Boladji Vinny Adjibi (Georgia Tech), Athanasios Avgetidis (Georgia Tech), Manos Antonakakis (Georgia Tech), Michael Bailey (Georgia Tech), Fabian Monrose (Georgia Tech)

Using orthographic, phonetic, and semantic models, we study the prevalence of defensive registrations related to a wide spectrum of transformations of the base domain names of Fortune 500 companies. As part of a large-scale evaluation, we explore several questions aimed at (a) understanding whether there are explainable factors (e.g., the size of the company's security team or its domain name's popularity rank) that correlate with a company's level of engagement regarding defensive registrations; (b) identifying the main actors in the defensive registration ecosystem that Fortune 500 companies rely upon; (c) uncovering the strategies used by these actors, and d) assessing the efficacy of those strategies from the perspective of queries emanating from a large Internet Service Provider (ISP).

Overall, we identified 19,523 domain names defensively registered by 447 Fortune 500 companies. These companies engage in defensive registrations sparingly, with almost 200 companies having fewer than ten defensive registrations. By analyzing the registrations, we found many similarities between the types of domain names the companies registered. For instance, they all registered many TLD-squatting domain names. As it turns out, those similarities are due to the companies' reliance on online brand protection (OBP) service providers to protect their brands. Our analysis of the efficacy of the strategies of those OBPs showed that they register domain names that receive most of the potential squatting traffic. Using regression models, we learned from those strategies to provide recommendations for future defensive registrants. Our measurement also revealed many domain names that received high proportions of traffic over long periods of time and could be registered for only 15 USD. To prevent the abusive use of such domain names, we recommend that OBP providers proactively leverage passive DNS data to identify and preemptively register highly queried available domain names.

View More Papers

Safety Misalignment Against Large Language Models

Yichen Gong (Tsinghua University), Delong Ran (Tsinghua University), Xinlei He (Hong Kong University of Science and Technology (Guangzhou)), Tianshuo Cong (Tsinghua University), Anyu Wang (Tsinghua University), Xiaoyun Wang (Tsinghua University)

Read More

Impact Tracing: Identifying the Culprit of Misinformation in Encrypted...

Zhongming Wang (Chongqing University), Tao Xiang (Chongqing University), Xiaoguo Li (Chongqing University), Biwen Chen (Chongqing University), Guomin Yang (Singapore Management University), Chuan Ma (Chongqing University), Robert H. Deng (Singapore Management University)

Read More

Modeling End-User Affective Discomfort With Mobile App Permissions Across...

Yuxi Wu (Georgia Institute of Technology and Northeastern University), Jacob Logas (Georgia Institute of Technology), Devansh Ponda (Georgia Institute of Technology), Julia Haines (Google), Jiaming Li (Google), Jeffrey Nichols (Apple), W. Keith Edwards (Georgia Institute of Technology), Sauvik Das (Carnegie Mellon University)

Read More

DeFiIntel: A Dataset Bridging On-Chain and Off-Chain Data for...

Iori Suzuki (Graduate School of Environment and Information Sciences, Yokohama National University), Yin Minn Pa Pa (Institute of Advanced Sciences, Yokohama National University), Nguyen Thi Van Anh (Institute of Advanced Sciences, Yokohama National University), Katsunari Yoshioka (Graduate School of Environment and Information Sciences, Yokohama National University)

Read More