AI RESEARCH
Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly Detector
arXiv CS.LG
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ArXi:2410.22967v5 Announce Type: replace The widespread usage of the Internet of Things (IoT) has raised the risks of cyber threats; thus, developing Anomaly Detection Systems (ADSs) that can adapt to evolving traffic pattern is critical. Previous studies primarily focused on offline unsupervised learning methods to safeguard ADSs, which is not applicable in practical real-world applications. In this paper, we design Adaptive NAD, an online and self-Adaptive unsupervised Network Anomaly Detection framework for security domains.