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

Automatic Library Fuzzing through API Relation Evolvement

Jiayi Lin (The University of Hong Kong), Qingyu Zhang (The University of Hong Kong), Junzhe Li (The University of Hong Kong), Chenxin Sun (The University of Hong Kong), Hao Zhou (The Hong Kong Polytechnic University), Changhua Luo (The University of Hong Kong), Chenxiong Qian (The University of Hong Kong)

Read More

Eclipse Attacks on Monero's Peer-to-Peer Network

Ruisheng Shi (Beijing University of Posts and Telecommunications), Zhiyuan Peng (Beijing University of Posts and Telecommunications), Lina Lan (Beijing University of Posts and Telecommunications), Yulian Ge (Beijing University of Posts and Telecommunications), Peng Liu (Penn State University), Qin Wang (CSIRO Data61), Juan Wang (Wuhan University)

Read More

Vision: Comparison of AI-assisted Policy Development Between Professionals and...

Rishika Thorat (Purdue University), Tatiana Ringenberg (Purdue University)

Read More