Liang Wang, Hyojoon Kim, Prateek Mittal, Jennifer Rexford (Princeton University)

In conventional DNS, or Do53, requests and responses are sent in cleartext. Thus, DNS recursive resolvers or any on-path adversaries can access privacy-sensitive information. To address this issue, several encryption-based approaches (e.g., DNS-over-HTTPS) and proxy-based approaches (e.g., Oblivious DNS) were proposed. However, encryption-based approaches put too much trust in recursive resolvers. Proxy-based approaches can help hide the client’s identity, but sets a higher deployment barrier while also introducing noticeable performance overhead. We propose PINOT, a packet-header obfuscation system that runs entirely in the data plane of a programmable network switch, which provides a lightweight, low-deployment-barrier anonymization service for clients sending and receiving DNS packets. PINOT does not require any modification to the DNS protocol or additional client software installation or proxy setup. Yet, it can also be combined with existing approaches to provide stronger privacy guarantees. We implement a PINOT prototype on a commodity switch, deploy it in a campus network, and present results on protecting user identity against public DNS services.

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MINOS: A Lightweight Real-Time Cryptojacking Detection System

Faraz Naseem (Florida International University), Ahmet Aris (Florida International University), Leonardo Babun (Florida International University), Ege Tekiner (Florida International University), A. Selcuk Uluagac (Florida International University)

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SymQEMU: Compilation-based symbolic execution for binaries

Sebastian Poeplau (EURECOM and Code Intelligence), Aurélien Francillon (EURECOM)

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Favocado: Fuzzing the Binding Code of JavaScript Engines Using...

Sung Ta Dinh (Arizona State University), Haehyun Cho (Arizona State University), Kyle Martin (North Carolina State University), Adam Oest (PayPal, Inc.), Kyle Zeng (Arizona State University), Alexandros Kapravelos (North Carolina State University), Gail-Joon Ahn (Arizona State University and Samsung Research), Tiffany Bao (Arizona State University), Ruoyu Wang (Arizona State University), Adam Doupe (Arizona State University),…

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KUBO: Precise and Scalable Detection of User-triggerable Undefined Behavior...

Changming Liu (Northeastern University), Yaohui Chen (Facebook Inc.), Long Lu (Northeastern University)

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