Christoph Kerschbaumer (Mozilla Corporation), Frederik Braun (Mozilla Corporation), Simon Friedberger (Mozilla Corporation), Malte Jürgens (Mozilla Corporation)

The web was originally developed in an attempt to allow scientists from around the world to share information efficiently. As the web evolved, the threat model for the web evolved as well. While it was probably acceptable for research to be freely shared with the world, current use cases like online shopping, media consumption or private messaging require stronger security safeguards which ensure that network attackers are not able to view, steal, or even tamper with the transmitted data. Unfortunately the Hypertext Transfer Protocol (http) does not provide any of these required security guarantees.

The Hypertext Transfer Protocol Secure (https) on the other hand allows carrying http over the Transport Layer Security (TLS) protocol and in turn fixes these security shortcomings of http by creating a secure and encrypted connection between the browser and the website. While the majority of websites support https nowadays, https remains an opt-in mechanism that not everyone perceives as necessary or affordable.

In this paper we evaluate the state of https adoption on the web. We survey different mechanisms which allow upgrading connections from http to https, and provide real world browsing data from over 140 million Firefox release users. We provide numbers showcasing https adoption in different geographical regions as well as on different operating systems and highlight the effectiveness of the different upgrading mechanisms. In the end, we can use this analysis to make actionable suggestions to further improve https adoption on the web.

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Tweezers: A Framework for Security Event Detection via Event...

Jian Cui (Indiana University), Hanna Kim (KAIST), Eugene Jang (S2W Inc.), Dayeon Yim (S2W Inc.), Kicheol Kim (S2W Inc.), Yongjae Lee (S2W Inc.), Jin-Woo Chung (S2W Inc.), Seungwon Shin (KAIST), Xiaojing Liao (Indiana University)

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CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models

Rui Zeng (Zhejiang University), Xi Chen (Zhejiang University), Yuwen Pu (Zhejiang University), Xuhong Zhang (Zhejiang University), Tianyu Du (Zhejiang University), Shouling Ji (Zhejiang University)

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MALintent: Coverage Guided Intent Fuzzing Framework for Android

Ammar Askar (Georgia Institute of Technology), Fabian Fleischer (Georgia Institute of Technology), Christopher Kruegel (University of California, Santa Barbara), Giovanni Vigna (University of California, Santa Barbara), Taesoo Kim (Georgia Institute of Technology)

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