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Design and Analysis for Fall Detection System Simplification
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使用机器学习方法将软件安全要求分为机密性,完整性和可用性
1Information Technology, Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
PeerJ. Computer science
|December 9, 2024
概括
这项研究将软件安全要求的分类自动化为保密性,完整性和可用性 (CIA) 三位一体. 具有句子转换器嵌入的支持向量机实现了87%的准确性,改善了要求可追溯性.
科学领域:
- 软件工程 软件工程 软件工程
- 信息安全 信息安全
- 自然语言处理自然语言处理.
背景情况:
- 安全要求是关键的非功能软件要求,通常基于机密性,完整性和可用性 (CIA) 三位一体.
- 基于自然语言的要求可能含糊不清,因此很难区分不同的安全维度.
- 自动化安全要求的分类有助于追踪和确保实施.
研究的目的:
- 提出和评估自动将安全要求分类到中央情报局三元组的方法.
- 为了比较功能提取技术 (TF-IDF和句子转换器嵌入) 与机器学习算法相结合.
主要方法:
- 为每个特征提取技术开发了五种机器学习模型 (SVM,KNN,RF,GB,BNB).
- 使用术语频率-反向文档频率 (TF-IDF) 和句子转换器嵌入功能提取.
- 创建了一个网络接口,用于实时分析和分类安全要求.
主要成果:
- 支持矢量机 (SVM) 模型与句子转换器嵌入相结合,达到87%的最高准确率.
- 与其他模型相比,这种方法在预测要求的安全维度方面表现优越.
- TF-IDF和其他机器学习算法表现出不同程度的成功,但SVM-句子-变压器组合的表现优于它们.
结论:
- 将安全要求自动分类为中央情报局三元组是可行的,对软件工程有好处.
- 句子转换器嵌入与SVM相结合,为此分类任务提供了一个高度准确的方法.
- 开发的系统可以提高安全要求管理的效率和可靠性.
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