SDN中基于条件熵和GHSOM的DDoS攻击检测方法

田俊峰; 齐鎏岭

doi:10.11959/j.issn.1000-436x.2018140

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SDN中基于条件熵和GHSOM的DDoS攻击检测方法

论文Ⅱ：学术论文 | 更新时间：2024-06-05

- SDN中基于条件熵和GHSOM的DDoS攻击检测方法
- DDoS attack detection method based on conditional entropy and GHSOM in SDN
- 通信学报 2018年39卷第8期页码：140-149
- 作者机构：
  
  1. 河北大学网络空间安全与计算机学院，河北保定 071002
  2. 河北省高可信信息系统重点实验室，河北保定 071002
- 作者简介：
  
  [ "田俊峰（1965-），男，河北保定人，河北大学教授、博士生导师，主要研究方向为信息安全与分布式计算。" ]
  [ "齐鎏岭（1992-），男，河北保定人，河北大学硕士生，主要研究方向为信息安全与分布式计算。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61170254);河北省自然科学基金资助项目(F2016201244)
- DOI：10.11959/j.issn.1000-436x.2018140
  中图分类号： TP309
- 网络首发：2018-08，
  
  纸质出版：2018-08-25
- 稿件说明：
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田俊峰, 齐鎏岭. SDN中基于条件熵和GHSOM的DDoS攻击检测方法[J]. 通信学报, 2018,39(8):140-149.

Junfeng TIAN, Liuling QI. DDoS attack detection method based on conditional entropy and GHSOM in SDN[J]. Journal on Communications, 2018, 39(8): 140-149.
田俊峰, 齐鎏岭. SDN中基于条件熵和GHSOM的DDoS攻击检测方法[J]. 通信学报, 2018,39(8):140-149. DOI： 10.11959/j.issn.1000-436x.2018140.

Junfeng TIAN, Liuling QI. DDoS attack detection method based on conditional entropy and GHSOM in SDN[J]. Journal on Communications, 2018, 39(8): 140-149. DOI： 10.11959/j.issn.1000-436x.2018140.

摘要

软件定义网络（SDN

software defined networking）简化了网络结构，但同时控制器也面临着“单点失效”的安全威胁。攻击者可以发送大量交换机流表中并不存在的伪造数据流，影响网络正常性能。为了准确检测这种攻击的存在，提出了基于条件熵和 GHSOM（growing hierarchical SOM）神经网络的 DDoS 攻击检测方法MBCE＆amp;G 。首先，依据此DDoS的阶段性特征，定位了网络中的受损交换机以发现可疑攻击流；然后，依据可疑攻击流种类的多样性特征，以条件熵的形式提取了四元组特征向量，将其作为神经网络的输入特征进行更加精确的分析；最后，搭建了实验环境完成验证。实验结果显示，MBCE＆amp;G检测方法可以有效检测SDN中的DDoS攻击。

Abstract

Software defined networking (SDN) simplifies the network architecture

while the controller is also faced with a security threat of “single point of failure”.Attackers can send a large number of forged data flows that do not exist in the flow tables of the switches

affecting the normal performance of the network.In order to detect the existence of this kind of attack

the DDoS attack detection method based on conditional entropy and GHSOM in SDN (MBCE＆amp;G) was presented.Firstly

according to the phased features of DDoS

the damaged switch in the network was located to find the suspect attack flows.Then

according to the diversity characteristics of the suspected attack flow

the quaternion feature vector was extracted in the form of conditional entropy

as the input features of the neural network for more accurate analysis.Finally

the experimental environment was built to complete the verification.The experimental results show that MBCE＆amp;G detection method can effectively detect DDoS attacks in SDN network.

关键词

Keywords

references

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