IEEE INFOCOM 2023
Fingerprinting and Classification
A Framework for Wireless Technology Classification using Crowdsensing Platforms
Alessio Scalingi (IMDEA Networks, Spain); Domenico Giustiniano (IMDEA Networks Institute, Spain); Roberto Calvo-Palomino (Universidad Rey Juan Carlos, Spain); Nikolaos Apostolakis (IMDEA Networks, Spain); Gérôme Bovet (Armasuisse, Switzerland)
Speaker Alessio Scalingi (IMDEA Networks)
Alessio Scalingi is Ph.D. student of the Pervasive Wireless Systems Group at IMDEA Networks Institute since January 2020.
He completed both his Bachelor's and Master's degrees in Computer Engineering at the University of Naples Federico II in 2015 and 2019, respectively.
During his Master's program, Alessio conducted research for his thesis at the Computer Science Lab of Saint Louis University in the United States. He also gained valuable experience as a visiting PhD at the Wireless Networks and Embedded Systems (WiNES) Laboratory in Boston, USA, for a period of six months. His primary research interests encompass Collaborative Spectrum Sensing, Machine Learning, Spectrum Anomaly Detection, Open-RAN, and Security in 5G and Beyond Networks.
MagFingerprint: A Magnetic Based Device Fingerprinting in Wireless Charging
Jiachun Li, Yan Meng, Le Zhang and Guoxing Chen (Shanghai Jiao Tong University, China); Yuan Tian (University of California Los Angeles, USA); Haojin Zhu (Shanghai Jiao Tong University, China); Sherman Shen (University of Waterloo, Canada)
In this paper, we design a magnetic based fingerprinting system MAGFINGERPRINT, which utilizes the alternating magnetic signals as the fingerprint and is compatible with existing wireless charging systems. MAGFINGERPRINT is convenient for the user since it only employs commercial-off-the-shelf (COTS) magnetic sensors and requires no action from users. In particular, for the charging device, based on its intrinsic manufacturing errors, MAGFINGERPRINT generates a unique fingerprint according to the distinct magnetic changes during the wireless charging process. It is shown that MAGFINGERPRINT can achieve an accuracy of 98.90% on wireless charging exposed coils, while it is also effective on different commercial wireless charging pads of Apple, Huawei, and Xiaomi.
Speaker Jiachun Li (Shanghai Jiao Tong University)
Jiachun Li is a Ph.D. candidate in the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. He received the B.S. degree in Communication Engineering from Huazhong University of Science and Technology in 2020. His research interests include smart home security and smart healthcare security.
Plug and Power: Fingerprinting USB Powered Peripherals via Power Side-channel
Riccardo Spolaor and Hao Liu (Shandong University, China); Federico Turrin (University of Padua, Italy); Xiuzhen Cheng (Shandong University, China); Mauro Conti (University of Padua, Italy; TU Delft, Netherlands)
In this paper, we present PowerID, a novel method to fingerprint USB peripherals based on their power consumption. PowerID analyzes the power traces from a peripheral to infer its identity and properties. We evaluate the effectiveness of our method on an extensive power trace dataset collected from 82 USB peripherals, including 35 models and eight types. Our experimental results show that PowerID accurately recognizes a peripheral type, model, activity, and identity.
Speaker Federico Turrin (University of Padova)
Federico Turrin received his Ph.D. in Brain, Mind, and Computer Science, in 2023 at the University of Padova. He is currently a Post Doc Researcher at the University of Padova and a Cybersecurity Engineer at SPRITZ Matter Srl. He has been visiting researcher at SUTD, in Singapore in 2022. His research interests lie primarily in Cyber-Physical System security with a particular focus on Industrial Control systems security, Vehicles Security, and Anomaly detection.
Contrastive learning with self-reconstruction for channel-resilient modulation classification
Erma Perenda (KU Leuven, Belgium); Sreeraj Rajendran (Sirris, Belgium); Mariya Zheleva (UAlbany SUNY, USA); Gérôme Bovet (Armasuisse, Switzerland); Sofie Pollin (KU Leuven, Belgium)
Speaker Mariya Zheleva (University at Albany – SUNY, New York, USA)
Mariya Zheleva is an Associate Professor in Computer Science at University at Albany – SUNY. She graduated with her PhD in Computer Science from University of California Santa Barbara in 2014. She leads the UbiNET Lab, which conducts research at the intersection of wireless communications and Information and Communication Technology for Development. Mariya is the recipient of the NSF CAREER award, the Dynamic Spectrum Alliance 2019 Award for University Research on New Opportunities for Dynamic Spectrum Access, and the University at Albany 2019 President’s Award for Exemplary Public Engagement. She is the co-lead for the NSF-supported National Radio Dynamic Zones Partnership and Workshop Series; and a founding member of SpectrumX.
A Better Cardinality Estimator with Fewer Bits, Constant Update Time, and Mergeability
Yang Du, He Huang and Yu-e Sun (Soochow University, China); Kejian Li (Soochow University, Hong Kong); Boyu Zhang and Guoju Gao (Soochow University, China)
Speaker Yang Du
Yang Du is currently a postdoctoral fellow in the School of Computer Science and Technology at Soochow University, P. R. China. He received his B.E. degree from Soochow University in 2015 and Ph.D. degree from University of Science and Technology of China in 2020. His research interests include network traffic measurement and sketch.
RecMon: A Deep Learning-based Data Recovery System for Network Monitoring
Huaiyi Zhao (Institute of Computing Technology, Chinese Academy of Sciences, China); Xinyi Zhang (CNIC & Chinese Academy of Sciences, China); Kun Xie (Hunan University, China); Dong Tian (CNIC Chinese Academy of Sciences, China); Gaogang Xie (CNIC Chinese Academy of Sciences & University of Chinese Academy of Sciences, China)
Speaker Huaiyi Zhao (Institute of Computing Technology, Chinese Academy of Sciences)
Huaiyi Zhao is a Ph.D candidate at Institute of Computing Technology, Chinese Academy of Sciences. His research interests include network architecture, network measurement and AI for network.
LightNestle: Quick and Accurate Neural Sequential Tensor Completion via Meta Learning
Yuhui Li (Hunan University, China); Wei Liang (Hunan University of Science and Technology, China); Kun Xie, Dafang Zhang and Songyou Xie (Hunan University, China); Kuan-Ching Li (Hunan University of Science and Technology, China)
Speaker Yuhui Li (Hunan University)
Excalibur: A Scalable and Low-Cost Traffic Testing Framework for Evaluating DDoS Defense Solutions
Xiang Chen and Hongyan Liu (Zhejiang University, China); Tingxin Sun (Fuzhou University, China); Qun Huang (Peking University, China); Dong Zhang (Fuzhou University, China); Xuan Liu (Yangzhou University & Southeast University, China); Boyang Zhou (Zhejiang Lab, China); Haifeng Zhou (Zhejiang University, China); Chunming Wu (College of Computer Science, Zhejiang University, China)
Speaker Xiang Chen
Xiang is a first-year PhD student at Zhejiang University. His advisors are Prof. Chunming Wu, Prof. Qun Huang, and Prof. Dong Zhang. He has received a Best Paper Award from IEEE/ACM IWQoS 2021 and a Best Paper Candidate from IEEE INFOCOM 2021. His research interests include programmable networks, network virtualization, and network security.
Age of Information
Minimizing Age of Information in Spatially Distributed Random Access Wireless Networks
Nicholas W Jones and Eytan Modiano (MIT, USA)
Speaker Nicholas Jones (MIT)
Nicholas Jones is a PhD candidate at MIT in the Laboratory for Information and Decision Systems, advised by Professor Eytan Modiano. He is interested in optimizing control of wireless networks for real-time and delay-sensitive applications.
Fresh-CSMA: A Distributed Protocol for Minimizing Age of Information
Vishrant Tripathi, Nicholas W Jones and Eytan Modiano (MIT, USA)
Speaker Vishrant Tripathi (MIT)
Vishrant Tripathi is a Ph.D. candidate in the EECS department at MIT, working with Prof. Eytan Modiano at the Laboratory for Information and Decision Systems (LIDS). His research is on modeling, analysis and design of communication networks, with emphasis on wireless and real-time networks. His current focus is on scheduling problems in networked control systems, multi-agent robotics and federated learning.
Age of broadcast and collection in spatially distributed wireless networks
Chirag Rao (US Army Research Laboratory & Massachusetts Institute of Technology, USA); Eytan Modiano (MIT, USA)
We quantify the average broadcast and collection ages in two scenarios: 1) instance-dependent, in which the locations of all nodes and interferers are known, and 2) instance-independent, in which they are not known but are located randomly, and expected age is characterized with respect to node locations. In the instance-independent case, we show that AoB and AoC scale super-exponentially with respect to the radius of the region surrounding the base station. Simulation results highlight how expected AoB and AoC are affected by network parameters such as network density, medium access probability, and the size of the coverage region.
Speaker Chirag Rao
Chirag is a PhD student at MIT's Laboratory for Information and Decision Systems.
Energy-aware Age Optimization: AoI Analysis in Multi-source Update Network Systems Powered by Energy Harvesting
Sujunjie Sun, Weiwei Wu, Chenchen Fu, Xiaoxing Qiu and Luo Junzhou (Southeast University, China)
Speaker Sujunjie Sun (Southeast University)
Sujunjie Sun is currently a Ph.D. student at the Department of Computer Science, Southeast University, Nanjing, China, in 2021. His research interest includes Wireless Networks, Optimization Theroy, Scheduling Algorithm, and Age of Information.