Hyunwoo Lee (Seoul National University), Zach Smith (University of Luxembourg), Junghwan Lim (Seoul National University), Gyeongjae Choi (Seoul National University), Selin Chun (Seoul National University), Taejoong Chung (Rochester Institute of Technology), Ted "Taekyoung" Kwon (Seoul National University)

Middleboxes (MBs) are widely deployed in order to enhance security and performance in networking.
However, as the communications over the TLS become increasingly common, the end-to-end channel model of the TLS undermines the efficacy of MBs.
Existing solutions, such as `split TLS' that intercepts TLS sessions, often introduce significant security risks by installing a custom root certificate or sharing a private key.
Many studies have confirmed the vulnerabilities of combining the TLS with MBs, which include certificate validation failures, unwanted content modification, and using obsolete ciphersuites.
To address the above issues, we introduce an MB-aware TLS protocol, dubbed maTLS, that allows MBs to participate in the TLS in a visible and accountable fashion.
Every participating MB now splits a session into two segments with its own security parameters in collaboration with the two endpoints.
However, the session is still secure as the maTLS protocol is designed to achieve the authentication of MBs, the audit of MBs' operations, and the verification of security parameters of segments.
We carry out testbed-based experiments to show that maTLS achieves the above security goals with marginal overhead.
We also prove the security model of maTLS by using Tamarin, a security verification tool.

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Samuel Weiser (Graz University of Technology), Mario Werner (Graz University of Technology), Ferdinand Brasser (Technische Universität Darmstadt), Maja Malenko (Graz University of Technology), Stefan Mangard (Graz University of Technology), Ahmad-Reza Sadeghi (Technische Universität Darmstadt)

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Daimeng Wang (University of California Riverside), Ajaya Neupane (University of California Riverside), Zhiyun Qian (University of California Riverside), Nael Abu-Ghazaleh (University of California Riverside), Srikanth V. Krishnamurthy (University of California Riverside), Edward J. M. Colbert (Virginia Tech), Paul Yu (U.S. Army Research Lab (ARL))

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Sina Faezi (University of California, Irvine), Sujit Rokka Chhetri (University of California, Irvine), Arnav Vaibhav Malawade (University of California, Irvine), John Charles Chaput (University of California, Irvine), William Grover (University of California, Riverside), Philip Brisk (University of California, Riverside), Mohammad Abdullah Al Faruque (University of California, Irvine)

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Ke Coby Wang (UNC Chapel Hill), Michael K. Reiter (UNC Chapel Hill)

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Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

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Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)