An integrated evolution-aware meta-learning framework with adversarial morphological augmentation for zero-day threat detections.

Introduction: Most modern threat detection frameworks rely on fixed class definitions and retrospective signatures derived from historical data, limiting their ability to adapt to evolving attack behaviors. However, contemporary threats are increasingly polymorphic, adaptive, and include a growing number of zero-day attacks, making traditional pattern-matching approaches insufficient. With expanding attack surfaces across cloud environments, IoT systems, and heterogeneous infrastructures, detection systems must move beyond static similarity matching and instead account for the evolutionary nature of threats. Despite advances, both deep learning-based and traditional anomaly detection approaches face critical limitations. Deep learning models often suffer from data scarcity, concept drift, poor uncertainty calibration, and limited causal interpretability, while conventional anomaly detection methods exhibit slow training, weak generalization, and reduced adaptability to novel attack patterns. Methods: To address these challenges, we propose M Results and discussion: Overall, M