7 citations · 8 across the 5 of their papers we have counts for
5 papers
Windows Malware Detector as a Compound AI System: Trade-Offs in Accuracy, Efficiency, and Adversarial Robustness
Andrea Ponte, Luca Demetrio, Luca Oneto +2
Industrial Windows malware detectors are commonly described as Compound AI Systems composed of multiple heterogeneous components, including rule-based mechanisms as well as machine…
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…
Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detection
Andrea Ponte, Luca Demetrio, Luca Oneto +3
Malware detection increasingly relies on AI systems that integrate signature-based detection with machine learning. However, these components are typically developed and combined i…
Empirical Quantification of Spurious Correlations in Malware Detection
Bianca Perasso, Ludovico Lozza, Andrea Ponte +3
End-to-end deep learning exhibits unmatched performance for detecting malware, but such an achievement is reached by exploiting spurious correlations -- features with high relevanc…
SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines
Andrea Ponte, Dmitrijs Trizna, Luca Demetrio +3
As a result of decades of research, Windows malware detection is approached through a plethora of techniques. However, there is an ongoing mismatch between academia -- which pursue…