| Summary: | We propose and evaluate a technique that learns the probability of a network transmission link experiencing a fault by using outlier flows (in the performance sense) as training data. This technique autonomously determines the most likely links causing performance degradation in a communications network; a critical feature of zero-touch network management. Our new Network Link Outlier Factor (NLOF) with most likely links (NLOF:MLL) is experimentally compared to the existing literature (including our original NLOF) using classification performance measures: recall, precision, <inline-formula> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula>-score, and time-to-detection. We utilize inferential statistics and a wide set of Mininet experiments to determine statistically significant performance differences. We find that our NLOF:MLL outperforms the existing literature wrt the important <inline-formula> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula>-score while exhibiting a competitive time-to-detection.
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