Neural Network-Based Approach for Detection and Mitigation of DDoS Attacks in SDN Environments

Oussama Hannache and Mohamed Chaouki Batouche
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Oussama Hannache: MISC Laboratory, Computer Science Department, University of Constantine 2, Algeria
Mohamed Chaouki Batouche: Information Technology Department, CCIS - RC, Princess Nourah University, Riyadh, Saudi Arabia

International Journal of Information Security and Privacy (IJISP), 2020, vol. 14, issue 3, 50-71

Abstract: Software defined networking (SDN) is a networking paradigm that allows for the easy programmability of network devices by decoupling the data plane and the control plane. On the other hand, Distributed Denial of Service (DDoS) attacks remains one of the major concerns for organizational network infrastructures and Cloud providers. In this article, the authors propose a Neural Network based Traffic Flow Classifier (TFC-NN) for live DDoS detection in SDN environments. This study provides a live traffic analysis method with a neural network. The training of the TFC-NN model is performed by a labelled dataset constructed from SDN normal traffic and an-under DDoS traffic. The study also provides a live mitigation process combined with the live TFC-NN-based DDoS detection. The approach is deployed and evaluated on an SDN architecture based on different performance metrics with different under-DDoS attack scenarios.

Date: 2020
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