Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning
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 binary program clustering (the task of clustering all incoming samples regardless of label) remains largely unexplored. This study presents the first systematic investigation of SSL and TRL methods for binary program clustering, conducted in two phases on the public Ember and Bodmas datasets. In Phase 1, we establish a performance ceiling by adapting prominent vision-based SSL models (BYOL, SimSiam, Barlow Twins, VICReg) for ...
| Authors | Martin Mocko, Daniela Chudá |
|---|---|
| Primary category | cs.CR |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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