Towards Opcode-Based Smart Contract Reentrancy Vulnerability Detection Using Explainable Deep Learning

Abstract

The rapid growth in blockchain technology adoption has highlighted the significance of security in Ethereum smart contracts. Due to its immutable nature, post-deployment rectification is not possible, and vulnerabilities such as reentrancy have led to substantial financial losses in recent years, making it a pressing research priority for its timely detection. Along with static and dynamic analysis tools, recent studies have shown promising results using Deep Learning (DL) and Machine Learning (ML) techniques for vulnerability detection using imagebased methods. Although these methods often suffer from high false positive rates and limited interpretability. To address these issues, we proposed an interpretable One-dimensional Convolutional Neural Network (1D CNN), a lightweight DL framework with integrated Gradients, an attribution for the Explainable AI (XAI) framework. This framework processes smart contract opcode in a series of sequences rendered as RGB-encoded strips, enabling effective feature extraction while preserving the contract semantics and execution order. Trained on a publicly available labeled comprehensive dataset named Messi-Q, which has already been used in prominent studies in the field. Approach achieves over 97% classification accuracy in detecting reentrancy vulnerability. More importantly, it provides fine-grained, opcode-level attributions offering a scalable and interpretable path forward for smart contract analysis.