论文标题
机器学习安全的风险管理框架
Risk Management Framework for Machine Learning Security
论文作者
论文摘要
机器学习模型的对抗性攻击已成为学术界和行业中的一个知名主题。这些攻击以及传统的安全威胁可能会损害组织资产的机密性，完整性和可用性，这些资产取决于机器学习模型的使用。尽管预测可能随着时间的推移可能开发的新攻击的类型并不容易，但可以评估与使用机器学习模型和设计措施相关的风险，从而有助于最大程度地减少这些风险。 在本文中，我们概述了一个新颖的框架，用于指导依赖机器学习模型的组织的风险管理过程。首先，我们定义了数据域，模型域和安全控制域中的评估因素（EFS）集。我们开发了一种采用资产和任务重要性的方法，设置了EFS对机密性，完整性和可用性的贡献的权重，并且基于EFS的实施得分，它决定了组织中的整体安全状态。基于此信息，可以识别实施的安全措施中的薄弱环节，并找出可能完全缺少哪些措施。我们认为，我们的框架可以帮助解决与组织中机器学习模型使用相关的安全问题，并指导他们专注于保护其资产的适当安全措施。
Adversarial attacks for machine learning models have become a highly studied topic both in academia and industry. These attacks, along with traditional security threats, can compromise confidentiality, integrity, and availability of organization's assets that are dependent on the usage of machine learning models. While it is not easy to predict the types of new attacks that might be developed over time, it is possible to evaluate the risks connected to using machine learning models and design measures that help in minimizing these risks. In this paper, we outline a novel framework to guide the risk management process for organizations reliant on machine learning models. First, we define sets of evaluation factors (EFs) in the data domain, model domain, and security controls domain. We develop a method that takes the asset and task importance, sets the weights of EFs' contribution to confidentiality, integrity, and availability, and based on implementation scores of EFs, it determines the overall security state in the organization. Based on this information, it is possible to identify weak links in the implemented security measures and find out which measures might be missing completely. We believe our framework can help in addressing the security issues related to usage of machine learning models in organizations and guide them in focusing on the adequate security measures to protect their assets.
