April 24, 2023, 12:45 a.m. | Tamer Abdelaziz, Aquinas Hobor

cs.LG updates on arXiv.org arxiv.org

We introduce SCooLS, our Smart Contract Learning (Semi-supervised) engine.
SCooLS uses neural networks to analyze Ethereum contract bytecode and
identifies specific vulnerable functions. SCooLS incorporates two key elements:
semi-supervised learning and graph neural networks (GNNs). Semi-supervised
learning produces more accurate models than unsupervised learning, while not
requiring the large oracle-labeled training set that supervised learning
requires. GNNs enable direct analysis of smart contract bytecode without any
manual feature engineering, predefined patterns, or expert rules.


SCooLS is the first application of …

analysis analyze application arxiv engineering ethereum expert exploit feature feature engineering gnns graph graph neural networks networks neural networks oracle patterns rules semi-supervised semi-supervised learning set smart smart contract supervised learning training unsupervised unsupervised learning vulnerability vulnerable

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