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Due to the lack of unified evaluation standards, the security analysis of smart contracts mainly relies on complex and not easily scalable expert rules. To address these issues, we employ slicing techniques to reduce the interference of extraneous code on the detection process, apply normalisation techniques to eliminate the differences between different compiler versions and use particle swarm optimisation algorithms to determine the similarity between contracts, thus improving the accuracy and efficiency of detection. In addition, we combine a variety of features such as static analysis, dynamic analysis and symbolic execution to gain a more comprehensive understanding of contract characteristics and behaviours for more accurate vulnerability identification. Experimental results show that the scheme significantly improves the detection capability and provides a new solution for the security detection of smart contracts.<\/p>","DOI":"10.4018\/ijswis.342850","type":"journal-article","created":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T12:07:39Z","timestamp":1714651659000},"page":"1-33","source":"Crossref","is-referenced-by-count":0,"title":["Particle Swarm Algorithm for Smart Contract Vulnerability Detection Based on Semantic Web"],"prefix":"10.4018","volume":"20","author":[{"given":"Tao","family":"Feng","sequence":"first","affiliation":[{"name":"School of Computer and Communication, Lanzhou University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyang","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Computer and Communication, Lanzhou University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJSWIS.342850-0","first-page":"1","article-title":"The monarch butterfly optimization algorithm for solving feature selection problems.","volume":"34","author":"M.Alweshah","year":"2020","journal-title":"Neural Computing & Applications"},{"key":"IJSWIS.342850-1","doi-asserted-by":"publisher","DOI":"10.4018\/IJSSCI.300359"},{"key":"IJSWIS.342850-2","doi-asserted-by":"publisher","DOI":"10.1109\/SANER50967.2021.00050"},{"key":"IJSWIS.342850-3","unstructured":"Datar, M., Altman, E., De Pellegrini, F., El Azouzi, R., & Touati, C. 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