Schooling to Exploit Foolish Contracts. (arXiv:2304.10737v1 [cs.CR])

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 application bytecode contract enable engine engineering ethereum expert exploit functions key large networks neural networks oracle patterns rules schooling smart smart contract training unsupervised learning vulnerability vulnerability analysis vulnerable