arXiv cs.LG
· Papers
Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning
arXiv:2608.02348v1 Announce Type: cross Abstract: Malware clustering is a critical task in cybersecurity that helps discover threats and analyze evolving malware families. While self-supervised learning (SSL) and tabular representation learning (TRL) have achieved breakthroughs in other domains, their application to bi