EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability

arXiv:2607. 24177v1 Announce Type: cross Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

• benchmarks
• safety