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InfoResearchPeer-reviewed

CECO-LAD: Cloud-Edge Collaboration for Unsupervised Log Anomaly Detection

Source
IEEE Xplore (Security & AI Journals)(opens in a new tab)
Published
Record updated

Summary

CECO-LAD is a cloud-edge framework for unsupervised log anomaly detection that combines a cloud-side ensemble of expectation-maximization-enhanced anomaly transformers (BAT) with a quantized edge model (Q-BAT). A Mahalanobis distance-based routing policy forwards only uncertain samples to the cloud and keeps confident cases at the edge. The authors report F1-scores of 99.98% on HDFS, 99.95% on OpenStack and 99.99% on BGL while reducing edge resource consumption.