IEEE INFOCOM 2023

Program at a Glance

IEEE INFOCOM 2023

Session Break-1-Day2: Coffee Break Session Lunch-Day2: Conference Lunch Session Break-2-Day2: Coffee Break Session Dinner-Day2: Conference Dinner

Session A-4: Blockchain Session A-6: Quantum Computing/Networking

Session B-4: Federated Learning 4 Session B-6: Federated Learning 5

Session C-4: UAV Session C-5: Optical and Mobile Session C-6: Programmable Switches

Session D-4: Fingerprinting and Classification Session D-5: Internet Measurement/Monitoring Session D-6: Age of Information

Session E-4: Optimization Session E-5: Wireless/Mobile Security 1 Session E-6: Wireless/Mobile Security 2

Session F-4: Activity Sensing

Session G-4: Traffic Shaping and Inspection

Session Demo-2: Demo Session 2

Session Panel: Panel - Networking: An Agenda for the Next Decade

Session Award: A Reflection with INFOCOM Achievement Award Winner

Session Poster-1: Poster Session 1

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)

Spectrum crowdsensing systems do not provide labeled data near real-time yet. We propose a framework that relies solely on Power Spectrum Density (PSD) data collected by low-cost RTL-SDR receivers. A major hurdle is to design a system that is computationally efficient for near real-time operation, yet using only the limited knowledge of 2 MHz bandwidth in low-cost spectrum sensors. First, we present a method for unsupervised transmission detection that works with PSD data already collected by the backend of the crowdsensing platform and that provides stable detection of transmission boundaries. Second, we introduce a data-driven deep learning solution to classify the radio frequency communication technology used by the transmitter, using transmission features in a compressed space extracted from single PSD measurements over at most 2 MHz band for inference to support near real-time operation. We build an experimental platform, and evaluate our framework with real-world data collected from 47 different sensors deployed across Europe. We show that our framework yields an average classification accuracy close to 94.25% over the testing dataset, with a maximum latency of 3.4 seconds when running in a major crowdsensing network.

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)

Wireless charging is a promising solution for charging battery-driven devices pervasively deployed in the Internet of Things (IoT). However, the wide deployment of wireless charging stations is vulnerable to the device masquerade attack, which causes financial loss when billing or charging system damages like overheating and explosion. Device fingerprinting is a classical technique to thwart the device masquerade attack. But existing works either are vulnerable to forging or require specialized equipment, which is not suitable for wireless charging.

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)

The literature and the news regularly report cases of exploiting Universal Serial Bus (USB) devices as attack tools for malware injections and private data exfiltration. To protect against such attacks, security researchers proposed different solutions to verify the identity of a USB device via side-channel information (e.g., timing or electromagnetic emission). However, such solutions often make strong assumptions on the measurement (e.g., electromagnetic interference-free area around the device), on a device's state (e.g., only at the boot or during specific actions), or are limited to one particular type of USB device (e.g., flash drive or input devices).

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)

Despite the substantial success of deep learning for modulation classification, models trained on a specific transmitter configuration and channel model often fail to generalize well to other scenarios with different transmitter configurations, wireless fading channels, or receiver impairments such as clock offset. This paper proposes Contrastive Learning with Self-Reconstruction called CLSR-AMC to learn good representations of signals resilient to channel changes. While contrastive loss focuses on the differences between individual modulations, the reconstruction loss captures representative features of the signal. Additionally, we develop three data augmentation operators to emulate the impact of channel fading and receiver imperfection impairments without exhaustive modeling of different channel profiles. We perform extensive experimentation with commonly used datasets. We show that CLSR-AMC outperforms its counterpart based on contrastive learning for the same amount of labelled data by significant average accuracy gains of 24.29%, 17.01%, and 15.97% in Additive White Gaussian Noise (AWGN), Rayleigh+AWGN, and Rician+AWGN channels, respectively.

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.

Session Chair

Francesco Restuccia

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)

Cardinality estimation is a fundamental problem with diverse practical applications. HyperLogLog (HLL) has become a standard in practice because it offers good memory efficiency, constant update time, and mergeability. Some recent work achieved better memory efficiency, but typically at the cost of impractical update time or losing mergeability, making them incompatible with applications like network-wide traffic measurement. This work presents SpikeSketch, a better cardinality estimator that reduces memory usage of HLL by 37% without sacrificing other crucial metrics. We adopt a bucket-based data structure to promise constant update time, design a smoothed log4 ranking and a spike coding scheme to compress cardinality observables into buckets, and propose a lightweight mergeable lossy compression to balance memory usage, information loss, and mergeability. Then we derive an unbiased estimator for recovering cardinality from the lossy-compressed sketch. We further implement SpikeSketch on the NetFPGA-SUME board. Theoretical and empirical results show that SpikeSketch can work as a drop-in replacement for HLL because it achieves a near-optimal MVP (memory-variance-product) of 4.08 (37% smaller than HLL) with constant update time and mergeability. Its memory efficiency even defeats ACPC and HLLL, the state-of-the-art lossless-compressed sketches using linear-time compression to reduce memory usage.

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)

Network monitoring systems struggle with the issue that the measurement data is incomplete, with only a subset of origin-destination (OD) pairs or time slots observed, due to the high deployment and measurement cost. Recent studies show that the missing data can be inferred from partial measurements using neural network models and tensor methods. However, these recovery methods fail to achieve accuracy, adaptability and high speed simultaneously. In this paper, we propose RecMon, a deep learning-based data recovery system that satisfies all three criteria. Global spatio-temporal attention and a data augmentation algorithm are proposed to improve model accuracy. A semisupervised learning-based scheme is devised to quickly update the model. We conduct extensive experiments on three real-world datasets to compare RecMon with four state-of-the-art methods in terms of online recovery performance. The experimental results show that RecMon can adapt to the latest state of the network and accurately recover network measurement data in less than 100 milliseconds. When 90% of the data is missing, the recovery accuracy of RecMon improves over the strongest baseline method by 22.7%, 16.0%, and 8.2% in the three datasets, respectively.

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)

Network operation and maintenance rely heavily on network traffic monitoring. Due to the measurement overhead reduction, lack of measurement infrastructure, and unexpected transmission error, network traffic monitoring systems suffer from incomplete observed data and high data sparsity problems. Recent studies model missing data recovery as a tensor completion task and show good performance. Although promising, the current tensor completion models adopted in network traffic data recovery lack of an effective and efficient retraining scheme to adapt to newly arrived data while retaining historical information. To solve the problem, we propose LightNestle, a novel sequential tensor completion scheme based on meta-learning, which designs (1) an expressive neural network to transfer spatial knowledge from previous embeddings to current embeddings; (2) an attention-based module to transfer temporal patterns into current embeddings in linear complexity; and (3) an meta-learning-based algorithms to iteratively recover missing data and update transfer modules to catch up with learned knowledge. We conduct extensive experiments on two real-world network traffic datasets to assess our performance. The result demonstrates that our proposed methods achieve both fast retraining and high recovery accuracy.

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)

To date, security researchers evaluate their solutions of mitigating denial-of-service (DDoS) attacks via kernel-based or kernel-bypassing testing tools. However, kernel-based tools exhibit poor scalability in attack traffic generation while kernel-bypassing tools introduce unacceptable monetary cost. We propose Excalibur, a scalable and low-cost testing framework for evaluating DDoS defense solutions. The key idea is to leverage the programmable switch to perform testing tasks with Tbps-level scalability and low cost. Specifically, Excalibur offers intent-based primitives to enable academic researchers to customize testing tasks on demand. Moreover, in view of switch resource limitations, Excalibur coordinates both a server and a programmable switch to jointly perform testing tasks. It realizes flexible attack traffic generation, which requires a large number of resources, in the server while using the switch to increase the sending rate of attack traffic to Tbps-level. Our experiments on a 64×100 Gbps Tofino switch demonstrate that Excalibur achieves orders-of-magnitude higher scalability and lower cost than existing tools.

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.

Session Chair

Gang Zhou

Minimizing Age of Information in Spatially Distributed Random Access Wireless Networks

Nicholas W Jones and Eytan Modiano (MIT, USA)

We analyze Age of Information (AoI) in wireless networks where nodes use a spatially adaptive random access scheme to send status updates to a central base station. We show that the set of achievable AoI in this setting is convex, and design policies to minimize weighted sum, min-max, and proportionally fair AoI by setting transmission probabilities as a function of node locations. We show that under the capture model, when the spatial topology of the network is considered, AoI can be significantly improved, and we obtain tight performance bounds on weighted sum and min-max AoI. Finally, we design a policy where each node sets its transmission probability based only on its own distance from the base station, when it does not know the positions of other nodes, and show that it converges to the optimal proportionally fair policy as the size of the network goes to infinity.

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)

We consider the design of distributed scheduling policies that minimize age of information in single-hop wireless networks. The centralized max-weight policy is known to be nearly optimal in this setting. Hence, our goal is to design a distributed CSMA scheme that can mimic its performance. To that end, we propose a distributed protocol called Fresh-CSMA and show that in an idealized setting, our protocol can match the scheduling decisions of the max-weight policy with high probability in each time-slot, and also match the theoretical performance guarantees of the max-weight policy over the entire time horizon. We then consider a more realistic setting and study the impact of protocol parameters on the probability of collisions and the overhead caused by the distributed nature of the protocol. Finally, we provide simulations that support our theoretical results and show that the performance gap between the ideal and realistic versions of Fresh-CSMA is small.

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 consider a wireless network with a base station broadcasting and collecting time-sensitive data to and from spatially distributed nodes in the presence of wireless interference. The Age of Information (AoI) is the time that has elapsed since the most-recently delivered packet was generated, and captures the freshness of information. In the context of broadcast and collection, we define the Age of Broadcast (AoB) to be the amount of time elapsed until all nodes receive a fresh update, and the Age of Collection (AoC) as the amount of time that elapses until the base station receives an update from all nodes.
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)

This work studies the Age-of-Information (AoI) minimization problem in the information gathering network systems, where time-sensitive data updates are collected from multiple information sources, which are equipped with a battery and harvest energy from ambient energy sources. In such systems, the transmission is available only when there is energy remained in the battery, which is jointly effected by the energy arrival pattern and transmission scheduling policy. This work studies the fundamental impact of the energy arrival on the AoI-optimization transmission scheduling by developing the closed-form expression of average AoI and analyzing its theoretical properties. For the unit battery case, the closed-form expression of the average AoI is derived and the optimal policy is proposed by analyzing the KKT conditions. For the arbitrary finite battery size, the closed-form expression of AoI under SRS policy space with infinite battery capacity is firstly analyzed. Then based on the analysis of the property in the AoI expression, a policy named Max Energy-Aware Weight (MEAW) is proposed by applying Lyapunov optimization, which achieves $2$-approximation in the full policy space. Experimental results validate the theoretical results and show that MEAW performs close to the theoretical lower bound and outperforms the state-of-the-art schemes.

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.

Session Chair

Clement Kam

Program at a Glance