TLSCG: Transfer Learning-Based Efficient Anomalous Smart Contract Generation to Empower Unknown Vulnerability Detection

Abstract

Security vulnerabilities in smart contracts can have serious economic consequences. Existing smart contract vulnerability detection methods rely primarily on strict rules defined by experts, making current research limited to detecting specific known vulnerabilities and difficult to deal with other types of anomalous contracts (i.e., the variants of contracts with potentially known vulnerabilities). The imbalance of a smart contract dataset also affects the effectiveness of deep learning-based methods. This paper proposes a new transfer learning-based, anomalous smart contract generation (TLSCG) method for abnormal contract detection, aimed at effectively detecting known vulnerabilities and other anomalous contracts. This method trains a smart contract operation code sequence generation model and improves the authenticity of generating smart contracts by adding semantic regularization terms to the loss function. Through transfer learning, the generative model can be readily extended to new types of vulnerabilities, obtaining known vulnerabilities and anomalous contract generation models, expanding training data, improving the generalization of detection models, and enabling detection models to detect anomalous contracts. By using a real smart contract dataset for validation, experiments show that the proposed method can effectively improve the generalization of the model in detecting known vulnerabilities. Compared to the latest rule-based vulnerability detection tools, the accuracy of anomalous contract detection is improved by 40% and the F1 score is improved by 24%.