By Prof. Roy Maxion, Research Professor, Computer Science and Machine learning, Carnegie Mellon University
Monday, October 24, 2022
4:00 p.m. – 5:00 p.m.
Building 3, Room 5220
Abstract
AI/ML systems “learn” to make decisions based on the data with which they are trained. Such systems are often used to make critical decisions in which mistakes can have serious consequences — e.g., systems for approving credit, job and college applications, digital
forensic procedures, and computer-user authentication. In these kinds of applications AI/ML decision algorithms are tasked with distinguishing between legitimate and fraudulent or wrong behavior.
We show that minor degradations in as little as 1-2 percent of the training data can change decision outcomes by nearly 20 percentage points, wrongly reversing distinctions between legitimacy and fraudulence. In one real-world application – user authentication – data
corruption was induced by USB keyboards injecting artifacts into the data, effecting an infidelity to the true signal. We illustrate how this phenomenon was discovered and validated.
About the speaker
Roy Maxion is a research professor in computer science and machine learning at Carnegie Mellon University, where he is also the director of the Dependable Systems Laboratory. His research has covered development and evaluation of highly reliable systems, human-computer interfaces, and automated detection, diagnosis and remediation of faulty or
unanticipated events (anomalies) in many domains — international banking, telecommunications networks, digital libraries, vendor help systems, semiconductor fabrication, process control, computer security, keystroke biometrics, camera ID forensics and others. He is broadly experienced in experimental design and evaluation.
Dr. Maxion is a founding member of the NIST-supported, multi-university Center for Statistics and Applications in Forensic Evidence, whose mission is to build a scientifically and statistically sound foundation for formal and experimental analysis of forensic evidence. He recently served as a member of the National Academy of Sciences committee on Future Research Goals and Directions for Foundational Science in
Cybersecurity. He won an IEEE 2019 Test of Time Award (with Kevin Killourhy) for the 2009 experimental paper, “Comparing Anomaly Detection Algorithms for Keystroke Dynamics.” He is on the editorial boards of the International Journal of Machine Learning and IEEE Security & Privacy. Dr. Maxion is an IEEE Fellow.
This event is open to the entire KAUST community and is brought to you by the Resilient Computing and Cybersecurity Center RC3 We look forward to seeing you there, Light refreshments will be served!