3 papers
cs.CR2026
EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability
Andrea Ponte, Daniel Gibert, Matous Kozak +5
Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since existing evaluations (i) differ i…
cs.CR2025
ByteShield: Adversarially Robust End-to-End Malware Detection through Byte Masking
Daniel Gibert, Felip Manyà
Research has proven that end-to-end malware detectors are vulnerable to adversarial attacks. In response, the research community has proposed defenses based on randomized and (de)r…
cs.CR2024
Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems
Daniel Gibert, Nikolaos Totosis, Constantinos Patsakis +2
The proliferation of malware, particularly through the use of packing, presents a significant challenge to static analysis and signature-based malware detection techniques. The app…