ACORN-IDS: Adaptive Continual Novelty Detection for Intrusion Detection Systems

Intrusion Detection Systems IDS must maintain reliable detection performance under rapidly evolving benign traffic patterns and the continual emergence of cyberattacks, including zero-day threats with no labeled data available. However, most machine learning-based IDS approaches either assume...

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection

The Internet of Things IoT, with its high degree of interconnectivity and limited computational resources, is particularly vulnerable to a wide range of cyber threats. Intrusion detection systems IDS have been extensively studied to enhance IoT security, and machine learning-based IDS ML-IDS show...