Abstract
Software defined networking (SDN) is an approach to network management that enables the network architecture to be centrally and intelligently controlled with the use of software applications. It is an innovative way in which networks are built by separating the control plane of network devices and data plan and thereby making their management easier through centralized control. The design of SDN allows a secured flexible network configurations. The major drawback in SDN is the formation of Distributed Denial of Service (DDoS) attacks. The DDOS introduces flood with large numbers of malicious packets from malicious hosts that consume network resources. However, the trust between the control planes and forwarding planes is the vulnerability that can be exploited by attackers to perform distributed denial of service attacks in an SDN. Also, there is no form of authentication or authorization between the controllers and switches before a flow is passed between the layers. This problem ultimately causes a denial of access to legitimate network users or degrade the performance of the network. The objective of this paper is to develop an approach to detect DDos Attacks in IPv6 Enabled SDNs using the Support Vector Machine (SVM) algorithm. The normal and attack traffic packets generated was 500,000 packets during the test for 20 min. The packets were pre-processed and the SVM was trained with 25% of the normal and the attack data. The SVM achieved an accuracy of 99.69% and a DDoS attacks detection rate of 100%.
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Adeniji, O.D., Adekeye, D.B., Ajagbe, S.A., Adesina, A.O., Oguns, Y.J., Oladipupo, M.A. (2023). Development of DDoS Attack Detection Approach in Software Defined Network Using Support Vector Machine Classifier. In: Ranganathan, G., Bestak, R., Fernando, X. (eds) Pervasive Computing and Social Networking. Lecture Notes in Networks and Systems, vol 475. Springer, Singapore. https://doi.org/10.1007/978-981-19-2840-6_25
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DOI: https://doi.org/10.1007/978-981-19-2840-6_25
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