56 citations · 138 across the 13 of their papers we have counts for
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cs.CR2021★ 6 cited
Realizable Universal Adversarial Perturbations for Malware
Raphael Labaca-Castro, Luis Muñoz-González, Feargus Pendlebury +3
Machine learning classifiers are vulnerable to adversarial examples -- input-specific perturbations that manipulate models' output. Universal Adversarial Perturbations (UAPs), whic…
cs.CR2021
Identifying Authorship Style in Malicious Binaries: Techniques, Challenges & Datasets
Jason Gray, Daniele Sgandurra, Lorenzo Cavallaro
Attributing a piece of malware to its creator typically requires threat intelligence. Binary attribution increases the level of difficulty as it mostly relies upon the ability to d…