A New Approach to Using Semantically Rich Functions for Vulnerability Detection Tasks in Smart Contracts

Abstract

Smart contracts are software programmes that are maintained in a decentralised ledger and perform predefined actions based on specific transactions directed towards them. Smart contracts are often coded using the Solidity programming language, which adheres to a certain structure and grammar. The programming language was specifically designed and implemented for the Ethereum platform. Languages with a well-defined structure are susceptible to certain vulnerabilities, the exploitation of which might result in significant financial ramifications. This study employs a Deep Learning (DL) model for the purpose of identifying vulnerabilities. By employing a selected methodology and accurately defining the structure of input data, it becomes feasible to identify intricate interdependencies among programme variables that harbour vulnerabilities and problems. Through the utilisation of carefully designed experiments, this methodology was examined in order to get a deeper comprehension of the model and enhance its overall efficacy. The model that was constructed utilised the Solidity corpus of smart-contracts as input data to classify vulnerabilities at the string level. The utilisation of the deep learning model enables the detection of vulnerabilities with different levels of complexity in smart contracts.