计算机科学 ›› 2025, Vol. 52 ›› Issue (9): 368-375.doi: 10.11896/jsjkx.241000007
周涛, 杜永萍, 谢润锋, 韩红桂
ZHOU Tao, DU Yongping, XIE Runfeng, HAN Honggui
摘要： 智能合约是在区块链上自动执行的代码,具有不可逆性且与金融交易密切相关,故其安全问题至关重要。然而,当前智能合约漏洞检测技术仍面临特征提取效率低、检测精度低以及过度依赖专家规则等问题。对此,提出一种基于异构合约图多维度特征深度融合的漏洞检测方法。首先,针对智能合约数据集的代码进行去噪处理,采用代码函数交换等数据增强方法扩充数据集,进而将其表示为异构合约图。其次,结合图嵌入技术以及代码预训练技术高效获取智能合约图以及对应操作码中节点的高维度语义表示。最后,设计双层异构图注意力网络深度融合在两种维度下学习到的节点特征,以实现高效的漏洞检测。针对不同类型漏洞的实验结果表明,所提方法整体表现较对比方法均有所提升,F1指标平均值高于77.72%,在拒绝服务漏洞类型的检测上表现最佳,F1值最高达到84.88%,较传统的深度学习方法和图拓扑检测方法分别提升了10.62％和22.34％。所提方法不仅提高了检测的效率和准确性,而且通过学习节点特征减少了对专家规则的依赖,为智能合约的安全性提供了更为可靠的保障。
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