Malware detectors trained on one dataset often stumble on another

This report summarizes a study that evaluated ML-based static Windows PE malware detectors across six public datasets and four external test sets, showing high in-distribution performance but substantial drops when models are tested on temporally or distributionally different datasets; it also highlights that training on obfuscated samples can improve detection of those samples yet reduce overall generalization to broader real-world data.