TY - GEN
T1 - A Graph Neural Network-Based Smart Contract Vulnerability Detection Method with Artificial Rule
AU - Wei, Ziyue
AU - Zheng, Weining
AU - Su, Xiaohong
AU - Tao, Wenxin
AU - Wang, Tiantian
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - As blockchain technology advances, the security of smart contracts has become increasingly crucial. However, most of smart contract vulnerability detection tools available on the market currently rely on artificial-predefined vulnerability rules, which result in suboptimal generalization ability and detection accuracy. Deep learning-based methods usually treat smart contracts as token sequences, which limit the utilization of structural information and the integration of artificial rules. To mitigate these issues, we propose a novel smart contract vulnerability detection method. First, we propose an approach for constructing contract graph to capture vital structural information, such as control- and data- flow. Then, we employ a Wide & Deep learning model to integrate the structural feature, sequencial feature, and artificial rules for smart contract vulnerability detection. Extensive experiments show that the proposed method performs exceptionally well in detecting four different types of vulnerabilities. The results demonstrate that integrating structural information and artificial rules can significantly improve the effectiveness of smart contract vulnerability detection.
AB - As blockchain technology advances, the security of smart contracts has become increasingly crucial. However, most of smart contract vulnerability detection tools available on the market currently rely on artificial-predefined vulnerability rules, which result in suboptimal generalization ability and detection accuracy. Deep learning-based methods usually treat smart contracts as token sequences, which limit the utilization of structural information and the integration of artificial rules. To mitigate these issues, we propose a novel smart contract vulnerability detection method. First, we propose an approach for constructing contract graph to capture vital structural information, such as control- and data- flow. Then, we employ a Wide & Deep learning model to integrate the structural feature, sequencial feature, and artificial rules for smart contract vulnerability detection. Extensive experiments show that the proposed method performs exceptionally well in detecting four different types of vulnerabilities. The results demonstrate that integrating structural information and artificial rules can significantly improve the effectiveness of smart contract vulnerability detection.
KW - Blockchain
KW - Graph neural network
KW - Smart contract
KW - Vulnerability detection
KW - Wide & Deep learning model
UR - https://www.scopus.com/pages/publications/85174626531
U2 - 10.1007/978-3-031-44216-2_20
DO - 10.1007/978-3-031-44216-2_20
M3 - 会议稿件
AN - SCOPUS:85174626531
SN - 9783031442155
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 241
EP - 252
BT - Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings
A2 - Iliadis, Lazaros
A2 - Papaleonidas, Antonios
A2 - Angelov, Plamen
A2 - Jayne, Chrisina
PB - Springer Science and Business Media Deutschland GmbH
T2 - 32nd International Conference on Artificial Neural Networks, ICANN 2023
Y2 - 26 September 2023 through 29 September 2023
ER -