App-DDoS detection method using partial binary tree based SVM algorithm

Bin ZHANG; Zihao LIU; Shuqin DONG; Lixun LI

doi:10.11959/j.issn.2096-109x.2018020

您当前的位置：

首页 >

文章列表页 >

App-DDoS detection method using partial binary tree based SVM algorithm

Papers | 更新时间：2024-06-03

- App-DDoS detection method using partial binary tree based SVM algorithm
- Chinese Journal of Network and Information Security Vol. 4, Issue 3, Pages: 24-34(2018)
- 作者机构：
  
  1. 信息工程大学，河南郑州 450001
  2. 河南省信息安全重点实验室，河南郑州 450001
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2018020
  CLC： TP393
- Online First：2018-03，
  
  Published：15 March 2018
- 稿件说明：
移动端阅览
Bin ZHANG, Zihao LIU, Shuqin DONG, et al. App-DDoS detection method using partial binary tree based SVM algorithm[J]. Chinese Journal of Network and Information Security, 2018, 4(3): 24-34.
DOI：

Bin ZHANG, Zihao LIU, Shuqin DONG, et al. App-DDoS detection method using partial binary tree based SVM algorithm[J]. Chinese Journal of Network and Information Security, 2018, 4(3): 24-34. DOI： 10.11959/j.issn.2096-109x.2018020.

摘要

针对基于流量特征的应用层DDoS检测方法侧重于检测持续型应用层DDoS攻击，而忽略检测上升型与脉冲型应用层 DDoS 攻击的问题，提出一种综合检测多类型应用层 DDoS 攻击的方法。首先通过 Hash函数及开放定址防碰撞方法，对多周期内不同源IP地址建立索引，进而实现HTTP GET数的快速统计功能，以支持对刻画数据规模、流量趋势及源 IP 地址分布差异所需特征参数的实时计算；然后采用偏二叉树结构组合SVM分类器分层训练特征参数，并结合遍历与反馈学习的方法，提出基于偏二叉树SVM多分类算法的应用层DDoS检测方法，快速区分出非突发正常流量、突发正常流量及多类型App-DDoS流量。实验表明，所提算法通过划分检测类型、逐层训练检测模型，与传统基于SVM、Navie Bayes的检测方法相比，具有更高的检测率与更低的误检率，且能有效区分出具体攻击类型。

Abstract

As it ignored the detection of ramp-up and pulsing type of application layer DDoS (App-DDoS) attacks in existing flow-based App-DDoS detection methods

an effective detection method for multi-type App-DDoS was proposed.Firstly

in order to fast count the number of HTTP GET for users and further support the calculation of feature parameters applied in detection method

the indexes of source IP address in multiple time windows were constructed by the approach of Hash function.Then the feature parameters by combining SVM classifiers with the structure of partial binary tree were trained hierarchically

and the App-DDoS detection method was proposed with the idea of traversing binary tree and feedback learning to distinguish non-burst normal flow

burst normal flow and multi-type App-DDoS flows.The experimental results show that compared with the conventional SVM-based and naïve-Bayes-based detection methods

the proposed method has more excellent detection performance and can distinguish specific App-DDoS types through subdividing attack types and training detection model layer by layer.

关键词

Keywords

references

陈飞 , 毕小红 , 王晶晶 , 等 . DDoS攻击防御技术发展综述 [J ] . 网络与信息安全学报 , 2017 , 3 ( 10 ): 16 - 24 .

CHEN F , BI X H , WANG J J , et al . Survey of DDoS defense:challenges and directions [J ] . Chinese Journal of Network and Information Security , 2017 , 3 ( 10 ): 16 - 24 .

SINGH K , SINGH P , KUMAR K . Application layer HTTP-GET flood DDoS attacks:research landscape and challenges [J ] . Computer ＆ Security , 2016 , 65 : 344 - 372 .

李锦玲 , 王斌强 . 基于最大频繁序列模式挖掘的 App-DDoS 攻击的异常检测 [J ] . 电子与信息学报 , 2013 , 35 ( 7 ): 1739 - 1745 .

LI J L , WANG B Q . Detecting App-DDoS attacks based on maximal frequent sequential pattern mining [J ] . Journal of Electronics ＆Information Technology , 2013 , 35 ( 7 ): 1739 - 1745 .

SANGJAE L , GISUNG K , SEHUM K . Sequence-order- independent network profiling for detecting application layer DDoS attacks [J ] . Eurasip Journal on Wireless Communication ＆ Networking , 2011 , 50 ( 1 ): 1 - 9 .

杨宏宇 , 常媛 . 基于 K 均值多重主成分分析的 App-DDoS 检测方法 [J ] . 通信学报 , 2014 , 35 ( 5 ): 16 - 24 .

YANG H Y , CHANG Y . App-DDoS detection method based on K-means multiple principal component analysis [J ] . Journal on Communications , 2014 , 35 ( 5 ): 16 - 24 .

YU S , THAPNGAM T , LIU J , et al . Discriminating DDoS flows from flash crowds using information distance [C ] // IEEE The 3rd International Conference on Network and System Security . 2009 : 351 - 356 .

LI K , ZHOU W L , LI P , et al . Distinguishing DDoS attacks from flash crowds using probability metrics [C ] // IEEE The 3rd International Conference on Network and System Security . 2009 : 9 - 17 .

YU S , ZHOU W L , JIA W J , et al . Discriminating DDoS attacks from flash crowds using flow correlation coefficient [J ] . IEEE Transactions on Parallel and Distributed Systems , 2012 , 23 ( 6 ): 1073 - 1080 .

SINGH KJ , THONGAM K , DE T . Entropy-based application layer DDoS attack detection using artificial neural networks [J ] .Entropy,2016:18(10),350-367. Entropy , 2016 , 18 ( 10 ): 350 - 367 .

NI T G , GUX Q , WANG H Y , et al . Real-Time detection of applica tion-layer DDoS attack using time series analysis [J ] . Journal of Control Science and Engineering , 2013 ( 5 ): 1 - 6 .

IRFAN S , AMIT M , VIBHAKAR M . Machine learning techniques used for the detection and analysis of morden types of DDoS attacks [J ] . International Research Journal of Engineering and Technology , 2017 , 4 ( 6 ): 16 - 24 .

LIU L , JIN X L , MIN G Y , et al . Anomaly diagnosis based on regression and classification analysis of statistical traffic features [J ] . Security and Communication Networks , 2014 , 7 : 1372 - 1383 .

PAL R , KUMAR S , SHARMA R L . A detailed classification of flash events:client,server and network characteristics [C ] // The International Conference on Computer Science ＆ Service System . 2012 : 960 - 963 .

杨新武 , 马壮 , 袁顺 . 基于弱分类器调整的多分类 Adaboost 算法 [J ] . 电子与信息学报 , 2016 , 38 ( 2 ): 373 - 380 .

YANG X W , MA Z , YUAN S . Multi-classification adaboost algorithm based on weak classifier adjustment [J ] . Journal of Electronics＆ Information Technology , 2016 , 38 ( 2 ): 373 - 380 .

KANGS , CHOS Z , PILSUNG K . Constructing a multi-class classifier using one-against-one approach with different binary classifier [J ] . Neurocomputing , 2015 , 149 : 677 - 682 .

SILVA C , RIBEIRO B . Multiclass ensemble of one-against-all SVM classifiers [C ] // The International Symposium on Neural Networks . 2016 : 531 - 539 .

JINDAL A , DUA A , KAUR K , et al . Decision tree and SVM-based data analytics for theft detection in smart grid [J ] . IEEE Transactions on Industrial Informatics , 2016 , 12 ( 3 ): 1005 - 1016 .

CORTES C , VAPNIK V . Support vector networks [J ] . Machine Learning , 1995 , 20 : 273 - 297 .

ITA . WorldCup98 [DB/OL ] . http://ita.ee.lbl.gov/html/contrib/World Cup.html http://ita.ee.lbl.gov/html/contrib/World Cup.html .

Views

1965

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰