Kamalov, FiruzZgheib, RitaLeung, Ho HonAl-Gindy, AhmedMoussa, Sherif2022-05-182022-05-18© 20212021Kamalov, F., Zgheib, R., Leung, H. H., Al-Gindy, A., & Moussa, S. (2021). Autoencoder-based intrusion detection system. 2021 International Conference on Engineering and Emerging Technologies (ICEET). https://doi.org/10.1109/ICEET53442.2021.9659562978-166542714-2https://doi.org/10.1109/ICEET53442.2021.9659562http://hdl.handle.net/20.500.12519/643This conference paper is not available at CUD collection. The version of scholarly record of this paper is published in 2021 International Conference on Engineering and Emerging Technologies (ICEET) (2021), available online at: https://doi.org/10.1109/ICEET53442.2021.9659562Given the dependence of the modern society on networks, the importance of effective intrusion detection systems (IDS) cannot be underestimated. In this paper, we consider an autoencoder-based IDS for detecting distributed denial of service attacks (DDoS). The advantage of autoencoders over traditional machine learning methods is the ability to train on unlabeled data. As a result, autoencoders are well-suited for detecting unknown attacks. The key idea of the proposed approach is that anomalous traffic flows will have higher reconstruction loss which can be used to flag the intrusions. The results of numerical experiments show that the proposed method outperforms benchmark unsupervised algorithms in detecting DDoS attacks. © 2021 IEEE.en-USPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.Anomaly detectionAutoencodersCybersecurityIntrusion detection systemsUnsupervised learningConference PaperCopyright : © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.