  Software-defined networking (SDN) is a new paradigm that allows developing
more flexible network applications. SDN controller, which represents a
centralized controlling point, is responsible for running various network
applications as well as maintaining different network services and
functionalities. Choosing an efficient intrusion detection system helps in
reducing the overhead of the running controller and creates a more secure
network. In this study, we investigate the performance of the well-known
anomaly-based intrusion detection approaches in terms of accuracy, false alarm
rate, precision, recall, f1-measure, area under ROC curve, execution time and
Mc Nemar's test. Precisely, we focus on supervised machine-learning approaches
where we use the following classifiers: Decision Trees (DT), Extreme Learning
Machine (ELM), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Neural
Networks (NN), Support Vector Machines (SVM), Random Forest (RT), K
Nearest-Neighbour (KNN), AdaBoost, RUSBoost, LogitBoost and BaggingTrees where
we employ the well-known NSL-KDD benchmark dataset to compare the performance
of each one of these classifiers.
