Autoencoder-based Intrusion Detection System
Firuz Kamalov, Rita Zgheib, Ho‐Hon Leung, Ahmed Al-Gindy, Sherif Moussa · 2021 International Conference on Engineering and Emerging Technologies (ICEET) · 2021
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.