cross-chain-smart-contract-vulnerability-detection-methods-slr
๐ A systematic literature review of cross-chain smart contract vulnerability detection methods
https://github.com/andstor/cross-chain-smart-contract-vulnerability-detection-methods-slr
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๐ A systematic literature review of cross-chain smart contract vulnerability detection methods
Basic Info
- Host: GitHub
- Owner: andstor
- Language: TeX
- Default Branch: master
- Homepage: https://github.com/andstor/cross-chain-smart-contract-vulnerability-detection-methods-slr/blob/master/main.pdf
- Size: 15.4 MB
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Metadata Files
README.md
Cross-chain Smart Contract Vulnerability Detection Methods: A Systematic Literature Review
Abstract
Vulnerability detection and security of Smart Contracts are of paramount importance because of their immutable nature. A Smart Contract is a program stored on a blockchain that runs when some predetermined conditions are met. While smart contracts have enabled a variety of applications on the blockchain, they may pose a significant security risk. Once a smart contract is deployed to a blockchain, it cannot be changed. It is therefore imperative that all bugs and errors are pruned out before deployment. With the increase in studies on Smart Contract vulner- ability detection tools and methods, it is important to systematically review the state-of-the-art tools and methods. This, to classify the existing solutions, as well as identify gaps and challenges for future research. In this Systematic Literature Review (SLR), a total of 125 papers on Smart Contract vulnerability analysis and detection methods and tools were retrieved. These were then filtered based on predefined inclusion and exclusion criteria. Snowballing was then applied. A total of 40 relevant papers were selected and analyzed. The vulnerability detection tools and methods were classified into six categories: Symbolic execution, Syntax analysis, Abstract interpretation, Data flow analysis, Fuzzing test, and Machine learning. This SLR provides a broader scope than just Ethereum. Thus, the cross-chain applicability of the tools and methods were also evaluated. Cross-chain vulnerability detection is in this SLR defined as a method for detecting vulnerabilities in Smart Contract code that can be applied for multiple blockchains. The results of this study show that there are many highly accurate tools and methods available for Smart Contract (SC) vulnerability detection. Especially Machine Learning has in recent years drawn much attention from the research community. However, little effort has been invested in Smart Contract vulnerability detection on other chains.
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Owner
- Name: Andrรฉ Storhaug
- Login: andstor
- Kind: user
- Location: Trondheim ๐ณ๐ด
- Company: NTNU
- Website: https://andre.storhaug.no
- Repositories: 87
- Profile: https://github.com/andstor
๐ CS PhD student @ Norwegian University of Science and Technology (NTNU)
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Storhaug"
given-names: "Andrรฉ"
orcid: "https://orcid.org/0000-0002-5321-7196"
title: "cross-chain-smart-contract-vulnerability-detection-methods-slr"
version: 1.0.0
date-released: 2022-01-19
url: "https://github.com/andstor/cross-chain-smart-contract-vulnerability-detection-methods-slr"
preferred-citation:
type: article
authors:
- family-names: "Storhaug"
given-names: "Andrรฉ"
orcid: "https://orcid.org/0000-0002-5321-7196"
affiliation: "Norwegian University of Science and Technology (NTNU)"
abstract: "Vulnerability detection and security of Smart Contracts are of paramount importance because of their immutable nature. A Smart Contract is a program stored on a blockchain that runs when some predetermined conditions are met. While smart contracts have enabled a variety of applications on the blockchain, they may pose a significant security risk. Once a smart contract is deployed to a blockchain, it cannot be changed. It is therefore imperative that all bugs and errors are pruned out before deployment. With the increase in studies on Smart Contract vulner- ability detection tools and methods, it is important to systematically review the state-of-the-art tools and methods. This, to classify the existing solutions, as well as identify gaps and challenges for future research. In this Systematic Literature Review (SLR), a total of 125 papers on Smart Contract vulnerability analysis and detection methods and tools were retrieved. These were then filtered based on predefined inclusion and exclusion criteria. Snowballing was then applied. A total of 40 relevant papers were selected and analyzed. The vulnerability detection tools and methods were classified into six categories: Symbolic execution, Syntax analysis, Abstract interpretation, Data flow analysis, Fuzzing test, and Machine learning. This SLR provides a broader scope than just Ethereum. Thus, the cross-chain applicability of the tools and methods were also evaluated. Cross-chain vulnerability detection is in this SLR defined as a method for detecting vulnerabilities in Smart Contract code that can be applied for multiple blockchains. The results of this study show that there are many highly accurate tools and methods available for Smart Contract (SC) vulnerability detection. Especially Machine Learning has in recent years drawn much attention from the research community. However, little effort has been invested in Smart Contract vulnerability detection on other chains."
title: "Cross-chain Smart Contract Vulnerability Detection Methods: A Systematic Literature Review"
url: "https://github.com/andstor/cross-chain-smart-contract-vulnerability-detection-methods-slr/blob/master/main.pdf"
month: 12
year: 2021
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