Fig. 3
Confusion matrix for CCCS-CIC-AndMal-2020 dataset using 51 CatBoost-selected features. Left: Raw classification counts showing the distribution of predictions across 15 malware families. Right: Normalized confusion matrix (row-wise) representing per-class recall values. The strong diagonal dominance indicates high classification accuracy across all classes.
