Caleb Stewart, Rhonda Gaede, Jeffrey Kulick (University of Alabama in Huntsville)

We present DRAGON, a graph neural network (GNN) that predicts data types for decompiled variables along with a confidence estimate for each prediction. While we only train DRAGON on x64 binaries compiled without optimization, we show that DRAGON generalizes well to all combinations of the x64, x86, ARM64, and ARM architectures compiled across optimization levels O0-O3. We compare DRAGON with two state-of-the-art approaches for binary type inference and demonstrate that DRAGON exhibits a competitive or superior level of accuracy for simple type prediction while also providing useful confidence estimates. We show that the learned confidence estimates produced by DRAGON strongly correlate with accuracy, such that higher confidence predictions generally correspond with a higher level of accuracy than lower confidence predictions.

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Rethinking Trust in Forge-Based Git Security

Aditya Sirish A Yelgundhalli (New York University), Patrick Zielinski (New York University), Reza Curtmola (New Jersey Institute of Technology), Justin Cappos (New York University)

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Cross-Origin Web Attacks via HTTP/2 Server Push and Signed...

Pinji Chen (Tsinghua University), Jianjun Chen (Tsinghua University & Zhongguancun Laboratory), Mingming Zhang (Zhongguancun Laboratory), Qi Wang (Tsinghua University), Yiming Zhang (Tsinghua University), Mingwei Xu (Tsinghua University), Haixin Duan (Tsinghua University)

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AlphaDog: No-Box Camouflage Attacks via Alpha Channel Oversight

Qi Xia (University of Texas at San Antonio), Qian Chen (University of Texas at San Antonio)

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Towards Parallel Binary Code Analysis

Xiaozhu Meng (University of Wisconsin-Madison)

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