The MANDO project is an open-source research initiative that endeavors to acquire an understanding of the structures of diverse smart contract graphs. The project's primary objective is to detect vulnerabilities in the source code and bytecode of smart contracts, both at the coarse-grained contract-level and fine-grained line-level, with a high degree of accuracy. The core engines of the MANDO project are constructed on the foundation of state-of-the-art heterogeneous deep graph learning methodologies.
MANDO
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- ge-sc-transformer Public
MANDO-HGT is a framework for detecting smart contract vulnerabilities. Given either in source code or bytecode forms, MANDO-HGT adapts heterogeneous graph transformers with customized meta relations for graph nodes and edges to learn their embeddings and train classifiers for detecting various vulnerability types in the contracts' nodes and graphs.
MANDO-Project/ge-sc-transformer’s past year of commit activity - ge-sc Public
MANDO is a new heterogeneous graph representation to learn the heterogeneous contract graphs' structures to accurately detect vulnerabilities in smart contract source code at both coarse-grained contract-level and fine-grained line-level.
MANDO-Project/ge-sc’s past year of commit activity - original-ge-sc Public Forked from MANDO-Project/ge-sc
MANDO is a new heterogeneous graph representation to learn the heterogeneous contract graphs' structures to accurately detect vulnerabilities in smart contract source code at both coarse-grained contract-level and fine-grained line-level.
MANDO-Project/original-ge-sc’s past year of commit activity - ge-sc-bytecode Public
The resource applied Graph Learning on smart contract vulnerability detection in bytecode form.
MANDO-Project/ge-sc-bytecode’s past year of commit activity - ge-sc-machine Public
MANDO-GURU, a deep graph learning-based tool, aims to accurately detect vulnerabilities in smart contract source code at both coarse-grained contract-level and fine-grained line-level.
MANDO-Project/ge-sc-machine’s past year of commit activity
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