CITADEL combines self-supervised masked autoencoders with KL-divergence-based memory selection and a hierarchical buffer to detect IoT intrusions without attack labels while retaining old knowledge.
Online self-supervised deep learning for in- trusion detection systems,
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CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection
CITADEL combines self-supervised masked autoencoders with KL-divergence-based memory selection and a hierarchical buffer to detect IoT intrusions without attack labels while retaining old knowledge.