基于联合特征的LDoS攻击检测方法

吴志军; 张景安; 岳猛; 张才峰

doi:10.11959/j.issn.1000-436x.2017075

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基于联合特征的LDoS攻击检测方法

学术论文 | 更新时间：2024-06-05

- 基于联合特征的LDoS攻击检测方法
- Approach of detecting low-rate DoS attack based on combined features
- 通信学报 2017年38卷第5期页码：19-30
- 作者机构：
  
  中国民航大学电子信息与自动化学院，天津 300300
- 作者简介：
  
  [ "吴志军（1965-），男，河南固始人，博士，中国民航大学教授、博士生导师，主要研究方向为网络空间安全。" ]
  [ "张景安（1989-），男，山东临沂人，中国民航大学硕士生，主要研究方向为信息安全、拒绝服务攻击的入侵检测。" ]
  [ "岳猛（1984-），男，河北沧州人，中国民航大学讲师，主要研究方向为信息安全、云计算、拒绝服务攻击的入侵检测。" ]
  [ "张才峰（1991-），男，山东济南人，中国民航大学硕士生，主要研究方向为信息安全、拒绝服务攻击的入侵检测。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(U1533107);国家自然科学基金资助项目;The National Natural Science Foundation of China(U1433105);中央高校基本科研业务基金资助项目;Fundamental Scientific Research Foundation of the Central University(3122016D003);中国民航大学研究生课程案例开发基金资助项目;Case Development Project of Graduate Program in Civil Aviation University of China;天津市自然科学重点基金资助项目;Key Project of Tianjin Natural Science Foundation(17JCZDJC30900）)
- DOI：10.11959/j.issn.1000-436x.2017075
  中图分类号： TP393.08
- 网络首发：2017-05，
  
  纸质出版：2017-05-25
- 稿件说明：
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吴志军, 张景安, 岳猛, 等. 基于联合特征的LDoS攻击检测方法[J]. 通信学报, 2017,38(5):19-30.

Zhi-jun WU, Jing-an ZHANG, Meng YUE, et al. Approach of detecting low-rate DoS attack based on combined features[J]. Journal on Communications, 2017, 38(5): 19-30.
吴志军, 张景安, 岳猛, 等. 基于联合特征的LDoS攻击检测方法[J]. 通信学报, 2017,38(5):19-30. DOI： 10.11959/j.issn.1000-436x.2017075.

Zhi-jun WU, Jing-an ZHANG, Meng YUE, et al. Approach of detecting low-rate DoS attack based on combined features[J]. Journal on Communications, 2017, 38(5): 19-30. DOI： 10.11959/j.issn.1000-436x.2017075.

摘要

低速率拒绝服务（LDoS

low-rate denial of service）攻击是一种降质服务（RoQ

reduction of quality）攻击，具有平均速率低和隐蔽性强的特点，它是云计算平台和大数据中心面临的最大安全威胁之一。提取了LDoS攻击流量的3个内在特征，建立基于BP神经网络的LDoS攻击分类器，提出了基于联合特征的LDoS攻击检测方法。该方法将LDoS攻击的3个内在特征组成联合特征作为BP神经网络的输入，通过预先设定的决策指标，达到检测LDoS攻击的目的。采用LDoS攻击流量专用产生工具，在NS2仿真平台和test-bed网络环境中对检测算法进行了测试与验证，实验结果表明通过假设检验得出检测率为 96.68%。与现有研究成果比较说明基于联合特征的LDoS攻击检测性优于单个特征，并具有较高的计算效率。

Abstract

LDoS (low-rate denial of service) attack is a kind of RoQ (reduction of quality) attack which has the characteristics of low average rate and strong concealment.These characteristics pose great threats to the security of cloud computing platform and big data center.Based on network traffic analysis

three intrinsic characteristics of LDoS attack flow were extracted to be a set of input to BP neural network

which is a classifier for LDoS attack detection.Hence

an approach of detecting LDoS attacks was proposed based on novel combined feature value.The proposed approach can speedily and accurately model the LDoS attack flows by the efficient self-organizing learning process of BP neural network

in which a proper decision-making indicator is set to detect LDoS attack in accuracy at the end of output.The proposed detection approach was tested in NS2 platform and verified in test-bed network environment by using the Linux TCP-kernel source code

which is a widely accepted LDoS attack generation tool.The detection probability derived from hypothesis testing is 96.68%.Compared with available researches

analysis results show that the performance of combined features detection is better than that of single feature

and has high computational efficiency.

关键词

Keywords

references

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