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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Poster: Understanding User Acceptance of Privacy Labels: Barriers and...

Jingwen Yan (Clemson University), Mohammed Aldeen (Clemson University), Jalil Harris (Clemson University), Kellen Grossenbacher (Clemson University), Aurore Munyaneza (Texas Tech University), Song Liao (Texas Tech University), Long Cheng (Clemson University)

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Query Privacy in Data Spaces

Shuwen Liu (School of Data Science, The Chinese University of Hong Kong, Shenzhen, China), George C. Polyzos (School of Data Science, The Chinese University of Hong Kong, Shenzhen, China and ExcID P.C., Athens, Greece)

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Evaluating Machine Learning-Based IoT Device Identification Models for Security...

Eman Maali (Imperial College London), Omar Alrawi (Georgia Institute of Technology), Julie McCann (Imperial College London)

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BrowserFM: A Feature Model-based Approach to Browser Fingerprint Analysis

Maxime Huyghe (Univ. Lille, Inria, CNRS, UMR 9189 CRIStAL), Clément Quinton (Univ. Lille, Inria, CNRS, UMR 9189 CRIStAL), Walter Rudametkin (Univ. Rennes, Inria, CNRS, UMR 6074 IRISA)

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