Rachael Little, Dongpeng Xu (University of New Hampshire)

Software obfuscation is a form of code protection designed to hide the inner workings of a program from reverse engineering and analysis. Mixed Boolean Arithmetic (MBA) is one popular form that obscures simple arithmetic expressions via transformation to more complex equations involving both boolean and arithmetic operations. Most prior works focused on developing strong MBA at the source code or expression level; however, how many of them are resilient against compiler optimizations still remain unknown. In this work, we carefully inspect the strength of MBA obfuscation after various compiler optimizations. We embed MBA expressions from several popular datasets into C programs and examine how they appear post-compilation using the compilers GCC, Clang, and MSVC. Surprisingly, we discover a notable trend of reduction in MBA size and complexity after compiler optimization. We report our findings and discuss how MBA expressions are impacted by compiler optimizations.

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Detecting IMSI-Catchers by Characterizing Identity Exposing Messages in Cellular...

Tyler Tucker (University of Florida), Nathaniel Bennett (University of Florida), Martin Kotuliak (ETH Zurich), Simon Erni (ETH Zurich), Srdjan Capkun (ETH Zuerich), Kevin Butler (University of Florida), Patrick Traynor (University of Florida)

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What Makes Phishing Simulation Campaigns (Un)Acceptable? A Vignette Experiment

Jasmin Schwab (German Aerospace Center (DLR)), Alexander Nussbaum (University of the Bundeswehr Munich), Anastasia Sergeeva (University of Luxembourg), Florian Alt (University of the Bundeswehr Munich and Ludwig Maximilian University of Munich), and Verena Distler (Aalto University)

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Statically Discover Cross-Entry Use-After-Free Vulnerabilities in the Linux Kernel

Hang Zhang (Indiana University Bloomington), Jangha Kim (The Affiliated Institute of ETRI, ROK), Chuhong Yuan (Georgia Institute of Technology), Zhiyun Qian (University of California, Riverside), Taesoo Kim (Georgia Institute of Technology)

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Passive Inference Attacks on Split Learning via Adversarial Regularization

Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

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