nCMD: Benign-Anchored Feature Selection for Imbalanced Network Intrusion Detection
Feature selection is critical for network intrusion detection systems (NIDS) operating under high-dimensional, highly imbalanced traffic, as found in operational and defense networks. Traditional filter methods rank features using global statistics computed symmetrically across classes and thus fail to capture the asymmetry of intrusion detection, where attacks are best characterized as deviations from dominant benign traffic. We propose benign-anchored Classwise Mean Deviation (nCMD), a lightweight and interpretable method that scores feature relevance based on the deviation of attack-class distributions from the benign-class mean, rather than a globally biased reference. This approach a...
| Authors | Abu Fuad Ahmad, Istiaque Ahmed |
|---|---|
| Primary category | cs.LG |
| Published | 7 June 2026 |
| Last updated at source | 26 September 2026 |
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