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TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection
The effectiveness of network intrusion detection systems, predominantly based on machine learning, are highly influenced by the dataset they are trained on.
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Anomaly Detection
on TII-SSRC-23
ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection
With a convolutional \ac{AE}, ARCADE automatically builds a profile of the normal traffic using a subset of raw bytes of a few initial packets of network flows so that potential network anomalies and intrusions can be efficiently detected before they cause more damage to the network.
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