Laura Matzen, Michelle A Leger, Geoffrey Reedy (Sandia National Laboratories)

Binary reverse engineers combine automated and manual techniques to answer questions about software. However, when evaluating automated analysis results, they rarely have additional information to help them contextualize these results in the binary. We expect that humans could more readily understand the binary program and these analysis results if they had access to information usually kept internal to the analysis, like value-set analysis (VSA) information. However, these automated analyses often give up precision for scalability, and imprecise information might hinder human decision making.

To assess how precision of VSA information affects human analysts, we designed a human study in which reverse engineers answered short information flow problems, determining whether code snippets would print sensitive information. We hypothesized that precise VSA information would help our participants analyze code faster and more accurately, and that imprecise VSA information would lead to slower, less accurate performance than no VSA information. We presented hand-crafted code snippets with precise, imprecise, or no VSA information in a blocked design, recording participants’ eye movements, response times, and accuracy while they analyzed the snippets. Our data showed that precise VSA information changed participants’ problem-solving strategies and supported faster, more accurate analyses. However, surprisingly, imprecise VSA information also led to increased accuracy relative to no VSA information, likely due to the extra time participants spent working through the code.

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From Library Portability to Para-rehosting: Natively Executing Microcontroller Software...

Wenqiang Li (State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences; Department of Computer Science, the University of Georgia, USA; School of Cyber Security, University of Chinese Academy of Sciences; Department of Electrical Engineering and Computer Science, the University of Kansas, USA), Le Guan (Department of Computer Science, the University…

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A Cross-Architecture Instruction Embedding Model for Natural Language Processing-Inspired...

Kimberly Redmond (University of South Carolina), Lannan Luo (University of South Carolina), Qiang Zeng (University of South Carolina)

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Understanding MPU Usage in Microcontroller-based Systems in the Wild

Wei Zhou, Zhouqi Jiang (School of Cyber Science and Engineering, Huazhong University of Science and Technology), Le Guan (School of Computing, University of Georgia)

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EarArray: Defending against DolphinAttack via Acoustic Attenuation

Guoming Zhang (Zhejiang University), Xiaoyu Ji (Zhejiang University), Xinfeng Li (Zhejiang University), Gang Qu (University of Maryland), Wenyuan Xu (Zhejing University)

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