Athanasios Kountouras (Georgia Institute of Technology), Panagiotis Kintis (Georgia Institute of Technology), Athanasios Avgetidis (Georgia Institute of Technology), Thomas Papastergiou (Georgia Institute of Technology), Charles Lever (Georgia Institute of Technology), Michalis Polychronakis (Stony Brook University), Manos Antonakakis (Georgia Institute of Technology)

The Domain Name System (DNS) is fundamental to communication on the Internet. Therefore, any proposed changes or extensions to DNS can have profound consequences on network communications. In this paper, we explore the implications of a recent extension to DNS called EDNS Client Subnet (ECS). This extension extends the visibility of client information to more domain operators by providing a prefix of a client’s IP address to DNS nameservers above the recursive nameserver. This raises numerous questions about the impact of such changes on network communications that rely on DNS.

In this paper, we present the results of a longitudinal study that measures the deployment of ECS using several DNS vantage points. We show that, despite being an optional extension, ECS has seen steady adoption over time—even for sites that do not benefit from its use. Additionally, we observe that the client subnet provided by ECS may provide less privacy than originally thought, with most subnets corresponding to a /24 CIDR or smaller. Lastly, we observe several positive and negative consequences resulting from the introduction of DNS. For example, DNS can help aid security efforts when analyzing DNS data above the recursive due to the addition of client network information. However, that same client information has the potential to exacerbate existing security issues like DNS leakage. Ultimately, this paper discusses how small changes to fundamental protocols can result in unintended consequences that can be both positive and negative.

View More Papers

Data Analytics and Expert Judgment in Time of Crisis:...

Igor Linkov, PhD Senior Science and Technology Manager, US Army Engineer Research and Development Center; Senior Data Analyst (on detail), FEMA/HHS R1 COVID Task Force; Adjunct Professor, Carnegie Mellon University

Read More

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Sinem Sav (EPFL), Apostolos Pyrgelis (EPFL), Juan Ramón Troncoso-Pastoriza (EPFL), David Froelicher (EPFL), Jean-Philippe Bossuat (EPFL), Joao Sa Sousa (EPFL), Jean-Pierre Hubaux (EPFL)

Read More

Demo #10: Security of Deep Learning based Automated Lane...

Takami Sato, Junjie Shen, Ningfei Wang (UC Irvine), Yunhan Jia (ByteDance), Xue Lin (Northeastern University), and Qi Alfred Chen (UC Irvine)

Read More

CHANCEL: Efficient Multi-client Isolation Under Adversarial Programs

Adil Ahmad (Purdue University), Juhee Kim (Seoul National University), Jaebaek Seo (Google), Insik Shin (KAIST), Pedro Fonseca (Purdue University), Byoungyoung Lee (Seoul National University)

Read More