Knowledge Migration Framework for Smart Contract Vulnerability Detection

no code implementations • 15 Dec 2024 • Luqi Wang, Wenbao Jiang

The approach markedly enhances the model's capacity for feature extraction and cross-class adaptation, while concurrently reducing computational overhead. In order to further enhance the extraction of vulnerability features, an adaptive fusion module is proposed in this paper, which aims to strengthen the interaction and fusion of feature information. The experimental results demonstrate that the STip model attains an average F1 value detection score of 91. 16% for the four vulnerabilities without disclosing the original smart contract data.

Data-free Knowledge Distillation Vulnerability Detection