Detecting Unknown Vulnerabilities in Smart Contracts with Multi-Label Classification Model Using CNN-BiLSTM

Abstract

AbstractSmart contracts are frequently targeted by hackers because they hold large amounts of money and cannot be modified once they are published. Existing detection methods mainly focus on known vulnerabilities with clear features and cannot deal with unknown vulnerabilities. As a consequence, proposing a method for detecting unknown vulnerabilities in smart contracts represents a significant advancement in the field of smart contract security. Aiming at this problem, based on the idea that the opcode sequences of transactions containing unknown vulnerabilities have similarities to the opcode sequences of transactions containing known vulnerabilities, we propose a novel approach for unknown vulnerability detection in smart contracts using a CNN-BiLSTM model. Our model determines whether a vulnerability is unknown by detecting the opcode sequence representing the entire execution process of a transaction. Experimental results with the opcode sequences of transactions show that the model can achieve 82.86% accuracy and 83.63% F1-score.KeywordsSmart contractsUnknown vulnerabilitiesVulnerability detectionCNN-BiLSTMOpcode sequences