Enhancing Smart Contract Security on VNT Blockchain with an Advanced Graph Neural Network Approach

Enhancing Smart Contract Security on VNT Blockchain with an Advanced Graph Neural Network Approach

연구 분야: Analysis

학회: AIBC '24: Proceedings of the 2024 5th International Artificial Intelligence and Blockchain Conference

Smart contracts, pivotal to blockchain technology, automate contract enforcement with precision and reliability but are plagued by security vulnerabilities that have led to significant financial losses. Traditional methods like static analysis and fuzzy testing, though foundational, typically fail to grasp the complex logic of smart contract code, resulting in poor adaptability to the evolving landscape of blockchain applications. To address these challenges, this paper introduces an advanced Graph Neural Network (GNN) approach tailored for smart contract vulnerability detection on the VNT blockchain platform. Our methodology leverages the inherent structural and semantic relationships within contract code, transforming it into a graph representation for deeper learning. The enhanced GNN model effectively captures intricate patterns and dependencies that traditional methods overlook, significantly improving detection accuracy and efficiency. Experimental results demonstrate that our approach outperforms existing techniques, offering a promising avenue for both refining smart contract security analyses and guiding future development practices. We further contribute to the field by providing a robust dataset of smart contracts, fostering further research and application enhancements. Experimental results demonstrate that the proposed method not only advances the security of smart contract in VNT but also establishes a new benchmark for the application of deep learning in blockchain.