Automated Malware Family Classification using Weighted Hierarchical Ensembles of Large Language Models

ArXi:2604.02490v1 Announce Type: cross Malware family classification remains a challenging task in automated malware analysis, particularly in real-world settings characterized by obfuscation, packing, and rapidly evolving threats. Existing machine learning and deep learning approaches typically depend on labeled datasets, handcrafted features, supervised