Last week, I gave a talk at the Center for Information Technology Policy at Princeton. My goal was to expand my usual research talk on passwords with broader predictions about where authentication is going. From the reaction and discussion afterwards one point I made stood out: authenticating humans is becoming a machine learning problem.
Problems with passwords are well-documented. They’re easy to guess, they can be sniffed in transit, stolen by malware, phished or leaked. This has led to loads of academic research seeking to replace passwords with something, anything, that fixes these “obvious” problems. There’s also a smaller sub-field of papers attempting to explain why passwords have survived. We’ve made the point well that network economics heavily favor passwords as the incumbent, but underestimated how effectively the risks of passwords can be managed in practice by good machine learning.
From my brief time at Google, my internship at Yahoo!, and conversations with other companies doing web authentication at scale, I’ve observed that as authentication systems develop they gradually merge with other abuse-fighting systems dealing with various forms of spam (email, account creation, link, etc.) and phishing. Authentication eventually loses its binary nature and becomes a fuzzy classification problem. Continue reading Authentication is machine learning