(SDN)是一种新兴的网络架构

将控制逻辑和转发逻辑分离.SDN网络中

控制器拥有对网络的全局控制能力.正是由于控制器的这一特性

使得它成为分布式拒绝服务(DDoS)攻击的主要目标.针对这个问题

提出了一种基于机器学习的方法来检测DDoS攻击的模型

首先使用熵值检测流是否异常

发出异常警告后提取网络流特征

依次调用SVM与K-means两种机器学习算法来检测DDoS攻击.实验结果表明

所提出的算法能够降低误报率

并且对DDoS攻击的检测率和准确率高于原始的SVM和K-means算法.此外

还通过实验验证了所提出的模型的CPU平均利用率低于无熵值检测的SVM+K-means算法.

Abstract

The Software Defined Network(SDN)is an emerging network architecture that separates control logic from forwarding logic.In SDN

the controller has a global control of the network.Because of this feature of the controller

making it becomes the main goal of the distributed denial of service(DDoS)attack.Aiming at this problem

this paper proposes a method based on machine learning to detect the DDoS attack model.Firstly

it uses the entropy to check whether the traffic is abnormal.After extracting the abnormal alarm

the network flow feature is extracted

and SVM and K-means are called to detect the DDoS attacks.The experimental results show that the proposed algorithm can reduce the false alarm rate

and the detection rate and accuracy of DDoS attacks are higher than those of the original SVM and K-means.In addition

the experimental results show that the average CPU utilization rate of the proposed model is lower than that of SVM + K-means without entropy detection

关键词

Keywords

references

Van Trung P, Huong T T, Van Tuyen D, et al. A multi-criteria-based DDoS-attack prevention solution using software defined networking[C]//2015International Conference on Advanced Technologies for Communications(ATC). Ho Chi Minh City, 2015: 308-313.

Barki L, Shidling A, Meti N, et al. Detection of distributed denial of service attacks in software defined networks[C]//2016International Conference on Advances in Computing, Communications and Informatics(ICACCI). Jaipur, 2016: 2576-2581.

Jadidi J Z, Muthukkumarasamy V, Sithirasenan E, et al. Flow-based anomaly detection using neural network optimized with gsa algorithm[C]//2013IEEE 33rd International Conference on Distributed Computing Systems Workshops. Philadelphia, PA, 2013: 76-81.

Robinson R R R, Thomas C. Ranking of machine learning algorithms based on the performance in classifying DDoS attacks[C]//2015IEEE Recent Advances in Intelligent Computational Systems(RAICS). Trivandrum, 2015: 185-190.

Nunes B A A, Mendonca M, Nguyen X N, et al. A survey of software-defined networking: past, present, and future of programmable networks[J]. IEEE Communications Surveys % amp; amp; Tutorials, 2014, 16(3): 1617-1634. DOI: 1617

Kreutz D, Ramos F M V, Veríssimo P E, et al. Software-defined networking: a comprehensive survey[J]. Proceedings of the IEEE, 2015, 103(1): 14-76. DOI: 14

Tang T A, Mhamdi L, McLernon D, et al. Deep learning approach for network intrusion detection in software defined networking[C]//2016International Conference on Wireless Networks and Mobile Communications(WINCOM). Marrakesh, Morocco, 2016: . 258-263.

Mousavi S M, St-Hilaire M. Early detection of DDoS attacks against SDN controllers[C]//2015International Conference on Computing, Networking and Communications(ICNC). Garden Grove, CA, 2015: 77-81.

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