Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training
📰 ArXiv cs.AI
Researchers propose a robust smart contract vulnerability detection method using contrastive learning-enhanced granular-ball training to improve accuracy with limited labeled data
Action Steps
- Collect and preprocess smart contract datasets
- Apply contrastive learning to enhance feature representations
- Implement granular-ball training to improve model robustness
- Evaluate the proposed method on benchmark datasets
Who Needs to Know This
AI engineers and cybersecurity experts on a team can benefit from this research to develop more accurate smart contract vulnerability detection tools, while data scientists can apply the proposed method to other domains with limited labeled data
Key Insight
💡 Contrastive learning can improve the robustness of smart contract vulnerability detection models even with limited labeled data
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🔒 Boost smart contract security with contrastive learning-enhanced granular-ball training! 💡
Key Takeaways
Researchers propose a robust smart contract vulnerability detection method using contrastive learning-enhanced granular-ball training to improve accuracy with limited labeled data
Full Article
Title: Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training
Abstract:
arXiv:2603.27734v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have emerged as a prominent approach for detecting smart contract vulnerabilities, driven by the growing contract datasets and advanced deep learning techniques. However, DNNs typically require large-scale labeled datasets to model the relationships between contract features and vulnerability labels. In practice, the labeling process often depends on existing open-sourced tools, whose accuracy cannot be guaranteed. Con
Abstract:
arXiv:2603.27734v1 Announce Type: cross Abstract: Deep neural networks (DNNs) have emerged as a prominent approach for detecting smart contract vulnerabilities, driven by the growing contract datasets and advanced deep learning techniques. However, DNNs typically require large-scale labeled datasets to model the relationships between contract features and vulnerability labels. In practice, the labeling process often depends on existing open-sourced tools, whose accuracy cannot be guaranteed. Con
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