对比产业与学术界对对抗机器学习的认知，发现安全教育能提升威胁意识。
Comparative Insights on Adversarial Machine Learning from Industry and Academia: A User-Study Approach
- 通过问卷和CTF挑战，调研产业与学生对对抗攻击的看法。
- 基于CTF的实战教学显著激发学生对生成式AI安全的兴趣。
- 建议将网络安全教育融入机器学习课程体系。
机器学习及其生成式AI应用的迅猛发展带来了显著的安全挑战，即对抗机器学习（AML）。本文通过两项综合性研究，探讨了产业界专业人士与学生对不同AML漏洞及其教育策略的看法。第一项研究通过对专业人士的在线调查发现，网络安全教育程度与对AML威胁的关注度存在显著相关性。第二项研究设计了两个结合自然语言处理与生成式AI概念的CTF挑战，并展示了针对训练数据集的投毒攻击。该挑战在卡内基梅隆大学本科生与研究生中进行评估，结果显示基于CTF的方法能有效激发学生对AML威胁的兴趣。根据研究参与者反馈，本文提出了详细建议，强调在机器学习课程中整合安全教育的紧迫性。
原文摘要 · Abstract (English)
An exponential growth of Machine Learning and its Generative AI applications brings with it significant security challenges, often referred to as Adversarial Machine Learning (AML). In this paper, we conducted two comprehensive studies to explore the perspectives of industry professionals and students on different AML vulnerabilities and their educational strategies. In our first study, we conducted an online survey with professionals revealing a notable correlation between cybersecurity education and concern for AML threats. For our second study, we developed two CTF challenges that implement Natural Language Processing and Generative AI concepts and demonstrate a poisoning attack on the training data set. The effectiveness of these challenges was evaluated by surveying undergraduate and graduate students at Carnegie Mellon University, finding that a CTF-based approach effectively engages interest in AML threats. Based on the responses of the participants in our research, we provide detailed recommendations emphasizing the critical need for integrated security education within the ML curriculum.
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