Empirical Evaluation of Autoencoder Models for Anomaly Detection in Packet-based NIDS
Soumyadeep Hore,
Quoc H. Nguyen,
Yulun Xu
et al.
Abstract:Anomaly detection is critical for network security. Unsupervised learning models trained on benign network traffic data aim to detect anomalies without relying on attack data sets. Autoencoder-based models have emerged as a promising approach for detecting anomalies in network intrusion data. While autoencoder models have predominantly been utilized in flow-based approaches, which are suitable for offline analysis, there is a notable gap in research concerning unsupervised learning, particularly autoencoder-ba… Show more
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