Low-code vulnerability identification based on TextCNN

Yuqiong Wang, Yuxiao Zhao, Xiang Wang, Weidong Tang, Jinhui Zhang, Zhaojie Yang, Peng Wang, Jian Hu
{"title":"Low-code vulnerability identification based on TextCNN","authors":"Yuqiong Wang, Yuxiao Zhao, Xiang Wang, Weidong Tang, Jinhui Zhang, Zhaojie Yang, Peng Wang, Jian Hu","doi":"10.1117/12.3031890","DOIUrl":null,"url":null,"abstract":"Vulnerability identification is a crucial quality assurance step in software engineering, dedicated to discovering and handling potential errors and abnormal behavior in source code. Most vulnerability detection methods are designed for conventional programming languages. With the widespread adoption of low-code development, there is a need for a vulnerability detection method specifically tailored to low-code environments. Thus, we present a robust low-code vulnerability identification model by integrating Convolutional Neural Network Text Classification (TextCNN) and an attention mechanism. The resulting model is capable of recognizing potential irregular patterns in the low code, assisting developers in promptly identifying and addressing potential software defects. It holds significant importance in enhancing the maintainability, stability, and security of the system. Simultaneously, it offers substantial support for the company's software development efforts and mitigates the risk of software defects. The experimental results demonstrate that the method in this paper can achieve accurate low-code vulnerability identification.","PeriodicalId":198425,"journal":{"name":"Other Conferences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Other Conferences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.3031890","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Vulnerability identification is a crucial quality assurance step in software engineering, dedicated to discovering and handling potential errors and abnormal behavior in source code. Most vulnerability detection methods are designed for conventional programming languages. With the widespread adoption of low-code development, there is a need for a vulnerability detection method specifically tailored to low-code environments. Thus, we present a robust low-code vulnerability identification model by integrating Convolutional Neural Network Text Classification (TextCNN) and an attention mechanism. The resulting model is capable of recognizing potential irregular patterns in the low code, assisting developers in promptly identifying and addressing potential software defects. It holds significant importance in enhancing the maintainability, stability, and security of the system. Simultaneously, it offers substantial support for the company's software development efforts and mitigates the risk of software defects. The experimental results demonstrate that the method in this paper can achieve accurate low-code vulnerability identification.
基于 TextCNN 的低代码漏洞识别
漏洞识别是软件工程中一个重要的质量保证步骤,致力于发现和处理源代码中的潜在错误和异常行为。大多数漏洞检测方法都是针对传统编程语言设计的。随着低代码开发的广泛采用,需要一种专门针对低代码环境的漏洞检测方法。因此,我们通过整合卷积神经网络文本分类(TextCNN)和注意力机制,提出了一种稳健的低代码漏洞识别模型。由此产生的模型能够识别低代码中潜在的不规则模式,帮助开发人员及时发现并解决潜在的软件缺陷。它对提高系统的可维护性、稳定性和安全性具有重要意义。同时,它还能为公司的软件开发工作提供实质性支持,并降低软件缺陷的风险。实验结果表明,本文中的方法可以实现准确的低代码漏洞识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信