Margin-reciprocal loss: enhancing robust network anomaly detection on imbalanced traffic data

Rachid Tahri (Hassan First University)
Abdellah Ouammou (Hassan First University)
Abdellatif Lasbahani (Sultan Moulay Slimane University)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 42, No 2: May 2026

Accurate detection of network intrusions remains challenging under severe class imbalance, where rare attacks such as remote-to-local (R2L) and user-to-root (U2R) are poorly represented. Although many learning-based intrusion detection systems achieve high overall accuracy, conventional loss functions often bias training toward majority classes, leading to weak minority-class performance. This paper introduces a smooth margin-reciprocal loss (MRL), inspired by distance-weighted discrimination (DWD), which emphasizes samples with small or negative margins while rapidly attenuating penalties for well-classified instances. Unlike probability-based focal loss, MRL operates directly on the signed margin and enables stable optimization with first-order methods. Experiments conducted on the NSL-KDD benchmark using linear and shallow multilayer perceptron models show that MRL consistently improves macro-F1 and per-class precision–recall AUC compared with hinge, logistic, and focal losses, with notable gains on minority attack classes.