EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability
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 in terms of data used for both training and testing; (ii) do not consider temporal analysis to showcase whether models withstand the passage of time; (iii) avoid security evaluations with adversarial attacks that could highlight their brittleness against content-injection attacks; and (iv) neglect the computational requirements for deployment, risking slow inference on endpoints. For these reasons, we develop EXE-Bench, a comprehensive benchmark of AI-based Windows malware detectors. EXE-Bench assesses performanc...
| Authors | Andrea Ponte, Daniel Gibert, Matous Kozak et al. |
|---|---|
| Primary category | cs.CR |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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