Marwan Krunz - Communication systems
Date: - Location: Eurecom
Abstract: Machine learning (ML) has recently been applied for inference and identification of radio frequency (RF) signals. Use cases include protocol classification over shared bands, modulation classification, and identification of rogue signals. Although accurate classifiers have been developed for different scenarios, research points to the vulnerability of such classifiers to adversarial machine learning (AML) attacks. In one type of attacks, a surrogate deep neural network (DNN) model is trained by the attacker to produce intelligently crafted low power “perturbations” that degrade the classification accuracy of the legitimate classifier. In this talk, I will first present several novel RF signal classifiers designed to facilitate spectrum coexistence and identify anomalous transmissions. These classifiers perform quite well in both simulations and OTA experimentation, considering benign (non-adversarial) noise. I will then show-case the impact of AML attacks on these and other popular classifiers. When added to the legitimate signal, AML perturbations are shown to uniformly degrade the classification accuracy, even in the very high SJR regime. Time permitting, I will also discuss other attacks models, in which the attacker generates mimics of legitimate signals, aiming to mislead the targeted classifier. Finally, I will discuss several possible defense mechanisms that aim at securing ML-based RF classifiers against adversarial attacks. Short bio: Marwan Krunz: is a Regents Professor in ECE at the University of Arizona. He also has a joint appointment as a Professor in Computer Science. He directs the Broadband Wireless Access and Applications Center (BWAC), a multi-university NSF/industry center that focuses on next-generation wireless technologies. He also holds a courtesy appointment as a professor at University Technology Sydney. Previously, he served as the site director for Connection One, an NSF/industry-funded center of five universities and 20+ industry affiliates. Dr. Krunz’s research is in the fields of wireless communications, networking, and security, with recent focus on applying AI and machine learning techniques for protocol adaptation, resource management, and signal intelligence. He has published more than 340 journal articles and peer-reviewed conference papers, and is a named inventor on 12 patents. His latest h-index is 62. He is an IEEE Fellow, an Arizona Engineering Faculty Fellow, and an IEEE Communications Society Distinguished Lecturer (2013-2015). He received the NSF CAREER award. He served as the Editor-in-Chief for the IEEE Transactions on Mobile Computing. He also served as editor for numerous IEEE journals. He was the TPC chair for INFOCOM’04, SECON’05, WoWMoM’06, and Hot Interconnects 9. He was the general co-chair for WiOpt’23, vice-chair for WiOpt 2016, and general co-chair for WiSec’12. Dr. Krunz served as chief scientist/technologist for two startup companies that focus on 5G and beyond wireless systems.
