papers
Publications (2)
cs.CR2026
Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning
Martin Mocko, Daniela Chudá
Malware clustering is a critical task in cybersecurity that helps discover threats and analyze evolving malware families. While self-supervised learning (SSL) and tabular represent…
cs.CR2025
Clustering Malware at Scale: A First Full-Benchmark Study
Martin Mocko, Jakub Ševcech, Daniela Chudá
Recent years have shown that malware attacks still happen with high frequency. Malware experts seek to categorize and classify incoming samples to confirm their trustworthiness or…