Detecting Reentrancy Vulnerability in Smart Contracts using Graph Convolution Networks

Abstract

Because of many advanced features, Decentralized Finance (DeFi) has become a hot topic in the past decade. As an application of blockchain technology, DeFi allows people to trade cryptocurrencies and other financial products more efficiently, securely, and privately. Specifically, using smart contracts further improves transaction rate and the quality of user experience (QoE) by defining the business logic via code. However, if smart contracts are not audited before compiling on an immutable blockchain, it may result in losses of millions. Therefore, there is a compelling need for efficient and effective measures to ensure the robustness and genuineness of smart contracts. In this paper, we propose a Homogeneous Graph Machine Learning Algorithm for Reentrancy attacks DEtection using Graph Convolution Networks (HARDEN). Reentrancy attacks are one of the most infamous vulnerabilities in smart contracts. The experimental results are encouraging and validate the feasibility of applying a Deep Learning (DL) approach to detect vulnerabilities in smart contracts. We hope this preliminary study will inspire more interest and discussions in the raising area.