Enhancing Smart Contract Vulnerability Detection using Graph-Based Deep Learning Approaches
Vinay Kumar Kasula, Akhila Reddy Yadulla, Mounica Yenugula, Bhargavi Konda
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.