The popularity of Software Defined Networks (SDNs) has grown in recent years, mainly because of their ability to simplify network management and improve network flexibility. However, this also makes them vulnerable to various types of cyber attacks. SDNs work on a centralized control plane which makes them more prone to network attacks. Research has demonstrated that deep learning (DL) methods can be successful in identifying intrusions in conventional networks, but their application in SDNs is still an open research area. In this research, we propose the use of DL techniques for intrusion detection in SDNs. We measure the effectiveness of our method by experimentation on a dataset of network traffic and comparing it to existing techniques. Our results show that the DL-based approach outperforms traditional methods in terms of detection accuracy and computational efficiency. The deep learning architecture that has been used in this research is a Long Short Term Memory Network and Self-Attention based architecture i.e. LSTM-Attn which achieves an Fl-score of 0.9721. Furthermore, this technique can be trained to detect new attack patterns and improve the overall security of SDNs.

翻译：近年来，软件定义网络（SDNs）因其简化网络管理与提升网络灵活性的能力而日益普及。然而，这也使其易受各类网络攻击。SDNs基于集中式控制平面运行，使其更易遭受网络攻击。研究表明，深度学习（DL）方法在传统网络入侵检测中成效显著，但其在SDNs中的应用仍是开放的研究领域。本研究提出采用深度学习技术进行SDNs入侵检测。我们通过对网络流量数据集的实验评估方法效能，并与现有技术进行对比。结果表明，基于深度学习的方法在检测精度与计算效率方面均优于传统方法。本研究采用的深度学习架构为基于长短期记忆网络与自注意力机制的LSTM-Attn架构，其F1分数达到0.9721。此外，该技术可通过训练检测新型攻击模式，从而提升SDNs的整体安全性。