Design and Implementation of System of the Web Vulnerability Detection Based on Crawler and Natural Language Processing | |
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Author | |
Abstract |
Natural Language Processing - Application code analysis and static rules are the most common methods for Web vulnerability detection, but this process will generate a large amount of contaminated data and network pressure, the false positive rate is high. This study implements a detection system on the basis of the crawler and NLP. The crawler visits page in imitation of a human, we collect the HTTP request and response as dataset, classify the dataset according to parameter characteristic and whether the samples get to interact with a database, then we convert text word vector, reduce the dimension and serialized them, through train dataset by NLP algorithm, finally we obtain a model that can accurately predict Web vulnerabilities. Experimental results show that this method can detect Web vulnerabilities efficiently, greatly reduce invalid attack test parameters, and reduce network pressure. |
Year of Publication |
2022
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Date Published |
may
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Publisher |
IEEE
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Conference Location |
Okinawa, Japan
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ISBN Number |
978-1-66548-284-4
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URL |
https://ieeexplore.ieee.org/document/9939264/
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DOI |
10.1109/ICINT55083.2022.00018
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