Title: Detection and mitigation of attacks in SDN-based IoT network using SVM

Authors: Shailendra Mishra

Addresses: Department of Computer Engineering, Majmaah University, Al Majmaah-11952, Riyadh, Saudi Arabia

Abstract: Adapting Software Defined Networking (SDN) raises many challenges including scalability and security on the Internet of Things (IoT) network. Growing network size increases resulting in the network load in the SDN controller and facing security challenges. Distributed Denial of Service (DDoS) attacks are among the most acute threats in the present scenario. The attack scenario and security of multiple controller networks are simulated and evaluated in this research. Simulation has been conducted in Mininet-SDN emulator, hosting OS was Ubuntu Linux, Wireshark was used for analysing the network traffic, support vector machines were used to classify the traffic flows. DDoS attacks were detected, and mitigation has been done using a support vector machine learning-based approach. The results show that the support vector machine's sensitivity, specificity and accuracy are excellent in the range of 98.7% to 98.8%. Security solutions are fast and effective in mitigating DDoS attacks.

Keywords: SDN; software defined networking; DDoS; distributed denial of service attack; SVM; support vector machine; DDoS mitigation.

DOI: 10.1504/IJCAT.2021.116009

International Journal of Computer Applications in Technology, 2021 Vol.65 No.3, pp.270 - 281

Received: 17 Aug 2020
Accepted: 06 Oct 2020
Published online: 06 Jul 2021 *

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