Design and Application of Legally Valid Payment Templates Based on Linking Contracts

Area of Science:

• Computer Science
• Law and Technology

Background:

• Smart contracts automate tasks like verification and payment, driven by blockchain technology.
• The legal status of smart contracts lags behind technological advancement, creating regulatory challenges.
• Existing methods for smart contract vulnerability detection have limitations in accuracy and feature extraction.

Purpose of the Study:

• To explore the integration of smart contracts within legal frameworks.
• To propose a new system for smart contract regulation, focusing on logic, arbitration, and verification.
• To develop an advanced payment template for smart contract vulnerability detection.

Main Methods:

• Investigated the incorporation of smart contracts into legal regulation.
• Developed a smart contract payment template utilizing semantic-aware graph neural networks.
• Analyzed contract logic, arbitration processes, and formal verification within current legal contexts.

Main Results:

• The proposed semantic-aware graph neural network method significantly enhances detection accuracy for smart contract vulnerabilities.
• The new approach substantially reduces the false alarm rate compared to traditional methods.
• The strategy demonstrates marked improvements across various performance indicators in experimental comparisons.

Conclusions:

• The semantic-aware graph neural network payment template offers a more effective solution for smart contract vulnerability detection.
• Integrating smart contracts with legal frameworks requires forward-looking approaches to address the law's inherent lag.
• This research contributes to advancing the security and regulatory understanding of smart contracts.