Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations

ArXi:2604.22629v1 Announce Type: cross This work proposes a structural approach to concept drift detection in malware classification using decision tree rulesets. Classifiers are trained across temporal windows on the EMBER2024 dataset, and drift is quantified by comparing extracted rule representations using feature importance, prediction agreement, activation stability, and coverage metrics. These metrics are correlated with both accuracy degradation and data distribution shift as complementary drift indicators.