Internet security and technology policy research regularly uses technical indicators of abuse to identify culprits and to tailor mitigation strategies. As a major obstacle, current inferences from abuse data that aim to characterize providers with poor security practices often use a naive normalization of abuse (abuse counts divided by network size) and do not take into account other inherent or structural properties of providers. Even the size estimates are subject to measurement errors relating to attribution, aggregation, and various sources of heterogeneity. More precise indicators are costly to measure at Internet scale. We address these issues for the case of hosting providers with a statistical model of the abuse data generation process, using phishing sites in hosting networks as a case study. We decompose error sources and then estimate key parameters of the model, controlling for heterogeneity in size and business model. We find that 84% of the variation in abuse counts across 45,358 hosting providers can be explained with structural factors alone. Informed by the fitted model, we systematically select and enrich a subset of 105 homogeneous "statistical twins" with additional explanatory variables, unreasonable to collect for all hosting providers. We find that abuse is positively associated with the popularity of websites hosted and with the prevalence of popular content management systems. Moreover, hosting providers who charge higher prices (after controlling for level differences between countries) witness less abuse. These structural factors together explain a further 77% of the remaining variation. This calls into question premature inferences from raw abuse indicators about the security efforts of actors, and suggests the adoption of similar analysis frameworks in all domains where network measurement aims at informing technology policy.
Further, they showed that the structural properties of hosting providers alone -such as different size, price, and business model variables -can explain more than 84% of the variance in abuse concentration of hosting providers REF .
Rotten Apples or Bad Harvest? What We Are Measuring When We Are Measuring Abuse
TOIT
Computer Science