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SecMLOps:一个全面的框架,用于整合整个机器学习操作生命周期的安全性
Xinrui Zhang1,2, Pincan Zhao3, Jason Jaskolka1
1Department of Systems and Computer Engineering, Carleton University, Ottawa, ON Canada.
Empirical software engineering
|February 16, 2026
概括
本研究介绍了安全机器学习操作 (SecMLOps),这是一个嵌入安全性到ML生命周期的框架,增强系统抵御复杂攻击的弹性. 它平衡了安全需求与可靠ML部署的性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 机器学习 (ML) 对复杂系统至关重要,但也面临诸如对抗性攻击之类的安全挑战.
- 目前的ML操作 (MLOps) 缺乏全面的安全集成,危及系统完整性.
- 确保ML部署对于值得信赖的自动驾驶汽车,医疗保健和金融至关重要.
研究的目的:
- 引入安全机器学习操作 (SecMLOps),这是一个整合整个MLOps生命周期安全性的框架.
- 保护ML应用程序免受针对MLOps不同阶段的复杂攻击.
- 为 ML 部署中平衡安全性和性能提供实际指导.
主要方法:
- 开发了一个全面的SecMLOps框架,将安全整合到MLOps生命周期中.
- 将SecMLOps应用于先进的行人检测系统 (PDS) 使用案例.
- 进行经验评估,分析安全性-性能权衡.
主要成果:
- SecMLOps框架有效地提高了ML应用程序的弹性和可靠性.
- 经验评估证明了SecMLOps的实际应用和影响.
- 确定了安全措施和系统性能之间的关键权衡.
结论:
- SecMLOps提供了一个强大的方法来保护整个ML生命周期.
- 为了在不损害运营效率的情况下优化安全,需要采取平衡的方法.
- 该框架为部署安全机器学习系统的从业人员提供了宝贵的指导.
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