教授对抗性机器学习:培养下一代技术和安全专业人员

Collin Payne, Edward J. Glantz
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引用次数: 0

摘要

机器学习的发展为扩展教育创造了机会,包括“对抗性”机器学习的研究,特别是在网络安全专业人员和机器学习专家的本科和研究生课程中。本文介绍了教学这些概念时可用的工具。这些信息还可以帮助系统设计人员减少设计缺陷,以及针对恶意攻击进行设计。本文建议使用这些工具来改进可能会强化机器学习系统的攻击性网络安全实践。这些工具包括新开发的机器学习库,使这种方法成为一种实用的选择。
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Teaching Adversarial Machine Learning: Educating the Next Generation of Technical and Security Professionals
The growth in machine learning has created an opportunity to expand education to include the study of "adversarial" machine learning, specifically in undergraduate and graduate courses for cybersecurity professionals and machine learning experts. This paper presents tools available in teaching these concepts. This information also helps system designers reduce design flaws, as well as design against malicious attacks. This paper recommends using these tools to improve offensive cyber security practices that may harden machine learning systems. These tools include newly developed machine learning libraries that make this approach a practical alternative.
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