An attention-based CNN-BiLSTM model for smart contract vulnerability detection

Abstract

Smart contracts operating on blockchain platforms like Ethereum often face critical security challenges, which can lead to significant financial damage. Existing detection techniques frequently fall short in terms of precision or scalability. To overcome these limitations, we introduce an innovative deep learning framework that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks and incorporates an attention mechanism. This model is designed to extract both short-range syntactic cues and long-distance contextual patterns from smart contract code. By applying advanced preprocessing and employing the SMOTE algorithm to handle class imbalance, our approach excels in identifying key vulnerabilities, including Reentrancy, Timestamp Dependency, Integer Overflows, and Dangerous Delegatecall . Experimental evaluations reveal notable gains in detecting rare vulnerability types, particularly in terms of recall and precision, underscoring the effectiveness of attention-augmented hybrid models for enhancing smart contract security analysis.