Autoencoder-based Intrusion Detection System

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Institute of Electrical and Electronics Engineers Inc.
Given 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.
This 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:
Anomaly detection, Autoencoders, Cybersecurity, Intrusion detection systems, Unsupervised learning
Kamalov, 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).