A Survey on Vulnerability Detection Tools of Smart Contract Bytecode

Xu, Junzhou; Dang, Fan; Ding, Xuan; Zhou, Min

doi:10.1109/iciscae51034.2020.9236931

Cited by 9 publications

(7 citation statements)

References 4 publications

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“…From start to finish, the transaction requires a certain amount of time. If a transaction to change the contract is conducted within this time frame and the changed transaction is confirmed first, the transaction that was initiated first would be impacted [42] [43]. In fact, this dependence on transaction order is a big issue; for example, a vendor can adjust the price after a customer has bought something, causing the buyer to pay more without their permission.…”

Section: Transaction Ordering Dependency (Tod)mentioning

confidence: 99%

“…Hackers can take advantage of this in lottery contracts to improve their winning rate. Most tools only detect timestamp dependencies in these forms of vulnerabilities [42] [44] [45].…”

Section: Predictable Random Number (Prn)mentioning

confidence: 99%

“…In most smart contract languages, the integer form, such as i64 in web-assembly and in Solidity's uint256, has a limited range. If the value of an integer attribute is higher than the range, it would be adjusted to fit within the range, resulting in incorrect performance [42].…”

Section: Integer Overflowmentioning

confidence: 99%

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A Vulnerability Detection Approach for Automated Smart Contract Using Enhanced Machine Learning Techniques

Sosu

Chen

Brown-Acquaye

et al. 2022

Preprint

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A typical contract would necessitate the use of an intermediary, make a payment to them, then wait for the record to return. Through a smart contract, though, it is as easy as putting the bitcoin into the vending machine, and the products would be published instantly. Smart contracts are Blockchain-based autonomous software. On Ethereum, a vast number of smart contracts have been deployed. Meanwhile, transaction security vulnerabilities have resulted in significant financial damages and have harmed the contract layer’s ecological integrity on Blockchain. As a result, detecting contract bugs reliably and efficiently is a new yet critical problem. Currently, available identification approaches, such as Oyente and Securify, depend primarily on symbolic execution or analysis. Since symbolic execution necessitates the discovery of all executable routes or the study of dependence diagrams in a contract, these approaches are time-consuming. In this paper, we suggest SmartPol, a machine learning-based method for detecting vulnerabilities in smart contracts. First, we use a data pre-processor to clean up the data before extracting features and classifying them. Second, we present a PSOGSA-based method for data optimisation and function extraction and a TSVM-based approach for semi-supervised learning classification models to identify smart contract vulnerabilities automatically. On EtherScan, SmartPol was used to test 49512 real-world smart contracts. The experimental findings show SmartPol’s reliability and efficacy. When we use PSOGSA for function extraction and TSVM classification sets, SmartPol’s predictive precision and accuracy are over 96%, and the average prediction time is 4 seconds on each smart contract.

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Section: Transaction Ordering Dependency (Tod)mentioning

confidence: 99%

“…Hackers can take advantage of this in lottery contracts to improve their winning rate. Most tools only detect timestamp dependencies in these forms of vulnerabilities [42] [44] [45].…”

Section: Predictable Random Number (Prn)mentioning

confidence: 99%

See 1 more Smart Citation

A Vulnerability Detection Approach for Automated Smart Contract Using Enhanced Machine Learning Techniques

Sosu

Chen

Brown-Acquaye

et al. 2022

Preprint

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“…Previous studies [18,19] proposed the approaches of machine learning, artificial intelligence, and deep learning integrated within vulnerability detection for smart contracts. In these studies, the fundamental approach involves building a system that can automatically evolve into more effectively detecting vulnerabilities in smart contracts.…”

Section: E Ai-driven Approachmentioning

confidence: 99%

“…6. AI architecture for Smart Contract Vulnerability Detection [19] This architecture in Fig. 6 consists of using machine learning algorithm into training an artificial intelligence (AI) model that can understand what smart contracts are and the vulnerabilities associated to them.…”

Section: E Ai-driven Approachmentioning

confidence: 99%

Detecting Vulnerabilities in Smart Contract within Blockchain: A Review and Comparative Analysis of Key Approaches

Kissoon

Bekaroo

2022

2022 3rd International Conference on Next Generation Computing Applications (NextComp)

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Detecting functional and security‐related issues in smart contracts: A systematic literature review

et al. 2022

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Blockchain is a platform of distributed elaboration, which allows users to provide software for a huge range of next-generation decentralized applications without involving reliable third parties. Smart contracts (SCs) are an important component in blockchain applications: they are programmatic agreements among two or more parties that cannot be rescinded. Furthermore, SCs have an important characteristic: they allow users to implement reliable transactions without involving third parties. However, the advantages of SCs have a price. Like any program, SCs can contain bugs, some of which may also constitute security threats. Writing correct and secure SCs can be extremely difficult because, once deployed, they cannot be modified. Although SCs have been recently introduced, a large number of approaches have been proposed to find bugs and vulnerabilities in SCs. In this article, we present a systematic literature review on the approaches for the automated detection of bugs and vulnerabilities in SCs. We survey 68 papers published between 2015 and 2020, and we annotate each paper according to our classification framework to provide quantitative results and find possible areas not explored yet. Finally, we identify the open problems in this research field to provide possible directions to future researchers.

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A Survey on Vulnerability Detection Tools of Smart Contract Bytecode

Cited by 9 publications

References 4 publications

A Vulnerability Detection Approach for Automated Smart Contract Using Enhanced Machine Learning Techniques

A Vulnerability Detection Approach for Automated Smart Contract Using Enhanced Machine Learning Techniques

Detecting Vulnerabilities in Smart Contract within Blockchain: A Review and Comparative Analysis of Key Approaches

Detecting functional and security‐related issues in smart contracts: A systematic literature review

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