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SpacePhish: The Evasion-space of Adversarial Attacks against Phishing Website Detectors using Machine Learning. (arXiv:2210.13660v1 [cs.CR])
Oct. 26, 2022, 1:23 a.m. | Giovanni Apruzzese, Mauro Conti, Ying Yuan
cs.CR updates on arXiv.org arxiv.org
Existing literature on adversarial Machine Learning (ML) focuses either on
showing attacks that break every ML model, or defenses that withstand most
attacks. Unfortunately, little consideration is given to the actual
\textit{cost} of the attack or the defense. Moreover, adversarial samples are
often crafted in the "feature-space", making the corresponding evaluations of
questionable value. Simply put, the current situation does not allow to
estimate the actual threat posed by adversarial attacks, leading to a lack of
secure ML systems.
We …
adversarial attacks evasion machine machine learning phishing space website
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