Enhancing Smart Contract Vulnerability Detection using Graph-Based Deep Learning Approaches

Abstract

To address the challenges of low accuracy and limited generalization in existing vulnerability detection methods, this paper presents a novel deep learning approach utilizing graph-based algorithms for detecting vulnerabilities in smart contracts. We begin by analyzing the characteristics of vulnerable smart contracts and introducing the concept of “critical opcodes.” A keyword extraction method is developed to effectively identify and select these critical opcodes from smart contracts. Following this, we integrate a critical opcode weighting mechanism into graph-based algorithms, enabling the capture of both hidden relational features and critical opcode characteristics inherent in vulnerable smart contracts. Experimental results indicate that our approach achieves a significant improvement in recognition accuracy, with F1-scores enhancing by 2.39% and 19.54% in binary and multi-class detection scenarios, respectively, when compared to traditional methods such as the LightGBM model.