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.

翻译：软件定义网络(SDN) 是改变传统网络架构的下一代。 SDN 是改变互联网网络架构的有希望的解决方案之一。 攻击由于SDN结构的集中性质而变得更加常见。 保护SDN结构至关重要。 在这项研究中, 我们提议在SDN背景下采用网络入侵探测系统深入学习模块( NIDS- DL) 。 我们建议的方法将网络入侵探测系统( NIDS) 与许多类型的深层次学习算法相结合。 我们的方法利用地物选择方法从NSL- KDD数据集的41个特征中提取了12个特征。 我们使用分类师( CNN、 DNN、 RNN、 LSTM 和 GRU ) 。 当我们比较分类师的分数时, 我们的方法产生了分别为98.63%、 98.53%、 98.13%、 98.04% 和 97.78% 的准确性结果。 我们的新方法( NIDS- DL) 使用5个深层学习分类师, 并用预处理数据集来收获最佳的DS。 我们的拟议方法可能意味着在将来的Bin DSDA中成功的分类中采用。