I-D Action: draft-anjum-nmop-anomaly-detection-evaluation-00.txt
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Internet-Draft draft-anjum-nmop-anomaly-detection-evaluation-00.txt is now available. Title: Evaluation Methodology for Machine-Learning-Based Network Anomaly Detection Author: Mateen Ali Anjum Name: draft-anjum-nmop-anomaly-detection-evaluation-00.txt Pages: 13 Dates: 2026-08-17 Abstract: The Network Management Operations (NMOP) working group has adopted documents describing an architecture, an operational lifecycle, and a semantics for network anomaly detection. Those documents direct implementers to minimize false positives and false negatives, but do not define how the accuracy of an anomaly detection implementation is to be measured, compared, or tracked over time. This document describes an evaluation methodology for machine-learning-based anomaly detection systems operating on network and infrastructure telemetry: the metrics to report and their known failure modes, a benchmarking procedure based on controlled fault injection and replay, and the properties a benchmark dataset needs in order to support reproducible, comparable evaluation. The methodology is informational and complements the adopted NMOP anomaly-detection documents. The IETF datatracker status page for this Internet-Draft is: https://datatracker.ietf.org/doc/draft-anjum-nmop-anomaly-detection-evaluation/ There is also an HTMLized version available at: https://datatracker.ietf.org/doc/html/draft-anjum-nmop-anomaly-detection-evaluation-00 Internet-Drafts are also available by rsync at: rsync.ietf.org::internet-drafts _______________________________________________ I-D-Announce mailing list -- [email protected] To unsubscribe send an email to [email protected]