  Software-Defined Networks (SDN) are the standard architecture for network
deployment. Intrusion Detection Systems (IDS) are a pivotal part of this
technology as networks become more vulnerable to new and sophisticated attacks.
Machine Learning (ML)-based IDS are increasingly seen as the most effective
approach to handle this issue. However, IDS datasets suffer from high class
imbalance, which impacts the performance of standard ML models. We propose
Sequential Binary Classification (SBC) - an algorithm for multi-class
classification to address this issue. SBC is a hierarchical cascade of base
classifiers, each of which can be modelled on any general binary classifier.
Extensive experiments are reported on benchmark datasets that evaluate the
performance of SBC under different scenarios.
