Abstract
Software-defined networking (SDN) is an advanced networking technology that yields flexibility with cost-efficiency as per the business requirements. SDN breaks the vertical integration of control and data plane and promotes centralized network management. SDN allows data intensive applications to work more efficiently by making the network dynamically configurable. With the growing development of SDN technology, the issue of security becomes critical because of its architectural characteristics. Currently, Distributed denial of service (DDoS) is one of the most powerful attacks that cause the services to be unavailable for normal users. DDoS seeks to consume the resources of the SDN controller with the intention to slow down working of the network. In this paper, a detailed analysis of the effect of spoofed and non-spoofed TCP-SYN flooding attacks on the controller resources in SDN is presented. We also suggest a machine learning based intrusion detection system. Five different classification models belong to a variety of families are used to classify the traffic, and evaluated using different performance indicators. Cross-validation technique is used to validate the classification models. This work enables better features to be extracted and classify the traffic efficiently. The experimental results reveal significantly good performance with all the considered classification models.
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