Full Text:   <291>

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CLC number: TP39

On-line Access: 2019-10-08

Received: 2018-07-18

Revision Accepted: 2018-09-14

Crosschecked: 2019-08-23

Cited: 0

Clicked: 805

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhi-jie Fan

http://orcid.org/0000-0002-7011-8632

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.9 P.1195-1208

http://doi.org/10.1631/FITEE.1800436


Discovery method for distributed denial-of-service attack behavior in SDNs using a feature-pattern graph model


Author(s):  Ya Xiao, Zhi-jie Fan, Amiya Nayak, Cheng-xiang Tan

Affiliation(s):  College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China; more

Corresponding email(s):   1710053@tongji.edu.cn, aaronzfan@126.com, nayak@uottawa.ca

Key Words:  Software-defined network, Distributed denial-of-service (DDoS), Behavior discovery, Distance metric learning, Feature-pattern graph


Ya Xiao, Zhi-jie Fan, Amiya Nayak, Cheng-xiang Tan. Discovery method for distributed denial-of-service attack behavior in SDNs using a feature-pattern graph model[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(9): 1195-1208.

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Abstract: 
The security threats to software-defined networks (SDNs) have become a significant problem, generally because of the open framework of SDNs. Among all the threats, distributed denial-of-service (DDoS) attacks can have a devastating impact on the network. We propose a method to discover DDoS attack behaviors in SDNs using a feature-pattern graph model. The feature-pattern graph model presented employs network patterns as nodes and similarity as weighted links; it can demonstrate not only the traffic header information but also the relationships among all the network patterns. The similarity between nodes is modeled by metric learning and the Mahalanobis distance. The proposed method can discover DDoS attacks using a graph-based neighborhood classification method; it is capable of automatically finding unknown attacks and is scalable by inserting new nodes to the graph model via local or global updates. Experiments on two datasets prove the feasibility of the proposed method for attack behavior discovery and graph update tasks, and demonstrate that the graph-based method to discover DDoS attack behaviors substantially outperforms the methods compared herein.

基于特征-模式图的SDN下分布式拒绝服务攻击发现方法

摘要:由于软件定义网络(software-defined networks, SDN)的开方式结构,软件定义网络环境下的安全威胁已成为一个重要问题。在所有威胁中,分布式拒绝服务攻击(distribute ddenial-of-service, DDoS)对网络具有巨大影响。本文提出一种基于特征-模式图模型的方法来发现软件定义网络环境下的DDoS攻击行为。所提出的特征-模式图采用网络模式作为节点,将其相似度作为加权边。该图模型可同时表示网络包的头信息和各网络模式之间的关系信息。节点之间的相似度由度量学习和马氏距离表示。所提方法可以基于图的邻近分类模型发现DDoS攻击,并具有自动发现未知攻击的能力且可通过全局或局部插入新节点的方式扩展已有图结构。两个数据集上的实验证明了所提方法在攻击行为检测和图更新任务上的可行性,并证明了本文基于图的模型在DDoS攻击检测上优于对比模型。

关键词:软件定义网络;分布式拒绝服务攻击;行为检测;距离度量学习;特征-模式图

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