Mingxuan Liu (Zhongguancun Laboratory; Tsinghua University), Yiming Zhang (Tsinghua University), Xiang Li (Tsinghua University), Chaoyi Lu (Tsinghua University), Baojun Liu (Tsinghua University), Haixin Duan (Tsinghua University; Zhongguancun Laboratory), Xiaofeng Zheng (Institute for Network Sciences and Cyberspace, Tsinghua University; QiAnXin Technology Research Institute & Legendsec Information Technology (Beijing) Inc.)

Domain names are often registered and abused for harmful and illegal Internet activities. To mitigate such threats, as an emerging security service, Protective DNS (PDNS) blocks access to harmful content by proactively offering rewritten DNS responses, which resolve malicious domains to controlled hosts. While it has become an effective tool against cybercrime, given their implementation divergence, little has been done from the security community in understanding the deployment, operational status and security policies of PDNS services.

In this paper, we present a large-scale measurement study of the deployment and security implications of open PDNS services. We first perform empirical analysis over 28 popular PDNS providers and summarize major formats of DNS rewriting policies. Then, powered by the derived rules, we design a methodology that identifies intentional DNS rewriting enforced by open PDNS servers in the wild. Our findings are multi-faceted. On the plus side, the deployment of PDNS is now starting to scale: we identify 17,601 DNS servers (9.1% of all probed) offering such service. For DNS clients, switching from regular DNS to PDNS induces negligible query latency, despite additional steps (e.g., checking against threat intelligence and rewriting DNS response) being required from the server side. However, we also find flaws and vulnerabilities within PDNS implementation, including evasion of blocking policies and denial of service. Through responsible vulnerability disclosure, we have received 12 audit assessment results of high-risk vulnerabilities. Our study calls for proper guidance and best practices for secure PDNS operation.

View More Papers

SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems

Guangke Chen (ShanghaiTech University), Yedi Zhang (National University of Singapore), Fu Song (Institute of Software, Chinese Academy of Sciences; University of Chinese Academy of Sciences)

Read More

HEIR: A Unified Representation for Cross-Scheme Compilation of Fully...

Song Bian (Beihang University), Zian Zhao (Beihang University), Zhou Zhang (Beihang University), Ran Mao (Beihang University), Kohei Suenaga (Kyoto University), Yier Jin (University of Science and Technology of China), Zhenyu Guan (Beihang University), Jianwei Liu (Beihang University)

Read More

Towards Automated Regulation Analysis for Effective Privacy Compliance

Sunil Manandhar (IBM T.J. Watson Research Center), Kapil Singh (IBM T.J. Watson Research Center), Adwait Nadkarni (William & Mary)

Read More