A new scheme of vulnerability analysis in smart contract with machine learning

Xing, Cipai; Chen, Zhuorong; Chen, Lexin; Guo, Xiaojie; Zheng, Zibin; Jin, Li

doi:10.1007/s11276-020-02379-z

Cited by 38 publications

(22 citation statements)

References 18 publications

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“…, 3} for any vertex in R) by inserting a new vertex of degree 2 into each edge of R, and by adding a new pendant edge to each leaf of R. The collection of such {1, 2, 3}-trees T R deduced from all {1, 3}-tree R is denoted by (3)  .…”

Section: More Results On ℋ-Factorsmentioning

confidence: 99%

“…graph modelling, isolated toughness, (P ≥3 , m)-factor deleted graph, isolated toughness (P ≥3 , m) factor deleted surface during the stage of network designing. It always requires the network structure to satisfy certain parameter bounds to ensure the networks with higher stand-up and less vulnerability (for more recent results on cyber security and related analysis, see Xu et al, 2 Xing et al, 3 Li et al, 4 Samira et al, 5 and Jaco et al 6 ).…”

Section: Introductionmentioning

confidence: 99%

“…-factor deleted graphs will change as well. Therefore, we propose a new perspective to look into this problem and introduce the concepts of isolated toughness ≥ P m ( , ) 3 factor deleted surface and isolated toughness variant…”

mentioning

confidence: 99%

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Network vulnerability parameter and results on two surfaces

2021

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Isolation toughness is a vital parameter to evaluate the vulnerability of computer networks. In specific network designing stage, it is necessary to find the lower bound of the isolated toughness, and strive to build a network that meets the stability requirements with the least cost. Gao et al.1 conjectured that if a graph G with κ ( G ) ≥ 3 m + 1 2 satisfies I ( G ) > 7 m + 5 4 m + 4 or I ′ ( G ) > 7 m + 5 4 m + 2 , then G is a ( P ≥ 3 , m ) ‐factor deleted graph. It's proved that this conjecture holds. However, it is found that as the connectivity changes, the tight lower bound of isolated toughness for ( P ≥ 3 , m ) ‐factor deleted graphs will change as well. Therefore, we propose a new perspective to look into this problem and introduce the concepts of isolated toughness ( P ≥ 3 , m ) factor deleted surface and isolated toughness variant ( P ≥ 3 , m ) factor deleted surface, where the result of the original conjecture is only a cross‐section on surfaces. The main contribution in this paper is to determine the concrete expression of these two surfaces.

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Section: More Results On ℋ-Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Network vulnerability parameter and results on two surfaces

2021

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“…An increasing number of practitioners indicate that smart contract vulnerability is hidden when blockchain and smart contracts are implemented in the manufacturing supply chain operational environment. Therefore, more researchers, such as He et al (2020) and Xing et al (2020), provide practical approaches to detecting and protecting smart contract vulnerability. However, according to our test results, if the vulnerability of smart contracts persists, its negative influence will expand gradually and further weaken the relationship between partners' trust and increasing visibility.…”

Section: High Smart Contractmentioning

confidence: 99%

Influence of Blockchain and Smart Contracts on Partners’ Trust, Visibility, Competitiveness, and Environmental Performance in Manufacturing Supply Chains

Chen

Chu³

2022

Journal of Business Economics and Management

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This study investigates the influence of blockchain and smart contracts on partners’ trust, increasing visibility, competitiveness, and environmental performance when implemented in manufacturing supply chains. Partial least squares (PLS) was the main method used to test and verify the research model and hypotheses. According to the test results, blockchain and smart contracts positively influence partners’ trust. Increasing trust produces a positive effect on increasing visibility, further promoting competitiveness and environmental performance. However, the vulnerability of smart contracts still causes information security concerns. If smart contracts are adopted for an extended time, partners will worry about problems of information vulnerability stemming from the smart contract, further reducing trust and affecting visibility. Regarding academic implications, this study not only proves the relationship between blockchain, smart contracts, partners’ trust, increasing visibility, competitiveness, and environmental performance. In addition, we also proved the vulnerability of smart contracts; our test results fill these gaps. Regarding practical implications, with extended contract periods, partners may expand their doubt in the smart contract and reduce information sharing, further affecting visibility. Manufacturers are reminded that smart contracts may carry hidden trouble that disrupts the relationship between partners’ trust and visibility.

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“…• The two methods introduced earlier failed to consider the impact of local code vulnerabilities, resulting in poor interpretability of these methods. The slice matrix proposed by Xing et al 121 is helpful to solve this problem. Slice matrix is a new method of extracting vulnerability features.…”

Section: Deep Learningmentioning

confidence: 99%

Security enhancement technologies for smart contracts in the blockchain: A survey

Wang

Zhu

et al. 2021

Trans Emerging Tel Tech

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Smart contract has shown its potential in cutting down the cost of administration through reshaping conventional business processes and in expanding the application of blockchain to areas that are beyond the cryptocurrency. However, with the rapid development and wide application of smart contracts, security issues have become a serious concern and have thus attracted widespread attention. As the result, a great deal of effort has been spent on improving and supporting secure development and on the application of smart contracts by introducing new and advanced vulnerability detection and privacy protection techniques in recent years. There is therefore the need for a comprehensive review of the new development on security enhancement technologies of smart contracts for the blockchain. This paper provides a review of the current research status and advances in smart contract security based on related literature published in recent years. Our review is divided into six categories along the line of the technology, which includes symbolic execution, abstract interpretation, fuzz testing, formal verification, deep learning, and privacy enhancement. A comparison of the various tools and methods developed to tackle security issues is provided. Challenges in the research of smart contract security as well as future directions are discussed. This paper intends to provide the inspiration and reference for follow-up research on the security issues of smart contracts in the blockchain.

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A new scheme of vulnerability analysis in smart contract with machine learning

Cited by 38 publications

References 18 publications

Network vulnerability parameter and results on two surfaces

Network vulnerability parameter and results on two surfaces

Influence of Blockchain and Smart Contracts on Partners’ Trust, Visibility, Competitiveness, and Environmental Performance in Manufacturing Supply Chains

Security enhancement technologies for smart contracts in the blockchain: A survey

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