Smart Contracts Vulnerability Analysis Pdf To address this, our study set out to systematically review the existing body of work, analysing 21 reviewed studies published between 2020 and 2024. A vulnerability detection framework for hyperledger fabric smart contracts based on dynamic and static analysis. in proceedings of the 26th in ternational conference on evaluation and assessment in software engineering, pages 366–374, 2022.
Pdf Vulnerability Detection In Ethereum Smart Contracts Via Machine Abstract—we systematically study 502 unique real world smart contract vulnerabilities exploits in years 2021 2022. we study how many of them can be exploited by malicious users and cannot be detected by existing analysis tools. In this section, we present related work on the detection of smart contract vulnerabilities, focusing primarily on static analysis techniques and deep learning methods. In this paper, we present a systematic survey of vulnerability analysis of smart contracts. we begin by providing a brief about the major types of attacks and vulnerabilities that are present in smart contracts. Existing methods on smart contract vulnerability detection heavily rely on fixed expert rules, leading to low detection accuracy. in this paper, we explore using graph neural networks (gnns) for smart contract vulnerability detection.
Pdf Identifying Vulnerabilities In Smart Contracts Using Interval In this paper, we present a systematic survey of vulnerability analysis of smart contracts. we begin by providing a brief about the major types of attacks and vulnerabilities that are present in smart contracts. Existing methods on smart contract vulnerability detection heavily rely on fixed expert rules, leading to low detection accuracy. in this paper, we explore using graph neural networks (gnns) for smart contract vulnerability detection. Table 3 presents a comparative overview of various smart contract vulnerability datasets, highlighting differences in size, labeling methods, contract sources, use cases, and types of vulnerabilities covered in the literature. This paper discusses the application of ai driven techniques for vulnerability detection in smart contracts, highlighting the limitations of traditional auditing methods. These factors, combined with the fact that most smart contracts involve high value cryptocurrency transactions, have led to frequent security incidents (e.g., the dao, parity wallet attacks) and bil lions of dollars in losses, making smart contract vulnerability detection a major research hotspot in both academia and industry [5]. In this paper, we present a systematic literature review on the state of the art in smart contract vulnerability detection research.
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