Comparative Insights on Adversarial Machine Learning from Industry and Academia: A User-Study Approach
This work addresses the need for better security education in ML curricula, but it is incremental as it focuses on user perspectives rather than technical solutions.
The paper conducted two user studies to understand perspectives on adversarial machine learning (AML) vulnerabilities and educational strategies, finding that cybersecurity education correlates with AML concern among professionals and that CTF challenges effectively engage students in AML threats.
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.