FARM: Few-shot Adaptive Malware Family Classification under Concept Drift

Malware classification models often suffer performance degradation under concept drift due to evolving threat landscapes and the emergence of novel malware families. This paper presents FARM (Few-shot Adaptive Recognition of Malware), a unified framework for detecting and adapting to both covariate drift and label drift in Windows Portable Executable (PE) malware family classification. FARM uses a triplet autoencoder to project samples into a discriminative latent space, enabling unsupervised drift detection through DBSCAN clustering and dynamic thresholding. To enable rapid adaptation, the framework employs a few-shot strategy that can incorporate new classes from only a small number of labeled samples. FARM also supports full retraining when sufficient drifted samples accumulate, allowing longer-term model updating. Experiments on the BenchMFC dataset show that FARM improves classification performance under covariate drift by 5.6%, and achieves an average F1 score of 0.85 on unseen malware families using few-shot adaptation, increasing to 0.94 after retraining. These results indicate that FARM provides an effective approach for drift-aware malware family classification in dynamic environments with limited supervision.