Smart Contract Vulnerability Detection Based on Dual Adversarial Domain Adaptation

With the widespread application of smart contracts and the expansion of asset management scale, various new attacks continue to emerge, and a method that can adapt to new vulnerabilities more quickly is urgently needed. Although deep learning methods have shown superior performance in vulnerability detection, their dependence on a large number of labeled samples limits their applicability in new vulnerability scenarios. Therefore, this article introduces a dual adversarial domain adaptation (DADA) approach. This approach consists of two generators and two discriminators. First, the source generator is pretrained with extensive labeled known vulnerability samples in the source domain to extract discriminative features, and its parameters are shared with the target generator. Subsequently, the features of the source and target domain samples are input into the source discriminator, and the target generator is guided to learn domain-invariant features through adversarial training; at the same time, the target domain discriminator is introduced to further weaken its dependence on the distribution of source domain features, thereby improving its adaptability to new vulnerabilities. We conducted experimental evaluations on public datasets, and the results show that our proposed method outperforms six mainstream deep learning-based detection methods. We applied domain adaptation methods to smart contract vulnerability detection for the first time, providing a reference for small sample learning in this field in the future.