Statistical Ranking: A Voting-Based Ensemble Approach to Feature Selection in Android Malware Detection - Silva, Anna Luiza Gomes da; Oliveira, Lucas Ferreira Areias de; Diniz, Angelo; Kreutz, Diego; Schmidt, Dionatan R.; Mansilha, Rodrigo; Paim, Kayuã Oleques (Anais do Simpósio Brasileiro de Cibersegurança (SBSeg), 2026)
High-dimensional Android malware datasets inflate training time and damage model generalization. We propose Statistical Ranking, an ensemble feature selection framework that enforces multi-perspective consensus: a feature is retained only when ranked in the top-k by at least two of three independent criteria (χ2, Mutual Information, and Random Forest importance), under a percriterion budget that scales with the original dimensionality. Across 11 public datasets under stratified 5-fold cross-validation, the method removes 89.8%–99.6% of the feature space with a median recall drop of 0.04, retaining recall ≥ 0.85 in 9 of 11 environments. An ablation over ranking criteria and voting thresholds shows that the ≥ 2-of-3 consensus maximizes the reduction, recall trade-off, and that the contribution of a third ranker depends on the redundancy between the marginal criteria, which varies with feature modality. On MH100K, 24,833 features are reduced to 93 (99.6%) at a cost of 6.5% in F1-score, and the one-off cost of selection is recovered after roughly eight retraining cycles, yielding a 27% reduction in total cost over a ten-update horizon.