Note special time for this seminar.
Zoom: https://oregonstate.zoom.us/j/91611213801?pwd=Wm9JSkN1eW84RUpiS2JEd0E5T…
Radio frequency (RF) device fingerprinting plays an important role in network security, enabling physical layer-based network access authentication and network device classification through the identification of devices from their transmitted RF signals. The use of deep learning for RF device fingerprinting has become prevalent in recent years as it has enabled the automated extraction of device-specific features and signatures solely from raw RF signals, without the need for expert domain knowledge to engineer informative features. This talk will describe two active areas of research related to RF device fingerprinting: 1) open-set detection for detecting unauthorized devices and 2) domain adaptation to deal with changes in channel conditions and environmental settings.
Weng-Keen Wong received his Ph.D. and M. S. degrees in computer science from Carnegie Mellon University in 2004 and 2001 respectively. He received his B.Sc. degree from the University of British Columbia in 1997. He is currently a Professor in the School of Electrical Engineering and Computer Science at Oregon State University. From 2016-2018, he served as a program director at the National Science Foundation under the Robust Intelligence Program in the Division of Information and Intelligent Systems. His research areas are in data mining and machine learning, with specific interests in anomaly detection, deep learning, probabilistic graphical models, computational sustainability and human-in-the-loop learning.