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.

View More Papers

Towards Anonymous Chatbots with (Un)Trustworthy Browser Proxies

Dzung Pham, Jade Sheffey, Chau Minh Pham, and Amir Houmansadr (University of Massachusetts Amherst)

Read More

TME-Box: Scalable In-Process Isolation through Intel TME-MK Memory Encryption

Martin Unterguggenberger (Graz University of Technology), Lukas Lamster (Graz University of Technology), David Schrammel (Graz University of Technology), Martin Schwarzl (Cloudflare, Inc.), Stefan Mangard (Graz University of Technology)

Read More

Tweezers: A Framework for Security Event Detection via Event...

Jian Cui (Indiana University), Hanna Kim (KAIST), Eugene Jang (S2W Inc.), Dayeon Yim (S2W Inc.), Kicheol Kim (S2W Inc.), Yongjae Lee (S2W Inc.), Jin-Woo Chung (S2W Inc.), Seungwon Shin (KAIST), Xiaojing Liao (Indiana University)

Read More

PyPANDA: Taming the PANDAmonium of Whole System Dynamic Analysis

Luke Craig, Tim Leek (MIT Lincoln Laboratory), Andrew Fasano, Tiemoko Ballo (MIT Lincoln Laboratory, Northeastern University), Brendan Dolan-Gavitt (New York University), William Robertson (Northeastern University)

Read More