SEED: Semi-supervised Continual MalwarE Detection for Tackling ConcEpt Drift on a BuDget

ArXi:2605.24903v1 Announce Type: cross Machine learning based malware detectors become obsolete over time due to concept drift in benign and malware applications. Recent methods rely on fully labeled data and use hierarchical contrastive loss (HCL) with active learning to improve robustness against drift by exploiting semantic structure in malware representations. However, obtaining labeled data in the security domain is difficult.