VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model. (arXiv:2309.08474v1 [cs.CR])

This paper presents VulnSense framework, a comprehensive approach to
efficiently detect vulnerabilities in Ethereum smart contracts using a
multimodal learning approach on graph-based and natural language processing
(NLP) models. Our proposed framework combines three types of features from
smart contracts comprising source code, opcode sequences, and control flow
graph (CFG) extracted from bytecode. We employ Bidirectional Encoder
Representations from Transformers (BERT), Bidirectional Long Short-Term Memory
(BiLSTM) and Graph Neural Network (GNN) models to extract and analyze these
features. The final …

comprehensive approach contracts detect detection ethereum features framework language natural natural language natural language processing network neural network nlp smart smart contracts types vulnerabilities vulnerability vulnerability detection