基于ANN-ISM范式的软件开发的AI驱动的网络安全框架
Habib Ullah Khan1, Rafiq Ahmad Khan2, Hathal S Alwageed3
1Department of Accounting and Information Systems, College of Bussiness and Economics, Qatar University, Doha, Qatar. Habib.khan@qu.edu.qa.
Scientific reports
|April 18, 2025
概括
本研究介绍了使用人工神经网络 (ANN) 和解释性结构建模 (ISM) 的AI驱动的网络安全框架,以提高软件开发中的威胁检测和风险评估. 人工智能模型通过比传统方法更有效地识别和减轻编码漏洞,显著提高了安全性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 传统的网络安全措施无法跟上软件开发中不断变化的威胁.
- 越来越多的人依赖软件应用程序,因此需要先进的安全解决方案.
- 人工智能 (AI) 为适应和改进网络安全防御提供了一个有希望的方法.
研究的目的:
- 为安全软件开发提供一个创新的AI驱动的网络安全框架.
- 改进软件开发生命周期 (SDLC) 内的威胁检测,漏洞评估和风险应对.
- 整合人工智能工具与网络安全建模,以实现动态和智能安全.
主要方法:
- 系统性文献审查 (SLR) 评估现有的网络安全风险和最佳实践.
- 经验调查以验证SLR发现.
- 混合方法将人工神经网络 (ANN) 结合起来,用于实时威胁检测和解释性结构建模 (ISM) 来分析风险相互依赖.
- 评估安全软件编码的AI驱动缓解模型的案例研究.
- 多级分类系统 (5个级别) 来评估组织的成熟度.
主要成果:
- 识别了软件编码中的15个网络安全风险和漏洞.
- 汇编了158个人工智能驱动的风险减轻最佳实践.
- 开发一个可扩展的模型,解决跨不同成熟度级别的网络安全风险.
- 人工智能在检测和修复安全漏洞方面表现优于传统系统.
- 较高成熟度水平 (4-5) 的组织需要进一步采用基于AI的保护工具.
结论:
- 拟议的ANN-ISM框架有效地将AI与网络安全建模集成为安全的软件编码.
- 由人工智能驱动的系统为根据开发阶段选择定制的安全增强提供了有价值的见解.
- 该框架支持自动化威胁分析,增强组织对网络安全威胁的警.
- 将人工智能系统与安全编码原则合并,可以改善人工智能产生的网络安全见解的实际应用.
关键词:
在这里,我们可以看到AIAIAI.在ANN-ISM建模中.一个案例研究.网络安全成熟度水平 网络安全成熟度水平网络安全风险和实践经验调查是实证调查.安全的软件编码.系统的文献审查 系统的文献审查更多相关视频
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