Tech Talk: The Challenges of Machine Learning in Adversarial Settings: A Systems Perspective

Abstract: Advances in machine learning have enabled new applications that border on science fiction. Autonomous cars, data analytics, adaptive communication and self-aware software systems are now revolutionizing markets by achieving or exceeding human performance. In this talk, I discuss the rapidly evolving use of machine learning in security-sensitive contexts and explore why many systems are vulnerable to nonobvious and potentially dangerous manipulation. We will examine sensitivity in applications where misuse might lead to harm—for instance, forcing adaptive networks into an unstable state, crashing an autonomous vehicle or bypassing an adult content filter. I explore how currently accepted wisdom about threats and defenses should be viewed (and sometimes refuted) in light of the functional and security challenges of real-world systems.  The talk concludes with a discussion of the technological, economic and societal challenges we face as a result of the rise of machine learning as fundamental construct of computational systems.
 

Bio: Patrick McDaniel is the William L. Weiss Professor of Information and Communications Technology and Director of the Institute for Networking and Security Research in the School of Electrical Engineering and Computer Science at the Pennsylvania State University. Professor McDaniel is also a Fellow of the IEEE and ACM and the director of the NSF Frontier Center for Trustworthy Machine Learning. He also served as the program manager and lead scientist for the Army Research Laboratory's Cyber-Security Collaborative Research Alliance from 2013to 2018. Patrick's research focuses on a wide range of topics in computer and network security and technical public policy. Prior to joining Penn State in 2004, he was a senior research staff member at AT&T Labs-Research.