  Software-Defined Networking (SDN) is the next generation to change the
architecture of traditional networks. SDN is one of the promising solutions to
change the architecture of internet networks. Attacks become more common due to
the centralized nature of SDN architecture. It is vital to provide security for
the SDN. In this study, we propose a Network Intrusion Detection System-Deep
Learning module (NIDS-DL) approach in the context of SDN. Our suggested method
combines Network Intrusion Detection Systems (NIDS) with many types of deep
learning algorithms. Our approach employs 12 features extracted from 41
features in the NSL-KDD dataset using a feature selection method. We employed
classifiers (CNN, DNN, RNN, LSTM, and GRU). When we compare classifier scores,
our technique produced accuracy results of (98.63%, 98.53%, 98.13%, 98.04%, and
97.78%) respectively. The novelty of our new approach (NIDS-DL) uses 5 deep
learning classifiers and made pre-processing dataset to harvests the best
results. Our proposed approach was successful in binary classification and
detecting attacks, implying that our approach (NIDS-DL) might be used with
great efficiency in the future.
