从行业和学术角度对比分析对抗机器学习：一种用户研究方法
Comparative Insights on Adversarial Machine Learning from Industry and Academia: A User-Study Approach
AI总结：
本文通过两项研究探讨行业人士和学生对对抗机器学习漏洞及教育策略的看法，发现CTF挑战能有效提升对AML威胁的兴趣，并强调整合安全教育的重要性。
摘要：
机器学习及其生成式AI的指数级增长带来了显著的安全挑战，通常称为对抗机器学习（AML）。本文通过两项综合研究探讨行业专业人士和学生对不同AML漏洞及其教育策略的看法。在第一项研究中，我们通过在线调查发现网络安全教育与对AML威胁的关注之间存在显著相关性。在第二项研究中，我们开发了两个CTF挑战，实现自然语言处理和生成式AI概念，并展示对训练数据集的污染攻击。这些挑战的有效性通过调查卡内基梅隆大学的本科生和研究生进行评估，发现基于CTF的方法能有效激发对AML威胁的兴趣。基于研究参与者的反馈，我们提供了详细的建议，强调在ML课程中整合安全教育的紧迫性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
