Impact of network load for anomaly detection in software-defined networking

Abstract

Software-Defined Networking (SDN) introduces a new network paradigm for separating the control plane and data plane. The control plane manages the packet flow in the data plane of the network. The anomaly detection in the context of SDN is to identify potentially harmful traffic. If an anomaly occurs because of malicious packets in SDN, inspecting the payload of packets is an effective way to recognize abnormal traffic. In this paper, we consider different bandwidths and topologies of the network for the detection of an anomaly in SDN. We also evaluate the performance of the SDN on the same network. We have implemented different tree topologies on OpenFlow controller using Mininet network emulator. We considered OpenFlow messages as a performance metric for evaluating the performance of the network with different tree topologies. © Springer Nature Singapore Pte Ltd. 2020.