论文标题
对抗机器学习 - 行业观点
Adversarial Machine Learning -- Industry Perspectives
论文作者
论文摘要
根据与28个组织的访谈，我们发现行业从业人员没有配备战术和战略工具来保护，检测和应对对机器学习（ML）系统的攻击。我们利用访谈中的见解，并在传统软件安全开发的背景下查看机器学习系统的角度列举了差距。我们从两个角色的角度撰写本文：开发人员/ML工程师和安全事件响应者，他们的任务是确保ML系统设计，开发和部署ML系统。本文的目的是让研究人员在对抗性ML时代修改和修改工业级软件的安全开发生命周期。
Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems. We leverage the insights from the interviews and we enumerate the gaps in perspective in securing machine learning systems when viewed in the context of traditional software security development. We write this paper from the perspective of two personas: developers/ML engineers and security incident responders who are tasked with securing ML systems as they are designed, developed and deployed ML systems. The goal of this paper is to engage researchers to revise and amend the Security Development Lifecycle for industrial-grade software in the adversarial ML era.
