  In this paper we employ quantitative measurements of cognitive vulnerability
triggers in phishing emails to predict the degree of success of an attack. To
achieve this we rely on the cognitive psychology literature and develop an
automated and fully quantitative method based on machine learning and
econometrics to construct a triaging mechanism built around the cognitive
features of a phishing email; we showcase our approach relying on data from the
anti-phishing division of a large financial organization in Europe. Our
evaluation shows empirically that an effective triaging mechanism for phishing
success can be put in place by response teams to effectively prioritize
remediation efforts (e.g. domain takedowns), by first acting on those attacks
that are more likely to collect high response rates from potential victims.
