Vulnerable Smart Contract Function Locating Based on Multi-Relational Nested Graph Convolutional Network

TL;DR

This paper introduces a novel multi-relational nested graph convolutional network (MRN-GCN) to accurately locate vulnerable functions in smart contracts by capturing rich syntactic and semantic relationships within contract code.

Contribution

The paper proposes a new multi-relational nested graph model and a specialized graph convolutional network for improved vulnerability detection in smart contracts.

Findings

MRN-GCN outperforms existing methods in accuracy, precision, recall, and F1-score.

The approach effectively captures complex code relationships to identify vulnerabilities.

Experimental results demonstrate significant improvements over baseline models.

Abstract

The immutable and trustable characteristics of blockchain enable smart contracts to be applied in various fields. Unfortunately, smart contracts are subject to various vulnerabilities, which are frequently exploited by attackers, causing financial damage to users.In this paper, we study the problem of vulnerable smart contract function locating. We construct a novel Multi-Relational Nested contract Graph (MRNG) to better characterize the rich syntactic and semantic information in the smart contract code, including the relationships between data and instructions. An MRNG represents a smart contract, where each node represents a function in the smart contract and each edge describes the calling relationship between the functions. In addition, we create a Multi-Relational Function Graph (MRFG) for each function, which characterizes the corresponding function code. That is, each function is characterized as an MRFG, which corresponds to a node in the MRNG. Each MRFG uses different types of edges to represent the different control and data relationships between nodes within a function. We also propose a Multi-Relational Nested Graph Convolutional Network (MRN-GCN) to process the MRNG. MRN-GCN first extracts and aggregates features from each MRFG, using the edge-enhanced graph convolution network and self-attention mechanism. The extracted feature vector is then assigned to the corresponding node in the MRNG to obtain a new Featured Contract Graph (FCG) for the smart contract. Graph convolution is used to further extract features from the FCG. Finally, a feed forward network with a Sigmoid function is used to locate the vulnerable functions. Experimental results on the real-world smart contract datasets show that model MRN-GCN can effectively improve the accuracy, precision, recall and F1-score performance of vulnerable smart contract function locating.