SC-CCA: A Deep Learning Framework for Smart Contract Vulnerability Detection Based on CNN, BiLSTM, and Self-Attention Mechanism

Abstract

Smart contracts, one of the most prominent applications of blockchain, are changing the way sectors like finance and law operate. On the flip side, code vulnerabilities on smart contracts are bad news for all, even more for those that trade and resell large amounts of digital assets, since the possible consequences can be disastrous. In this study, we propose SC-CCA, a new deep learning framework that specifically tackles the limitations of conventional methods for detecting smart contract vulnerabilities. SC-CCA embeds CNN combined with Bi-LSTM and Self-Attention Mechanism for further improving the extraction of features from a contract code. In this framework, the use of pre-trained CodeBERT language models for word embedding improves the ability of the feature extraction module to obtain more comprehensive semantic information and context-related information. Feature extraction and pipelined feature concatenation at different levels improve detection and robustness. Experimental results show that, especially in the context of vulnerability detection, SC-CCA achieves higher accuracy, recall and F1-score when compared to existing approaches on the Smart Willed datasets. Our research offers valuable insights for analyzing complex code and detecting a wider range of vul-nerabilities in smart contracts.