24 citations · 35 across the 7 of their papers we have counts for
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cs.LG2024★ 1 cited
On the Effectiveness of Adversarial Training on Malware Classifiers
Hamid Bostani, Jacopo Cortellazzi, Daniel Arp +3
Adversarial Training (AT) is a key defense against Machine Learning evasion attacks, but its effectiveness for real-world malware detection remains poorly understood. This uncertai…
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…