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.
Security and privacy for low power iot devices on 5g and beyond networks: Challenges and future directions,
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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.