In this episode, we talk about:
- Security tools don’t get a free pass when it comes to involving end users as part of the design process.
- People studying and building ML-based security tools make a lot of assumptions. Instead of wasting time on assumptions, why not learn from security practitioners directly?
- Businesses (and academia) are investing a great deal in building ML-based security tools. But are those tools actually useful? Are they introducing problems you didn’t anticipate? And even if they are useful, how do you know security practitioners will adopt them?
- Why are adversarial machine learning defenses outlined in academic research not being put into practice? Jaron outlines three places where there are significant roadblocks: First, there are barriers to developers being aware of these defenses in the first place. Second, developers need to understand how the threats impact their systems. And third, they need to know how to effectively implement the defenses (and, importantly, be incentivized to do so).
Jaron Mink is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University focused on the intersection of usable security, machine learning, and system security.
In this episode, we highlight two of Jaron’s papers:
- “Everybody’s Got ML, Tell Me What Else Do You Have”: Practitioners’ Perception of ML-Based Security Tools and Explanations.”
- “Security is not my field, I’m a stats guy”: A Qualitative Root Cause Analysis of Barriers to Adversarial Machine Learning Defenses in Industry
