A Model to Detect Faked Link in Software Defined Network

Dong LI, Xing WANG, Yong-pu GU

Abstract


This paper establishes a SDN-based network traffic anomaly detection model based on decision tree. Firstly, five statistical indexes are presented to describe the behavior of network traffic, then normal and abnormal traffic data to train the machine learning model to detect traffic anomaly. Four machine learning algorithms, decision tree, k-nearest neighbor, support vector machine and naive Bayes, are used to predict the detection rate, false alarm rate and other indicators. Experiments shows that decision tree algorithm is suitable for detecting traffic anomaly in SDN network.

Keywords


SDN, Network security, Faked link


DOI
10.12783/dtcse/cmsam2018/26527

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