Virtual Seminar by Marwan Krunz

Title: Machine Learning Classification of RF Signals over Congested and Contested Spectrum: Algorithms and Experimentation

Date and Time: February 23, 2022 at 11AM ET

Registration Process: Please register at

Abstract: Machine learning (ML) has recently been applied for the classification of radio frequency (RF) signals. One use case of interest relates to the discernment between different wireless protocols that operate over a shared and potentially contested spectrum. Although highly accurate classifiers have been developed for various wireless scenarios, research points to the vulnerability of such classifiers to adversarial machine learning (AML) attacks. In one such attack, 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 DNN protocol classifiers that our team designed for a shared spectrum environment. These classifiers performed quite well in both simulations and OTA experimentation, considering benign (non-adversarial) noise. I will then present several AML techniques that an attacker may use to generate low power perturbations. When combined with a legitimate signal, these perturbations are shown to uniformly degrade the classification accuracy, even in the very high SNR regime. Different attack models are studied, depending on how much information the attacker has about the defender’s classifier. These models range from a “white-box’” attack (attacker has full knowledge of the defender’s DNN, including its hyperparameters, its training dataset, and even the seeds used to train the network), to a “black-box” attack. Time permitting, I will discuss possible defense mechanisms as well as other research efforts related to detection of adversarial transmissions.

Bio: Marwan Krunz is a Regents Professor at the University of Arizona. He holds the Kenneth VonBehren Endowed Professorship in ECE and is also a professor of 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  the Connection One center. Dr. Krunz’s research is on resource management, network protocols, and security for wireless systems. He has published more than 300 journal articles and peer-reviewed conference papers, and is a named inventor on 12 patents. His latest h-index is 60. 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 vice-chair for WiOpt 2016 and general co-chair for WiSec’12. Dr. Krunz served as chief scientist for two startup companies that focus on 5G and beyond systems and machine learning for wireless communications.

About the Monthly Virtual Seminar Series:

The IEEE TCCN Security Special Interest Group conducts a monthly virtual seminar series to highlight the challenges in securing the next generation (xG) wireless networks. The talks will feature cutting edge research addressing both technical and policy issues to advance the state-of-the-art in security techniques, architectures, and algorithms for wireless communications.