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

IntroductionMost 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, detectio