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.
@cite_19: Active Internet measurement studies rely on a list of targets to be scanned. While probing the entire IPv4 address space is feasible for scans of limited complexity, more complex scans do not scale to measuring the full Internet. Thus, a sample of the Internet can be used instead, often in form of a "top list". The most widely used list is the Alexa Global Top1M list. Despite their prevalence, use of top lists is seldomly questioned. Little is known about their creation, representativity, potential biases, stability, or overlap between lists. As a result, potential consequences of applying top lists in research are not known. In this study, we aim to open the discussion on top lists by investigating the aptness of frequently used top lists for empirical Internet scans, including stability, correlation, and potential biases of such lists.
The work that is most recent and most closely related to ours is that of Scheitle al @cite_19 , who compared Alexa's, Majestic's and Umbrella's lists on their structure and stability over time, discussed their usage in (Internet measurement) research through a survey of recent studies, calculated the potential impact on their results, and drafted guidelines for using the rankings. We focus on the implications of these lists for security research, expanding the analysis to include representativeness, responsiveness and benignness. Moreover, we are the first to empirically demonstrate the possibility of malicious large-scale manipulation, and propose a concrete solution to these shortcomings by providing researchers with improved and publicly available rankings.