Name
Abstract | ||
|---|---|---|
Domain top lists serve as critical resources for the Internet measurement, security, and privacy research communities. Hundreds of prior research studies have used these lists as a set of supposedly popular domains to investigate. However, existing top lists exhibit numerous issues, including a lack of transparency into the list data sources and construction methods, high volatility, and easy ranking manipulation. Despite these flaws, these top lists remain widely used today due to a lack of suitable alternatives. In this paper, we systematically explore the construction of a domain top list from scratch. Using an extensive passive DNS dataset, we investigate different top list design considerations. As a product of our exploration, we produce a voting-based domain ranking method where we quantify the domain preferences of individual IP addresses, and then determine a global ranking across addresses through a voting mechanism. We empirically evaluate our top list design, demonstrating that it achieves better stability and manipulation resistance than existing top lists, while serving as an open and transparent ranking method that other researchers can use or adapt. |
Year | Venue | DocType |
|---|---|---|
2022 | PROCEEDINGS OF THE 31ST USENIX SECURITY SYMPOSIUM | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qinge Xie | 1 | 0 | 0.34 |
| Shujun Tang | 2 | 0 | 0.34 |
| Xiaofeng Zheng | 3 | 0 | 0.34 |
| Qingran Lin | 4 | 0 | 0.34 |
| Baojun Liu | 5 | 0 | 0.34 |
| Haixin Duan | 6 | 237 | 36.86 |
| Frank Li | 7 | 0 | 0.34 |