A Multimodal Deep Learning Approach for Efficient Vulnerability Detection in Smart Contracts

Abstract

In this paper, we present a comprehensive approach for efficient vulnerability detection in Ethereum smart contracts using a multimodal deep learning (DL) approach. Our proposed approach combines two levels of features in smart contracts, including source code, bytecode, and utilizes BERT and Bi-LSTM models to extract and analyze the features. The last layer of our multimodal approach is a fully connected layer that predicts the vulnerability in Ethereum smart contracts. We address the limitations of existing deep learning-based vulnerability detection methods for smart contracts, which often rely on a single type of feature or model, resulting in limited accuracy and effectiveness. The experimental results show that our proposed approach achieves superior results compared to existing state-of-the-art methods, demonstrating the effectiveness and potential of multimodal DL approaches in smart contract vulnerability detection.