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Flow-Based Anomaly Detection Using BNN for Attack Mitigation on SDN
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Author(s): Nang May Phu Lwin (University of Computer Studies, Mandalay, Myanmar)and Su Thawda Win (University of Computer Studies, Mandalay, Myanmar)
Copyright: 2022
Volume: 9
Issue: 1
Pages: 17
Source title:
International Journal of Smart Security Technologies (IJSST)
Editor(s)-in-Chief: Yassine Maleh (Sultan Moulay Slimane University, Morocco)and Ahmed A. Abd EI-Latif (Faculty of Science, Menoufia University, Shebin El-Koom, Egypt)
DOI: 10.4018/IJSST.304072
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Abstract
Distributed Denial of Service (DDoS) attack remains one of the major issues that compromises the resources and services of the components in Software Defined Networks (SDN) environments. The implementation of intrusion prevention system (IPS) in OpenFlow-based SDN architecture has emerged to strengthen the security mechanisms by exploiting the concepts of SDN and OpenFlow protocols. This article provides the anomaly detection of the live traffic flow with Backpropagation Neural Network (BNN) for the online detection and mitigation of DDoS attacks. The dataset from the testbed is used to emulate the efficiency of the proposed method. The results achieve more than 90% detection accuracy with less than 6% false alarm rate. CUP utilization on the centralized controller is also measured by means of SYN and UDP flooding to calculate the effect of malicious traffic on the resources of the system.
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