{TESSERACT}: Eliminating Experimental Bias in Malware Classification across Space and Time
USENIX Security Symposium
Pages: 729 - 746
Published: Aug 14, 2019
Abstract
Is Android malware classification a solved problem? Published F1 scores of up to 0.99 appear to leave very little room for improvement. In this paper, we argue that results are commonly inflated due to two pervasive sources of experimental bias: spatial bias caused by distributions of training and testing data that are not representative of a real-world deployment; and temporal bias caused by incorrect time splits of training and testing sets,...
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