Sandra Siby (EPFL), Marc Juarez (University of Southern California), Claudia Diaz (imec-COSIC KU Leuven), Narseo Vallina-Rodriguez (IMDEA Networks Institute), Carmela Troncoso (EPFL)

Virtually every connection to an Internet service is preceded by a DNS lookup which is performed without any traffic-level protection, thus enabling manipulation, redirection, surveillance, and censorship. To address these issues, large organizations such as Google and Cloudflare are deploying recently standardized protocols that encrypt DNS traffic between end users and recursive resolvers such as DNS-over-TLS (DoT) and DNS-over-HTTPS (DoH). In this paper, we examine whether encrypting DNS traffic can protect users from traffic analysis-based monitoring and censoring. We propose a novel feature set to perform the attacks, as those used to attack HTTPS or Tor traffic are not suitable for DNS’ characteristics. We show that traffic analysis enables the identification of domains with high accuracy in closed and open world settings, using 124 times less data than attacks on HTTPS flows. We find that factors such as location, resolver, platform, or client do mitigate the attacks performance but they are far from completely stopping them. Our results indicate that DNS-based censorship is still possible on encrypted DNS traffic. In fact, we demonstrate that the standardized padding schemes are not effective. Yet, Tor — which does not effectively mitigate traffic analysis attacks on web traffic— is a good defense against DoH traffic analysis.

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Locally Differentially Private Frequency Estimation with Consistency

Tianhao Wang (Purdue University), Milan Lopuhaä-Zwakenberg (Eindhoven University of Technology), Zitao Li (Purdue University), Boris Skoric (Eindhoven University of Technology), Ninghui Li (Purdue University)

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Custos: Practical Tamper-Evident Auditing of Operating Systems Using Trusted...

Riccardo Paccagnella (University of Illinois at Urbana–Champaign), Pubali Datta (University of Illinois at Urbana–Champaign), Wajih Ul Hassan (University of Illinois at Urbana–Champaign), Adam Bates (University of Illinois at Urbana–Champaign), Christopher W. Fletcher (University of Illinois at Urbana–Champaign), Andrew Miller (University of Illinois at Urbana–Champaign), Dave Tian (Purdue University)

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When Malware is Packin' Heat; Limits of Machine Learning...

Hojjat Aghakhani (University of California, Santa Barbara), Fabio Gritti (University of California, Santa Barbara), Francesco Mecca (Università degli Studi di Torino), Martina Lindorfer (TU Wien), Stefano Ortolani (Lastline Inc.), Davide Balzarotti (Eurecom), Giovanni Vigna (University of California, Santa Barbara), Christopher Kruegel (University of California, Santa Barbara)

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