When Deep Learning Meets Smart Contracts

TL;DR

This paper introduces a deep learning approach for analyzing Solidity smart contracts, enabling clone detection, bug identification, and validation to improve security and reliability in blockchain transactions.

Contribution

The paper presents a novel deep learning-based method for smart contract analysis, including a web tool, SmartEmbed, for real-time clone and bug detection in Solidity contracts.

Findings

High clone ratio (~90%) in Solidity code compared to traditional software.

The approach detects over 1000 clone-related bugs efficiently.

SmartEmbed aids developers in identifying repetitive contracts and known vulnerabilities.

Abstract

Ethereum has become a widely used platform to enable secure, Blockchain-based financial and business transactions. However, many identified bugs and vulnerabilities in smart contracts have led to serious financial losses, which raises serious concerns about smart contract security. Thus, there is a significant need to better maintain smart contract code and ensure its high reliability. In this research: (1) Firstly, we propose an automated deep learning based approach to learn structural code embeddings of smart contracts in Solidity, which is useful for clone detection, bug detection and contract validation on smart contracts. We apply our approach to more than 22K solidity contracts collected from the Ethereum blockchain, results show that the clone ratio of solidity code is at around 90%, much higher than traditional software. We collect a list of 52 known buggy smart contracts belonging to 10 kinds of common vulnerabilities as our bug database. Our approach can identify more than 1000 clone related bugs based on our bug databases efficiently and accurately. (2) Secondly, according to developers' feedback, we have implemented the approach in a web-based tool, named SmartEmbed, to facilitate Solidity developers for using our approach. Our tool can assist Solidity developers to efficiently identify repetitive smart contracts in the existing Ethereum blockchain, as well as checking their contract against a known set of bugs, which can help to improve the users' confidence in the reliability of the contract. We optimize the implementations of SmartEmbed which is sufficient in supporting developers in real-time for practical uses. The Ethereum ecosystem as well as the individual Solidity developer can both benefit from our research.