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Improving Software Security Through a LLM-Based Vulnerability Detection Model

delete2026-01-01
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AI
S
Syeda Sadia Alam
M
Mst Shapna Akter
A
Alfredo Cuzzocrea *
DOI：10.1007/978-3-032-02049-9_9delete
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摘要

摘要

En 中文
早期漏洞识别在确保软件开发过程中安全的重要性不容否认。本研究中，我们介绍了CWEpredBELL，一种独特的自动化漏洞预测方法，该方法利用源自CodeBERT的改进预训练语言模型。通过二元分类层、改进的优化器和微调的损失函数来提升模型性能，我们的方法特别适用于识别源代码中的漏洞。我们采用交叉验证技术和可解释的模型无关解释（LIME）方法来识别源代码中的具体错误行。实验比较表明，CWEpredBELL是自动识别漏洞的有效方法。
Keyword:
Vulnerability Identification
CodeBert
Cybersecurity
SWE
LLM
NLP
LIME

期刊

D
DATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2025, PT I
IF:
0
论文数:
25
被引数:
0

机构

Metropolitan University 封面图
Metropolitan University
学者数:
18
论文数: 13
被引数: 28
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