From Rule-Driven to Data-Driven: Technological Evolution, Challenges, and Future Trends in Smart Contract Vulnerability Detection

View Full Paper

Ask AI

Bookmark

Authors

Discussion

This analysis reveals machine learning enhances smart contract vulnerability detection, suggesting new training paradigms and model integration.

Machine learning improves detection accuracy by analyzing complex vulnerabilities, adapting more efficiently to dynamic environments.
Key metrics show advancements in feature representation as neural self-encoding replaces manual design methods, enriching detection capabilities.
Analysis of technological evolution highlights the role of graph neural networks in understanding cross-contract dependencies and interactions.
Future trends emphasize the need for dynamic graph networks and federated knowledge-sharing to enhance detection systems' adaptability and auditability.

Qingcheng Yu (2025) studied this question.

View Full Paper

Ask AI

Bookmark

View Full Paper

Ask AI

Bookmark

Authors

This analysis reveals machine learning enhances smart contract vulnerability detection, suggesting new training paradigms and model integration.

Machine learning improves detection accuracy by analyzing complex vulnerabilities, adapting more efficiently to dynamic environments.
Key metrics show advancements in feature representation as neural self-encoding replaces manual design methods, enriching detection capabilities.
Analysis of technological evolution highlights the role of graph neural networks in understanding cross-contract dependencies and interactions.
Future trends emphasize the need for dynamic graph networks and federated knowledge-sharing to enhance detection systems' adaptability and auditability.

Qingcheng Yu (2025) studied this question.

Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark