Anomaly and intrusion detection using deep learning for software-defined networks: A survey

Software-Defined Networks (SDN) represent an adaptable paradigm for dealing with network users’ dynamic demands. Confidentiality, integrity, and availability are fundamental pillars for the security of the networks, which are often targeted by cyberattacks. The scientific community has been recently exploring deep learning to implement Network Intrusion Detection Systems (NIDS) against network attacks. In this survey, we aim to present an empirical literature review on state-of-the-art NIDS based on deep learning for defending SDNs. The essential steps to develop such systems are carefully examined: benchmark datasets, data preprocessing, deep learning modeling, hyperparameter tuning, and performance evaluation. There has been a growing trend in published works since 2021, underpinning the importance of the research field, which is still active and under investigation. We support the development of the area by discussing the identified open issues and future research directions. [Display omitted] •Review the latest trends in deep learning-based intrusion detection.•Present the current most relevant benchmark datasets and performance metrics.•Discuss the preprocessing methods commonly applied to the detection system input.•Introduce a deep learning model taxonomy and how their hyperparameters are tuned.•Identify the knowledge gaps in the area, pointing out future research directions.