38 citations · 38 across the 5 of their papers we have counts for
5 papers
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…
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…
Network Intrusion Datasets: A Survey, Limitations, and Recommendations
Patrik Goldschmidt, Daniela Chudá
Data-driven cyberthreat detection has become a crucial defense technique in modern cybersecurity. Network defense, supported by Network Intrusion Detection Systems (NIDSs), has als…
AI Research is not Magic, it has to be Reproducible and Responsible: Challenges in the AI field from the Perspective of its PhD Students
Andrea Hrckova, Jennifer Renoux, Rafael Tolosana Calasanz +3
With the goal of uncovering the challenges faced by European AI students during their research endeavors, we surveyed 28 AI doctoral candidates from 13 European countries. The outc…
Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detection
Dominik Macko, Patrik Goldschmidt, Peter Pištek +1
Constant evolution and the emergence of new cyberattacks require the development of advanced techniques for defense. This paper aims to measure the impact of a supervised filter (c…