Smart contract: a survey towards extortionate vulnerability detection and security enhancement

Porkodi, S.; Kesavaraja, D.

doi:10.1007/s11276-023-03587-z

Cited by 3 publications

(7 citation statements)

References 65 publications

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“…2: How is blockchain used to improve cyber security? 3: What methods are available for blockchain solutions to manage securi [13] 1: What are the static analysis tools available for Ethereum blockchain SC Electronics 2024, 13, x FOR PEER REVIEW [34] Technical aspect, future directions survey 2021 n [4] Difficulties and future trends review 2022 n [13] Analysis tools SLR 2022 se [35] Vulnerability detection and machine learning survey 2022 n [36] Vulnerability detection tools within Web 3.0 survey 2023 n [14] Security threats, collaborative defense review 2023 n [37] Vulnerability detection survey 2023 n [38] Platforms, applications, and challenges review 2023…”

Section: Referencementioning

confidence: 99%

“…4 Keywords are not mentioned in the methodology. Security threats, collaborative defense review 2023 not clear 3 2018-2023 [37] Vulnerability detection survey 2023 not clear 3 Until 2023 [38] Platforms, applications, and challenges review 2023 clear 1  2015-May 2020 1 The identification, exclusion, eligibility, and inclusion methodology are clear. 2 The number of selected papers is not clear.…”

Section: Referencementioning

confidence: 99%

“…4: What are the solutions to the identified challenges? [35] Vulnerability detection and machine learning survey 2022 not clear 3 Until 2021 [36] Vulnerability detection tools within Web 3.0 survey 2023 not clear 3  2011-2023 [14] Security threats, collaborative defense review 2023 not clear 3 2018-2023 [37] Vulnerability detection survey 2023 not clear 3 Until 2023 [38] Platforms, applications, and challenges review 2023 clear 1  2015-May 2020 1 The identification, exclusion, eligibility, and inclusion methodology are clear. 2 The number of selected papers is not clear.…”

Section: Referencementioning

confidence: 99%

“…Furthermore, the survey [37] explores vulnerability detection and security enhancement. The method of article selection in their study has not been clearly revealed.…”

Section: Referencementioning

confidence: 99%

“…[32] 1: What are the latest blockchain applications focused 2: How is blockchain used to improve cyber security? 3: What methods are available for blockchain solution [13] 1: What are the static analysis tools available for Ether Vulnerability detection and machine learning survey [36] Vulnerability detection tools within Web 3.0 survey [14] Security threats, collaborative defense review [37] Vulnerability detection survey [38] Platforms, applications, and challenges review 1…”

Section: Table 6 Research Questions Followmentioning

confidence: 99%

See 4 more Smart Citations

Ethereum Smart Contract Vulnerability Detection and Machine Learning-Driven Solutions: A Systematic Literature Review

Kiani,

Sheng

2024

Electronics

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In recent years, emerging trends like smart contracts (SCs) and blockchain have promised to bolster data security. However, SCs deployed on Ethereum are vulnerable to malicious attacks. Adopting machine learning methods is proving to be a satisfactory alternative to conventional vulnerability detection techniques. Nevertheless, most current machine learning techniques depend on sufficient expert knowledge and solely focus on addressing well-known vulnerabilities. This paper puts forward a systematic literature review (SLR) of existing machine learning-based frameworks to address the problem of vulnerability detection. This SLR follows the PRISMA statement, involving a detailed review of 55 papers. In this context, we classify recently published algorithms under three different machine learning perspectives. We explore state-of-the-art machine learning-driven solutions that deal with the class imbalance issue and unknown vulnerabilities. We believe that algorithmic-level approaches have the potential to provide a clear edge over data-level methods in addressing the class imbalance issue. By emphasizing the importance of the positive class and correcting the bias towards the negative class, these approaches offer a unique advantage. This unique feature can improve the efficiency of machine learning-based solutions in identifying various vulnerabilities in SCs. We argue that the detection of unknown vulnerabilities suffers from the absence of a unique definition. Moreover, current frameworks for detecting unknown vulnerabilities are structured to tackle vulnerabilities that exist objectively.

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Section: Referencementioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Section: Referencementioning

confidence: 99%

“…Furthermore, the survey [37] explores vulnerability detection and security enhancement. The method of article selection in their study has not been clearly revealed.…”

Section: Referencementioning

confidence: 99%