Stealing Malware Classifiers and AVs at Low False Positive Conditions. (arXiv:2204.06241v1 [cs.CR])

Model stealing attacks have been successfully used in many machine learning
domains, but there is little understanding of how these attacks work in the
malware detection domain. Malware detection and, in general, security domains
have very strong requirements of low false positive rates (FPR). However, these
requirements are not the primary focus of the existing model stealing
literature. Stealing attacks create surrogate models that perform similarly to
a target model using a limited amount of queries to the target. The …

conditions false positive malware stealing