CLC number: TP39
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-08-23
Cited: 0
Clicked: 6745
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.
@article{title="Discovery method for distributed denial-of-service attack behavior in SDNs using a feature-pattern graph model",
author="Ya Xiao, Zhi-jie Fan, Amiya Nayak, Cheng-xiang Tan",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="9",
pages="1195-1208",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800436"
}
%0 Journal Article
%T Discovery method for distributed denial-of-service attack behavior in SDNs using a feature-pattern graph model
%A Ya Xiao
%A Zhi-jie Fan
%A Amiya Nayak
%A Cheng-xiang Tan
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 9
%P 1195-1208
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800436
TY - JOUR
T1 - Discovery method for distributed denial-of-service attack behavior in SDNs using a feature-pattern graph model
A1 - Ya Xiao
A1 - Zhi-jie Fan
A1 - Amiya Nayak
A1 - Cheng-xiang Tan
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 9
SP - 1195
EP - 1208
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800436
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.
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