MANDO-HGT: Heterogeneous Graph Transformers for Smart Contract Vulnerability Detection
Author:
Affiliation:
1. Leibniz Universität Hannover,L3S Research Center,Hannover,Germany
2. Singapore Management University,Singapore
3. Independent Researcher,Atlanta,Georgia,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10173934/10173935/10174104.pdf?arnumber=10174104
Reference79 articles.
1. Hunting the ethereum smart contract: Color-inspired inspection of potential attacks;huang,2018
2. DC-Hunter: Detecting dangerous smart contracts via bytecode matching;han;Cyber Security,2020
3. Multi-Class classification of vulnerabilities in smart contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing
4. Smart Contract Vulnerability Detection using Graph Neural Network
5. Towards safer smart contracts: A sequence learning approach to detecting security threats;tann,2018
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2. Empirical Study of Move Smart Contract Security: Introducing MoveScan for Enhanced Analysis;Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis;2024-09-11
3. Ethereum Smart Contract Vulnerability Detection and Machine Learning-Driven Solutions: A Systematic Literature Review;Electronics;2024-06-12
4. Detecting Reentrancy Vulnerability in Smart Contracts using Graph Convolution Networks;2024 IEEE 21st Consumer Communications & Networking Conference (CCNC);2024-01-06
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