Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation

V. Pochat, Tom van Goethem, Samaneh Tajalizadehkhoob, Maciej Korczyński, W. Joosen
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引用次数: 423

Abstract

In order to evaluate the prevalence of security and privacy practices on a representative sample of the Web, researchers rely on website popularity rankings such as the Alexa list. While the validity and representativeness of these rankings are rarely questioned, our findings show the contrary: we show for four main rankings how their inherent properties (similarity, stability, representativeness, responsiveness and benignness) affect their composition and therefore potentially skew the conclusions made in studies. Moreover, we find that it is trivial for an adversary to manipulate the composition of these lists. We are the first to empirically validate that the ranks of domains in each of the lists are easily altered, in the case of Alexa through as little as a single HTTP request. This allows adversaries to manipulate rankings on a large scale and insert malicious domains into whitelists or bend the outcome of research studies to their will. To overcome the limitations of such rankings, we propose improvements to reduce the fluctuations in list composition and guarantee better defenses against manipulation. To allow the research community to work with reliable and reproducible rankings, we provide Tranco, an improved ranking that we offer through an online service available at this https URL.
Tranco:一个以研究为导向的顶级网站排名,加强了对操纵的防范
为了评估网络代表性样本中安全和隐私实践的普遍程度,研究人员依赖于网站人气排名,如Alexa列表。虽然这些排名的有效性和代表性很少受到质疑,但我们的研究结果却恰恰相反:我们展示了四个主要排名的内在属性(相似性、稳定性、代表性、响应性和亲和性)如何影响它们的构成,从而可能歪曲研究得出的结论。此外,我们发现对手操纵这些列表的组成是微不足道的。我们是第一个经验验证,在每个列表的域名的行列很容易改变,在Alexa的情况下,通过一个单一的HTTP请求。这使得对手可以大规模地操纵排名,将恶意域名插入白名单,或者根据自己的意愿扭曲研究结果。为了克服这种排名的局限性,我们提出了改进措施,以减少列表组成的波动,并保证更好地防御操纵。为了使研究界能够进行可靠和可重复的排名,我们提供了Tranco,这是我们通过以下https URL提供的在线服务提供的改进排名。
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