A containerised approach to labelled C&C traffic

A containerised approach to labelled C&C traffic

A challenge for data-driven methods for intrusion detection is the availability of high quality and realistic data, with ground truth at suitable level of granularity to train machine learning models. Here, we explore a container-based approach for simulating and labelling C&C traffic of real ma...

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Hauptverfasser: Asprusten, Markus Leira, Gjerstad, Julie Lidahl, Grov, Gudmund, Kjellstadli, Espen Hammer, Flood, Robert, Clausen, Henry, Aspinall, David
Format: Artikel
Sprache:nor
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Zusammenfassung:A challenge for data-driven methods for intrusion detection is the availability of high quality and realistic data, with ground truth at suitable level of granularity to train machine learning models. Here, we explore a container-based approach for simulating and labelling C&C traffic of real malware through a proof-of-concept implementation.
ISSN:1893-6563