Cognitive Networks Technical Committee

Title: Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Practical Considerations

Date and Time: November 19, 2021 at 11AM ET

Registration Process: Please register at https://tinyurl.com/wh89zv5w

Abstract: As the Internet of Things (IoT) continues to grow, ensuring the security of systems that rely on wireless IoT devices has become critically important. Deep learning-based passive physical layer transmitter authorization systems, referred as RF fingerprinting, have been introduced recently for this purpose. RF fingerprinting approaches use wireless signals directly to verify identity of radio frequency transmitters based on imperfections in their radio hardware. However, most of the existing work using machine learning for RF fingerprinting has mainly focused on classification approaches assuming a closed set of transmitters. In practice, most serious security problems would arise if malicious transmitters outside this closed set are misclassified and authorized. In this talk, we formulate the problem of recognizing authorized transmitters and rejecting new transmitters as open set recognition and anomaly detection. We consider approaches based on one and several binary classifiers, multi-class classifiers, and signal reconstruction, and study how these approaches scale with the number of authorized transmitters. We propose using a known set of unauthorized transmitters to assist the training and study its impact. The authorization robustness against temporal changes in fingerprints is also evaluated as a function of the approach and the dataset structure. For this work, we have created a large Wi-Fi dataset consisting of about 10 million packets sent by 174 off-the-shelf Wi-Fi radios and simultaneously captured by 41 USRPs during 4 captures performed in ORBIT testbed along a month. We have also developed generative deep learning methods to emulate unauthorized signal samples for the augmentation of training datasets. We explored two different data augmentation techniques, one that exploits a limited number of known unauthorized transmitters and the other that does not require any unauthorized transmitters. Our results indicate that data augmentation allows for significant increases in open set classification accuracy, especially when the authorized set is small. Another practical problem in authentication systems based on deep learning is training time when the new transmitters are added. We have developed a fast authentication algorithm based on information retrieval that uses feature vectors as RF fingerprints and locality sensitive hashing(LSH) to create a database that can quickly searched by approximate nearest neighbor algorithm. The proposed algorithm matches the accuracy of deep learning models, while allowing for more than 100x faster retraining.

Bio: Danijela Cabric is Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles. Her research interests include novel radio architectures, signal processing, communications, machine learning and networking techniques for spectrum sharing, 5G millimeter-wave, massive MIMO and IoT systems. Dr. Cabric received the Samueli Fellowship in 2008, the Okawa Foundation Research Grant in 2009, Hellman Fellowship in 2012, the National Science Foundation Faculty Early Career Development (CAREER) Award in 2012 and Qualcomm Faculty Award in 2020 and 2021. Dr. Cabric is an IEEE Fellow. Her research on deep learning based RF transmitter fingerprinting is supported by SRC/JUMP CONIX center (https://conix.io/).

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.

Title: Brainstorming Generative Adversarial Networks (BGANs): Framework and Application to Wireless Networks

Date and Time: October 22, 2021 at 10AM EDT

Registration Process: Please register at https://tinyurl.com/2sn8tbwj

Abstract: Due to major communication, privacy, and scalability challenges stemming from the emergence of large-scale Internet of Things services, machine learning is witnessing a major departure from traditional centralized cloud architectures toward a distributed machine learning (ML) paradigm where data is dispersed and processed across multiple edge devices. A prime example of this emerging distributed ML paradigm is Google’s renowned federated learning framework. Despite the tremendous recent interest in distributed ML, remarkably, prior work in the area remains largely focused on the development of distributed ML algorithms for inference and classification tasks. In contrast, in this talk, we introduce the novel framework of brainstorming generative adversarial networks (BGANs) that constitutes one of the first implementations of distributed, multi-agent generative GAN models that does not rely on a centralized parameter server. We show how BGAN allows multiple agents to gain information from one another, in a fully distributed manner, without sharing their real datasets but by “brainstorming” their generated data samples. We then demonstrate the higher accuracy and scalability of BGAN compared to the state of the art through extensive experiments. We then illustrate how BGAN can be used to address key problems in the field of wireless communications by analyzing a millimeter wave channel modeling problem for wireless networks that rely on unmanned aerial vehicles (UAVs). We conclude this talk with an overview on the future outlook of the exciting area of distributed ML and its current and future applications in wireless systems.

Bio: Walid Saad (S’07, M’10, SM’15, F’19) received his Ph.D degree from the University of Oslo in 2010. He is currently a Professor at the Department of Electrical and Computer Engineering at Virginia Tech, where he leads the Network sciEnce, Wireless, and Security (NEWS) laboratory. His research interests include wireless networks, machine learning, game theory, security, unmanned aerial vehicles, cyber-physical systems, and network science. Dr. Saad is a Fellow of the IEEE. He is also the recipient of the NSF CAREER award in 2013 and of the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He was the author/co-author of ten conference best paper awards at WiOpt in 2009, ICIMP in 2010, IEEE WCNC in 2012, IEEE PIMRC in 2015, IEEE SmartGridComm in 2015, EuCNC in 2017, IEEE GLOBECOM in 2018, IFIP NTMS in 2019, IEEE ICC in 2020, and IEEE GLOBECOM in 2020. He is the recipient of the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, of the 2017 IEEE ComSoc Best Young Professional in Academia award, of the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award, and of the 2019 IEEE ComSoc Communication Theory Technical Committee. He was also a co-author of the 2019 IEEE Communications Society Young Author Best Paper and of the 2021 IEEE Communications Society Young Author Best Paper. He currently serves as an editor for several major IEEE Transactions.

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.

Title: Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks

Date: May 26, 2021; Time: 11AM EDT

Registration: Please register at https://albany.zoom.us/meeting/register/tJYvd-irrDMuH9ImDFQ65_9LugOGR4J-fEBg

Abstract: Federated learning is a machine learning setting where the centralized location trains a learning model by using remote devices. Federated learning algorithms cannot be employed in wireless networks unless the unreliable and resource-constrained nature of the wireless medium is taken into account. In this talk, I shall present a federated learning algorithm that is suitable for cellular wireless networks in a real-world scenario. I shall discuss its convergence properties, and the effects of local computation steps and communication steps on its convergence. Through experiments on real and synthetic datasets, I shall demonstrate the convergence of the proposed algorithm.

Bio: Ekram Hossain (IEEE Fellow) is a Professor in the Department of Electrical and Computer Engineering at University of Manitoba, Winnipeg, Canada. He is a Member (Class of 2016) of the College of the Royal Society of Canada, a Fellow of the Canadian Academy of Engineering, and also a Fellow of the Engineering Institute of Canada (http://home.cc.umanitoba.ca/~hossaina). He received his Ph.D. in Electrical Engineering from University of Victoria, Canada, in 2001. Dr. Hossain’s current research interests include design, analysis, and optimization of wireless communication networks (with emphasis on beyond 5G/6G cellular), applied machine learning and game theory, and network economics. He was elevated to an IEEE Fellow “for contributions to spectrum management and resource allocation in cognitive and cellular radio networks”. He was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2017, 2018, 2019, and 2020. Dr. Hossain has won several research awards including the “2017 IEEE Communications Society Best Survey Paper Award, the 2011 IEEE Communications Society Fred Ellersick Prize Paper Award, University of Manitoba Merit Award in 2010, 2013, 2014, and 2015 (for Research and Scholarly Activities), and the IEEE Wireless Communications and Networking Conference 2012 (WCNC’12) Best Paper Award. He received the 2017 IEEE ComSoc TCGCC (Technical Committee on Green Communications & Computing) Distinguished Technical Achievement Recognition Award “for outstanding technical leadership and achievement in green wireless communications and networking”. Currently he serves as the Editor-in-Chief of the IEEE Press and an Editor for IEEE Transactions on Mobile Computing. Previously, he served as an Area Editor for the IEEE Transactions on Wireless Communications in the area of “Resource Management and Multiple Access” (2009-2011) and an Editor for the IEEE Journal on Selected Areas in Communications – Cognitive Radio Series (2011-2014). He serves as the Director of Magazines for the IEEE Communications Society (2020-2021). Dr. Hossain was an elected Member of the Board of Governors of the IEEE Communications Society for the term 2018-2020. He is a Distinguished Lecturer of the IEEE Communications Society. He is a registered Professional Engineer in the province of Manitoba, Canada.

About the Monthly Virtual Seminar Series: The IEEE TCCN Special Interest Group for AI and Machine Learning in Security 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.

Title: Secure Code Execution on Untrusted Remote Devices

Date: April 28, 2021; Time: 1PM EDT

Registration: Please register using the following link. You will receive a link in your email to attend the talk online.
https://forms.gle/bTah3LEyV5vUxMAw7

Abstract: Our society is increasingly reliant upon a wide range of Cyber-Physical Systems (CPS), Internet-of-Things (IoT), embedded, and so-called “smart” devices. They often perform safety-critical functions in numerous settings, e.g., home, office, medical, automotive and industrial. Some devices are small, cheap and specialized sensors and/or actuators. They tend to have meager resources, run simple software, sometimes upon “bare metal”. If such devices are left unprotected, consequences of forged sensor readings or ignored actuation commands can be catastrophic, particularly, in safety-critical settings. This prompts the following three questions: (1) How to trust data produced by a simple remote embedded device? (2) How to ascertain that this data was produced via execution of expected software? And, (3) Is it possible to attain (1) and (2) under the assumption that all software on the remote device might be modified or compromised? In this talk, we answer these questions by describing APEX: (Verified) Architecture for Proofs of Execution, the first of its kind result for low-end embedded systems. This work has a range of applications, especially, to authenticated sensing and trustworthy actuation, APEX incurs low overhead, making it affordable even for lowest-end embedded devices; it is also publicly available.

Bio: Gene Tsudik is a Distinguished Professor of Computer Science at the University of California, Irvine (UCI). He obtained his PhD in Computer Science from USC in 1991. Before coming to UCI in 2000, he was at the IBM Zurich Research Laboratory (1991-1996) and USC/ISI (1996-2000). His research interests include many topics in security, privacy and applied cryptography. Gene Tsudik is a Fulbright Scholar, Fulbright Specialist (twice), a fellow of ACM, IEEE, AAAS, IFIP and a foreign member of Academia Europaea. From 2009 to 2015 he served as Editor-in-Chief of ACM Transactions on Information and Systems Security (TISSEC, renamed TOPS in 2016). Gene was the recipient of 2017 ACM SIGSAC Outstanding Contribution Award. He is also the author of the first crypto-poem published as a refereed paper.

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.

Title: Adversarial Machine Learning for Wireless Security in 5G and Beyond

Date and Time: March 26, 2021 at 10AM ET

Registration Process: Please register using the following link. You will receive a link in your email to attend the talk online.

https://forms.gle/krrmynfr3DDH7ENS9

Abstract: Machine learning provides powerful means to learn from the dynamic spectrum environment and solve complex tasks for wireless communications. Supported by recent advances in algorithmic and computational capabilities, deep learning has emerged as a viable solution to efficiently utilize the limited spectrum resources and optimize wireless communications, with 5G and beyond enhancements to meet the ever-growing demands for high-rate and low-latency communications. As deep learning is becoming a key component in emerging wireless technologies, a new security threat arises due to adversarial machine learning that exploits the vulnerabilities of deep learning to adversarial manipulations. Adversarial machine learning has been applied to different data domains ranging from computer vision to natural language processing. By considering the unique characteristics of the wireless medium, this talk will present adversarial machine learning as a new attack surface for the next-generation communication systems. Novel attack and defense mechanisms built upon adversarial machine learning will be described with examples from signal classification, dynamic spectrum access, and 5G and beyond applications related to spectrum co-existence, user authentication, covert communications, and network slicing. Research challenges and directions will be discussed for effective and safe adoption of much-needed machine learning techniques in the emerging wireless technologies.

Bio: Dr. Yalin Sagduyu is the Director of Networks and Security Division at Intelligent Automation, Inc. (IAI). He received his Ph.D. degree in Electrical and Computer Engineering from University of Maryland, College Park. At IAI, he directs a division of over 50 research scientists and engineers, and executes a broad portfolio of R&D projects on wireless communications, networks, security, machine learning, adversarial machine learning, and 5G and beyond. He has been a Visiting Research Professor in the Electrical and Computer Engineering Department of University of Maryland, College Park. He served as a Conference Track Chair at IEEE PIMRC, IEEE GlobalSIP and IEEE MILCOM, and in the organizing committee of IEEE GLOBECOM. He organized and chaired workshops at IEEE CNS, IEEE ICNP, ACM Mobicom, and ACM WiSec. He received the Best Paper Award at IEEE HST.

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.