BARS: Benign-Anchored Ranking and Selection for False Alarm Reduction in Network Intrusion Detection

False alarms remain a major barrier to deploying network intrusion detection systems (NIDS). In high-volume environments, even a sub-1% false positive rate can generate tens of thousands of daily alerts. Filter-based feature selection is attractive because it operates upstream of the classifier and adds no inference-time cost. However, classical filters use class-symmetric criteria that ignore the asymmetry of intrusion detection, where benign traffic defines the baseline and attacks are deviations from it. A recent class-asymmetric filter, Classwise Mean Deviation (CMD), addresses this issue but anchors its score to a global mean that shifts toward attack distributions under class imbala...

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