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From P-Code to GNN: extract binary code semantics

Quarkslab Blog · published 12 August 2026

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14 Sept 2026
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14 Sept 2026
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From P-Code to GNN: Extracting Binary Code Semantics Read at the source ↗
14 Sept 2026

Links from this post

Link
Host
https://github.com/quarkslab/conf-presentations/blob/master/Confs/ESANN2026/ES2026-155.pdf
github.com
https://www.usenix.org/system/files/sec22-marcelli.pdf
usenix.org
https://github.com/quarkslab/pcode_graph
github.com
https://dl.acm.org/doi/pdf/10.1145/3576915.3616589
dl.acm.org
https://dl.acm.org/doi/pdf/10.1145/3533767.3534367
dl.acm.org
https://blog.quarkslab.com/sighthouse-automated-function-identification.html
blog.quarkslab.com
https://github.com/Cisco-Talos/binary_function_similarity
github.com
https://github.com/Cisco-Talos/binary_function_similarity/issues/39
github.com
https://pytorch-geometric.readthedocs.io/en/latest/cheatsheet/gnn_cheatsheet.html
pytorch-geometric.readthedocs.io
https://arxiv.org/pdf/2405.01663
arxiv.org
https://arxiv.org/abs/1905.12265
arxiv.org
https://proceedings.neurips.cc/paper_files/paper/2020/file/d89a66c7c80a29b1bdbab0f2a1a94af8-Paper.pdf
proceedings.neurips.cc
https://en.wikipedia.org/wiki/Receiver_operating_characteristic
en.wikipedia.org