English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Internet security and technology policy research regularly uses technicalindicators of abuse in order to identify culprits and to tailor mitigationstrategies. As a major obstacle, readily available data are often misalignedwith actual information needs. They are subject to measurement errors relatingto observation, aggregation, attribution, and various sources of heterogeneity.More precise indicators such as size estimates are costly to measure atInternet scale. We address these issues for the case of hosting providers witha statistical model of the abuse data generation process, using phishing sitesin hosting networks as a case study. We decompose error sources and thenestimate key parameters of the model, controlling for heterogeneity in size andbusiness model. We find that 84\,\% of the variation in abuse counts across45,358 hosting providers can be explained with structural factors alone.Informed by the fitted model, we systematically select and enrich a subset of105 homogeneous "statistical twins" with additional explanatory variables,unreasonable to collect for \emph{all} hosting providers. We find that abuse ispositively associated with the popularity of websites hosted and with theprevalence of popular content management systems. Moreover, hosting providerswho charge higher prices (after controlling for level differences betweencountries) witness less abuse. These factors together explain a further 77\,\%of the remaining variation, calling into question premature inferences from rawabuse indicators on security efforts of actors, and suggesting the adoption ofsimilar analysis frameworks in all domains where network measurement aims atinforming technology policy.

Rotten Apples or Bad Harvest? What We Are Measuring When We Are Measuring Abuse