1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.LG2024
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.CR2022★ 1 cited
Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers
Limin Yang, Zhi Chen, Jacopo Cortellazzi +5
Malware classifiers are subject to training-time exploitation due to the need to regularly retrain using samples collected from the wild. Recent work has demonstrated the feasibili…