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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PowerRadio: Manipulate Sensor Measurement via Power GND Radiation

Yan Jiang (Zhejiang University), Xiaoyu Ji (Zhejiang University), Yancheng Jiang (Zhejiang University), Kai Wang (Zhejiang University), Chenren Xu (Peking University), Wenyuan Xu (Zhejiang University)

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Throwaway Accounts and Moderation on Reddit

Cheng Guo (Clemson University), Kelly Caine (Clemson University)

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Evaluating LLMs Towards Automated Assessment of Privacy Policy Understandability

Keika Mori (Deloitte Tohmatsu Cyber LLC, Waseda University), Daiki Ito (Deloitte Tohmatsu Cyber LLC), Takumi Fukunaga (Deloitte Tohmatsu Cyber LLC), Takuya Watanabe (Deloitte Tohmatsu Cyber LLC), Yuta Takata (Deloitte Tohmatsu Cyber LLC), Masaki Kamizono (Deloitte Tohmatsu Cyber LLC), Tatsuya Mori (Waseda University, NICT, RIKEN AIP)

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A Field Study to Uncover and a Tool to...

Leon Kersten (Eindhoven University of Technology), Kim Beelen (Eindhoven University of Technology), Emmanuele Zambon (Eindhoven University of Technology), Chris Snijders (Eindhoven University of Technology), Luca Allodi (Eindhoven University of Technology)

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