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一个神经模糊的安全风险评估系统,用于软件开发生命周期
Olayinka Olufunmilayo Olusanya1, Rasheed Gbenga Jimoh2, Sanjay Misra3,4
1Department of Computer Science, Tai Solarin University of Education, Ijagun, Ogun State, Nigeria.
Heliyon
|July 22, 2024
概括
本研究介绍了使用自适应神经模糊推理系统 (ANFIS) 的软件风险评估 (SRA) 模型,以提高软件开发生命周期 (SDLC) 所有阶段的软件开发安全性.
科学领域:
- 软件工程 软件工程 软件工程
- 人工智能的人工智能
- 风险管理 风险管理
背景情况:
- 软件开发在其整个生命周期中容易受到各种风险的影响.
- 现有的风险评估方法可能无法充分解决特定阶段的漏洞.
- 确保软件开发生命周期 (SDLC) 对于强大的软件至关重要.
研究的目的:
- 为每个SDLC阶段开发一个软件风险评估 (SRA) 模型.
- 使用适应性神经模糊推理系统 (ANFIS) 进行风险建模.
- 通过特定阶段的风险评估,提高软件开发过程的安全性.
主要方法:
- 为每个SDLC阶段确定和验证的风险变量.
- 收集有关风险因素和相关SRA的数据.
- 开发了基于ANFIS的SRA模型,风险因素作为输入,SRA作为输出.
- 训练和测试模型使用70-80%的训练数据和20-30%的测试数据.
主要成果:
- 在SDLC阶段发现并证实了许多风险变量:要求 (11),设计 (8),实施 (9),集成 (4),操作 (6).
- 制定了ANFIS模型,每个阶段都有不同的推断规则:要求 (2048),设计 (256),实施 (512),集成 (16),运行 (64).
- 基于使用测试数据集的准确性评估模型性能.
结论:
- 基于ANFIS的SRA模型有效地评估每个SDLC阶段的安全风险.
- 实施特定阶段的SRA模型有助于实现更安全的软件开发过程.
- 这种方法提供了一种系统的方法,在整个SDLC中减轻风险.
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