A deep one-class classifier for network anomaly detection using autoencoders and one-class support vector machines

IntroductionThe integration of deep learning models into Network Intrusion Detection Systems (NIDS) has shown promising advancements in distinguishing normal network traffic from cyber-attacks due to their capability to learn complex non-linear patterns. These approaches typically rely on both benig...

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Principais autores:	Polyzois Bountzis, Dimitris Kavallieros, Theodora Tsikrika, Stefanos Vrochidis, Ioannis Kompatsiaris
Formato:	Artigo
Idioma:	Inglês
Publicado:	Frontiers Media S.A. 2025-10-01
Series:	Frontiers in Computer Science
Assuntos:	anomaly detection autoencoders network intrusion detection one-class support vector machine one-class classification semi-supervised learning
Acceso en liña:	https://www.frontiersin.org/articles/10.3389/fcomp.2025.1646679/full
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