Automated Vulnerability Detection in Smart Contracts Using Deep Learning

Abstract

Smart contracts, essential to Blockchain functionality, can be compromised by vulnerabilities like reentrancy attacks, allowing unscrupulous entities to misappropriate funds. A universal and efficient multi-modal vulnerability detection framework is created to tackle detection issues that exceed the capability of standard methods such as fuzzy testing and symbolic execution. The methodology incorporates BiLSTM, EfficientNet, and Transformer architectures, augmented by CNN2D and BiGRU for better feature extraction and sequence modeling. The SMARTBUG dataset is employed in two formats: compiled OPCODES and features extracted via Word2Vec from smart contract source code. Preprocessing entails utilizing Word2Vec to produce N-gram numerical representations, succeeded by an 80-20 division for training and testing. The system analyzes multi-modal inputs, such as grayscale image attributes, opcode frequency statistics, and source code sequences, facilitating comprehensive vulnerability characterisation. The experimental assessment assesses the proposed model in comparison to existing algorithms, including MLP, GRU, and BiLSTM, utilizing criteria such as accuracy, precision, recall, and F-score. The CNN2D + BiGRU + EfficientNet + Transformer setup attains the greatest detection accuracy of 91.9%, surpassing all benchmarks. The system reduces dependence on domain knowledge by automating feature extraction, enabling adaptation across diverse smart contract forms and improving security in blockchain contexts