56 citations · 57 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 1 cited
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)
Zeliang Kan, Shae McFadden, Daniel Arp +5
Machine learning (ML) plays a pivotal role in detecting malicious software. Despite the high F1-scores reported in numerous studies reaching upwards of 0.99, the issue is not compl…
cs.CR2018★ 56 cited
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time
Feargus Pendlebury, Fabio Pierazzi, Roberto Jordaney +2
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 com…