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The '''[[Statistical Analysis of DNS Abuse in gTLDs Final Report]]''' was released on 08/25/2017 and measured rates of common forms of abusive activities in the [[DNS|Domain Name System]]. It was commissioned by the Competition, Consumer Trust, and Consumer Choice Review Team with the support of ICANN. Maciej Korczy, Maarten Wullink, Samaneh Tajalizadehkhoob, Giovane Moura, and Cristian Hesselman authored the study. The study combines data sets from zone files, domain WHOIS information, ICANN active measurements, and 11 [[RBL]] representing [[malware]], [[phishing]], and spam. The results of the study indicate that abuse counts primarily correlate with stricter registration policies. This report offers a comprehensive descriptive statistical comparison of rates of DNS abuse in new and legacy gTLDs as they pertain to spam, phishing, and malware distribution. Using regression modeling, this performance of inferential statistical analysis also tested the correlation between passively and actively measured properties of new gTLDs as predictors of rates of abuse. This report also analyzed proportions of abusive domains across other relevant to abusive practices players, such as registrars and privacy/proxy service providers.

The '''[[Statistical Analysis of DNS Abuse in gTLDs Final Report]]''' was released in August 2017 and measured rates of common forms of abusive activities in the [[DNS|Domain Name System]]. It was commissioned by the Competition, Consumer Trust, and Consumer Choice Review Team with the support of ICANN. Maciej Korczy, Maarten Wullink, Samaneh Tajalizadehkhoob, Giovane Moura, and Cristian Hesselman authored the study. The study combines data sets from zone files, domain WHOIS information, ICANN active measurements, and 11 [[RBL]] representing [[malware]], [[phishing]], and spam. The results of the study indicate that abuse counts primarily correlate with stricter registration policies. This report offers a comprehensive descriptive statistical comparison of rates of DNS abuse in new and legacy gTLDs as they pertain to spam, phishing, and malware distribution. Using regression modeling, this performance of inferential statistical analysis also tested the correlation between passively and actively measured properties of new gTLDs as predictors of rates of abuse. This report also analyzed proportions of abusive domains across other relevant to abusive practices players, such as registrars and privacy/proxy service providers.

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