Deep learning methods in network intrusion detection: A survey and an objective comparison. (1st November 2020) - Gamage, Sunanda; Samarabandu, Jagath
Abstract: The use of deep learning models for the network intrusion detection task has been an active area of research in cybersecurity. Although several excellent surveys cover the growing body of research on this topic, the literature lacks an objective comparison of the different deep learning models within a controlled environment, especially on recent intrusion detection datasets. In this paper, we first introduce a taxonomy of deep learning models in intrusion detection and summarize the research papers on this topic. Then we train and evaluate four key deep learning models - feed-forward neural network, autoencoder, deep belief network and long short-term memory network - for the intrusion classification task on two legacy datasets (KDD 99, NSL-KDD) and two modern datasets (CIC-IDS2017, CIC-IDS2018). Our results suggest that deep feed-forward neural networks yield desirable evaluation metrics on all four datasets in terms of accuracy, F1-score and training and inference time. The results also indicate that two popular semi-supervised learning models, autoencoders and deep belief networks do not perform better than supervised feed-forward neural networks. The implementation and the complete set of results have been released for future use by the research community. Finally, we discuss the issues in the research literature that were revealed in the survey and suggest several potential future directions for research in machine learning methods for intrusion detection. Highlights: Gives a taxonomy and survey of deep learning models for intrusion detection. Evaluates four deep learning models on four intrusion detection datasets. Feed-forward neural networks perform best across all metrics on all datasets. Discusses issues in intrusion detection research and future directions.