Intrusion Detection System using Transformer Encoder and CNN-BiLSTM in Software-Defined Networks
El Youssofi Chaymae, Abdellatif Kobbane, Chougdali Khalid, Ben Othman Jalel · 2024
The emergence of Software-defined Networks (SDN) has played a significant role in shaping the future of networking technologies. SDN aims to improve the flexibility, efficiency, and scalability of traditional networks by centralizing network control, allowing network administrators to manage and control the network through software applications. However, the flexibility provided by SDN architecture unveils numerous emerging network security concerns that require more attention to enhance SDN network security. So, in this paper, we propose a innovative intrusion detection system (IDS) for SDN using a hybrid model that combines CNN-BiLSTM and Transformer Encoder. The proposed approach was tested using the NSL-KDD and CICIDS2017 datasets, achieving accuracy rates of 99.7% and 99.68% respectively. The obtained results demonstrate that our proposed deep learning-based approach provides a robust security solution for detecting intrusions in SDN environments.