Two Stage Anomaly Detection for Network Intrusion Detection
Publikation aus Digital, Policies
Forschungsgruppe Cyber Security and Defence
Proceedings of the 7th International Conference on Information Systems Security and Privacy (ICISSP 2021), pages 450-457 DOI: 10.5220/0010233404500457, 2/2021
Network intrusion detection is one of the most import tasks in today’s cyber-security defence applications. Inthe field of unsupervised learning methods, variants of variational autoencoders promise good results. The factthat these methods are very computationally time-consuming is hardly considered in the literature. Therefore,we propose a new two-stage approach combining a fast preprocessing or filtering method with a variationalautoencoder using reconstruction probability. We investigate several types of anomaly detection methodsmainly based on autoencoders to select a pre-filtering method and to evaluate the performance of our concepton two well established datasets.
Keywords: Autoencoder, Deep Learning, Anomaly Detection, Network Intrusion Detection, Variational Autoencoder