Mobile technologies, fueled by advances in wireless communications, revolutionized our society beyond the pioneers' dreams. It enables ubiquitous access to information, and connects people to each other, and to a rapidly increasing number of services, and businesses. However, a plethora of emerging applications, such as Massive IoT (MToT), autonomous cars, robotics, and augmented reality are driving the demand for spectrum to new heights. Spectrum scarcity is becoming a critical issue. At the same time, wireless systems are increasingly softwarized, and SDR platforms are highly capable, with small form factor and low cost. This is both a blessing for developing new communications techniques, and a curse as it lowered the barrier for attacks from smart jamming, to tracking, spoofing, compromising wireless chips, or weaponizing drones. On the other hand, advances in machine learning and its success in computer vision demonstrate what can be achieved when adequate deep learning models leverage large datasets and computation power.
This confluence of trends raises challenging research questions to: understand the spectrum, in real-time and a-posteriori, detect, classify, and predict communication patterns in time, frequency, and space in order to fine-grain share the spectrum efficiently as well as to mitigate unintentional and malicious interference, or threats from drones. Agencies such as the FAA and FCC have regulations against such threats, but not the necessary technology to enforce them. Our approach is multi-fold, capitalizing on the advances in ML, and the flexibility and computational capability of SDRs, while addressing, from the ground up, the need to understand and adapt in real-time in a crowded spectrum and the presence of malicious actors. In particular, we seek to devise RF-Centric machine learning techniques to address these challenges, including the development of new RF-Centric ML model architectures, feature extraction layers, activation and regularization functions.
DARPA Spectrum Collaboration Challenge (SC2): Our initial results where demonstrated within the DARPA Spectrum Collaboration Challenge (SC2) where our team Sprite representing Northeastern University (led by Guevara Noubir, and Triet Vo-Huu and included doctoral students Tien Vo-Huu, Hai Nguyen, Norbert Ludant, and Marinos Vomvas) was a winner in the 2017 Preliminary Event 1 ($750K), and a winner in the 2018 Preliminary Event 2 ($750K), and a finalist in 2019. Besides the development of novel elastic Pilotless Filter Bank Multicarrier communications techniques, we demonstrated the ability RFML technique to detect, classify, and localize in time and spectrum emissions from a variety of communication technologies.
Detecting, Localizing and Classifying Arbitrary Emissions: We later extended our techniques developing a framework for systematic and generalizable detection and classification of RF emissions with the ability to operate in real-time, over a wideband spectrum, and even in highly congested environments. We applied our approach to commercial waveforms including Wi-Fi, Bluetooth, ZigBee, Lightbridge (DJI drones) and XPD (microphones), achieving over 95 mean Average Precision (mAP) processing over 6Gbps streams of RF samples corresponding to 100MHz [SC2-RFML].
The 3GPP 5G cellular system is hailed as a major step towards a ubiquitous and pervasive communications infrastructure. It is indeed flexible and extensible, with slices to support a variety of unique applications requirements, from Massive IoT (MIoT), Ultra-Reliable Low-Latency Communications (URLLC), to enhanced Mobile Broadband (eMBB), and massive Machine Type Communications (mMTC), as well as specific industry requirements such as V2X, Smart Grid, and Remote Healthcare. This capability to address unique needs, along with the redesign around Service Based Architecture, and Network Functions Virtualization is very promising to adequately support a larger number of applications including critical ones such as self-driving cars, robotics, and remote surgeries.
Cellular systems, however, have a history of security, privacy, and robustness issues since their second generation (GSM) that took security and privacy more seriously. Over the years, we and other researchers around the world were able to demonstrate attacks against every generation of cellular systems from 2G to 4G, by preying on design, implementation, and operation flaws. Within projects funded and in collaboration with the DoD we have been analyzing the security, robustness, privacy of 5G systems and devising protections for current and future generations of cellular communications systems. Our research runs along three main axes: (1) analysis of security and privacy threats, (2) design and implementation of countermeasures, and (3) testbed setup and experimental validations. We identified several vulnerabilities in the 5G design pertaining to both DoD networks and general networks (e.g., the ability to stealthily spoof the 5G signal synchronization block with emissions 3.4dB below the legitimate signal). We developed several mitigations, implemented them, and evaluated them in our testbed using the 5G Open Air Interface platform. We are interested in the security of both Radio Access Network (RAN) and the Core Network.