An Attention-based Wide and Deep Neural Network for Reentrancy Vulnerability Detection in Smart Contracts

Abstract

In recent years, smart contracts have become integral to blockchain applications, offering decentralized, transparent, and tamper-proof execution of agreements. However, vulnerabilities in smart contracts pose significant security risks, leading to financial losses. This paper presents an Attention-based Wide and Deep Neural Network (AWDNN) for Reentrancy vulnerability Detection in Ethereum smart contracts. By emphasizing crucial smart contract features, AWDNN enhances its precision in identifying complex vulnerability patterns. Our approach includes three phases: code optimization, vectorization, and vulnerability detection. We streamline smart contract code by removing extraneous components and extracting key fragments. These fragments are transformed into vectors that capture the smart contract’s semantic features, and subsequently subjected through the wide and deep neural network to detect vulnerabilities. Experimental results show that our model performs well compared to existing tools. Future work aims to detect additional vulnerabilities and incorporate advanced vectorization techniques to enhance efficiency.