Education
- Ph.D. in Cybersecurity - Northeastern University (2025)
- M.S. in Cybersecurity - Northeastern University (2020)
- B.S. in Computer Science and Mathematics - Bridgewater State University (2018, magna cum laude)
Research Interests
Clifton Paul Robinson earned his Ph.D. in Cybersecurity from Northeastern University in December 2024. He received his B.S. in Computer Science and Mathematics, magna cum laude, from Bridgewater State University in 2018, and completed his M.S. in Cybersecurity at Northeastern in 2020. He is now a Research Associate at the Institute for the Wireless Internet of Things under Professor Tommaso Melodia. His research spans wireless network security, deep learning-based security solutions, AI-driven spectrum sensing, and digital twins for the wireless spectrum. His work on TwiNet received the Best Paper Award at IEEE GLOBECOM 2024. He joined Wentworth Institute of Technology as an Assistant Professor.
Publications
Wireless network emulators are being increasingly used for developing and evaluating new solutions for Next Generation (NextG) wireless networks. However, the reliability of the solutions tested on emulation platforms heavily depends on the precision of the emulation process, model design, and parameter settings. To address, obviate, or minimize the impact of errors of emulation models, in this work, we apply the concept of Digital Twin (DT) to large-scale wireless systems. Specifically, we demonstrate the use of Colosseum, the world's largest wireless network emulator with hardware-in-the-loop, as a DT for NextG experimental wireless research at scale. As proof of concept, we leverage the Channel emulation scenario generator and Sounder Toolchain (CaST) to create the DT of a publicly available over-the-air indoor testbed for sub-6 GHz research, namely, Arena. Then, we validate the Colosseum DT through experimental campaigns on emulated wireless environments, including scenarios concerning cellular networks and jamming of Wi-Fi nodes, on both the real and digital systems. Our experiments show that the DT is able to provide a faithful representation of the real-world setup, obtaining an average similarity of up to 0.987 in throughput and 0.982 in Signal to Interference plus Noise Ratio (SINR).
LinkJamming attacks have plagued wireless communication systems and will continue to do so going forward with technological advances. These attacks fall under the category of Electronic Warfare (EW), a continuously growing area in both attack and defense of the electromagnetic spectrum, with one subcategory being electronic attacks (EA). Jamming attacks fall under this specific subcategory of EW as they comprise adversarial signals that attempt to disrupt, deny, degrade, destroy, or deceive legitimate signals in the electromagnetic spectrum. While jamming is not going away, recent research advances have started to get the upper hand against these attacks by leveraging new methods and techniques, such as machine learning. However, testing such jamming solutions on a wide and realistic scale is a daunting task due to strict regulations on spectrum emissions. In this paper, we introduce eSWORD (emulation (of) Signal Warfare On Radio-frequency Devices), the first large-scale framework that allows users to safely conduct real-time and controlled jamming experiments with hardware-in-the-loop. This is done by integrating METEOR, an electronic warfare (EW) threat-emulating software developed by the MITRE Corporation, into the Colosseum wireless network emulator that enables large-scale experiments with up to 49 software-defined radio nodes. We compare the performance of eSWORD with that of real-world jamming systems by using an over-the-air wireless testbed (considering safe measures when conducting experiments). Our experimental results demonstrate that eSWORD achieves up to 98% accuracy in following throughput, signal-to-interference-plus-noise ratio, and link status patterns when compared to real-world jamming experiments, testifying to the high accuracy of the emulated eSWORD setup.
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