An Efficient Flow Control Approach for SDN-based Network Threat Detection and Migration Using Support Vector Machine

Ping Wang*, Hsiao-Chung Lin, Wen-Hui Lin, Kuo-Ming Chao, Chi-Chun Lo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Most existing approaches for solving the network threat problems focus on the specific security mechanisms, for example, network intrusion detection system (NIDS) detection, firewall configuration, rather than on flow management approaches to defend network threats with an SDN (Software Defined Networking) architecture. Accordingly, this study proposes an improved behaviour-based SVM (support vector machine) with learning algorithm for use in the security monitoring system (SMS) to categorize network threats for network intrusion detection system. The model also adopted the ID3 decision tree theory to outrank raw features and determine the most qualified features to train support vector classifier (SVC) considering the overall detection precision rate of experiments which speeds up the learning of normal and intrusive patterns and and increases the accuracy of detecting intrusion. By using sFlow collector and analyzer associated with sFlow-RT toolset, the experimental results proved that the SMS enables a defender to classify the network threats with defence strategies and defend network threats.
Original languageEnglish
Title of host publication2016 IEEE 13TH INTERNATIONAL CONFERENCE ON E-BUSINESS ENGINEERING (ICEBE)
Pages56-63
DOIs
StatePublished - 2016
Event13th IEEE International Conference on e-Business Engineering (ICEBE) - Macau, China
Duration: 4 Nov 20166 Nov 2016

Publication series

Name2016 IEEE 13TH INTERNATIONAL CONFERENCE ON E-BUSINESS ENGINEERING (ICEBE)

Conference

Conference13th IEEE International Conference on e-Business Engineering (ICEBE)
CountryChina
CityMacau
Period4/11/166/11/16

Keywords

  • Software-defined networking; network threat; Support vector machine; ID3 decision tree; NIDS

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    Wang, P., Lin, H-C., Lin, W-H., Chao, K-M., & Lo, C-C. (2016). An Efficient Flow Control Approach for SDN-based Network Threat Detection and Migration Using Support Vector Machine. In 2016 IEEE 13TH INTERNATIONAL CONFERENCE ON E-BUSINESS ENGINEERING (ICEBE) (pp. 56-63). (2016 IEEE 13TH INTERNATIONAL CONFERENCE ON E-BUSINESS ENGINEERING (ICEBE)). https://doi.org/10.1109/ICEBE.2016.10