论文标题
对机器学习的实践攻击：对抗窗户恶意软件的案例研究
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
论文作者
论文摘要
尽管机器学习容易受到对抗性示例的影响，但它仍然缺乏在不同应用程序上下文中评估其安全性的系统过程和工具。在本文中，我们讨论了如何使用实际攻击来开发机器学习的自动化和可扩展的安全性评估，并在Windows恶意软件检测中报告了用例。
While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures and tools for evaluating its security in different application contexts. In this article, we discuss how to develop automated and scalable security evaluations of machine learning using practical attacks, reporting a use case on Windows malware detection.
