Smart Contract Vulnerability Detection Based on Clustering Opcode Instructions

Abstract

Smart contracts are programs running on the blockchain. In recent years, due to the continuous occurrence of smart contract security accidents, how to effectively detect vulnerabilities in smart contracts has received extensive attention. Machine learning-based vulnerability detection techniques have the advantage of not requiring expert rules. However, existing approaches have limitations in identifying vulnerabilities caused by version updates of smart contract compilers. In this paper, we propose OC-Detector, a smart contract vulnerabilities detection approach based on opcode instruction clustering. OC-Detector learns the characteristics of opcode instructions to cluster them and replaces opcode instructions belonging to the same cluster with the cluster number. After that, the similarity is calculated against the contract in the vulnerability database to identify vulnerabilities. Experimental results demonstrate that OC-Detector improves the F 1 value of detecting vulnerabilities from 0.04 to 0.40 compared to DC-Hunter, Securify, SmartCheck, and Osiris. Additionally, compared to DC-Hunter, F 1 value is improved by 0.27 when detecting vulnerabilities in smart contracts compiled by different version compilers.