Workshops

Session AidTSP-TS1: AidTSP Technical Session I Session AidTSP-TS2: AidTSP Technical Session II Session AidTSP-TS3: AidTSP Technical Session III Session AidTSP-TS4: AidTSP Technical Session IV
Session AoI-Open: Opening Session Session AoI-Key: Keynote Session AoI-SCH: Scheduling and Optimization for AoI Session AoI-CBrk: Virtual Coffee Break Session AoI-BEY: Beyond AoI Metrics
Session CNERT-O: Opening Session Session CNERT-K: Keynote Session CNERT-P: Panel Session CNERT-S1: Session 1, Experimentation Enabling Capabilities Session CNERT-S2: Session 2, Services and network design Session CNERT-S3: Session 3, Wireless Session CNERT-D: Demo Session
Session DeepWireless-OS: Opening Session Session DeepWireless-KS1: Keynote Session 1 Session DeepWireless-TS1: Technical Session 1 - Deep Learning for Wireless Security Session DeepWireless-TS2: Technical Session 2 - Deep Learning for Wireless Applications Session DeepWireless-KS2: Keynote Session 2 Session DeepWireless-TS3: Technical Session 3 - Deep Learning for 5G Communications Session DeepWireless-CS: Closing Session
Session DroneCom-TS1: Technical Session I Session DroneCom-KS1: Keynote Session I Session DroneCom-TS2: Technical Session II Session DroneCom-TS3: Technical Session III Session DroneCom-KS2: Keynote Session II Session DroneCom-TS4: Technical Session IV
Session FOGML-S1: Opening Remarks Session FOGML-S2: Federated Learning Session FOGML-S3: Distributed Learning and Edge Computing
Session GI-00: Session#0 - Opening Session with Invited Keynote Session GI-01: Session#1 - Applying Machine Learning to Next Generation Internet Session GI-02: Session#2 - Performance Optimization of Next Generation Internet
Session ICCN-KS1: Security in Wireless Body Area Networks Session ICCN-B1: Virtual Coffee Break Session ICCN-S1: Cloud and Edge Computing Session ICCN-S2: Cloud and Edge Security Session ICCN-B2: Virtual Lunch Break Session ICCN-S3: Cloud and Edge Computing Session ICCN-S4: Cloud and Edge Computing
Session MobiSec-S1: AI & Security (I) Session MobiSec-K: Keynote: AI for Cybersecurity and Security of AI Session MobiSec-S2: AI & Security (II) Session MobiSec-S3: Mobile & Edge Security Session MobiSec-S4: Communication Security
Session NG-OPERA-OS: Opening Session Session NG-OPERA-KS: Keynote Session Session NG-OPERA-PS: Panel Session Session NG-OPERA-Break-1: Break Session NG-OPERA-TS1: Technical Session 1: Machine Learning and Resource Optimization for 6G RANs Session NG-OPERA-Lunch: Lunch Session NG-OPERA-TS2: Technical Session 2: System-level Solutions for Open RANs Session NG-OPERA-Break-2: Break Session NG-OPERA-TS3: Technical Session 3: Non-terrestrial Integration and Posters Session NG-OPERA-ACS: Awards and Closing
Session NetSciQCom-S1: Keynote Talk Session NetSciQCom-S2: Contributed Talks
Session PerAI-6G-Opening: Opening Session Session PerAI-6G-Keynote-1: Keynote Session 1 Session PerAI-6G-Keynote-2: Keynote Session 2 Session PerAI-6G-Session-1: Session 1: AI applications in 6G Session PerAI-6G-Session-2: Session 2: Distributed Learning in 6G Session PerAI-6G-Session-3: Session 3: Digital Twin in 6G Session PerAI-6G-Session-4: Session 4: Resource Management in 6G Session PerAI-6G-Closing: Closing Session
Session WISARN-O: Opening Session WISARN-K1: Keynote Session WISARN-CB: Virtual Coffee Break Session WISARN-S1: Technical Session
Session Wireless-Sec-I: Wireless Security I Session Wireless-Sec-II: Wireless Security II

Session Wireless-Sec-I

Wireless Security I

Conference
2:00 PM — 3:30 PM EDT
Local
May 20 Sat, 11:00 AM — 12:30 PM PDT

Vulnerability Exploit Pattern Generation and Analysis for proactive security risk mitigation for 5G networks

Wiktor S?dkowski (Nokia Networks, Poland); Clifton Fernandes (Nokia, United Kingdom (Great Britain)); Rakshesh P Bhatt (Nokia Networks Bangalore, India); Kodandram Ranganath (Nokia, India)

0
This paper presents a proactive intelligent mechanism to detect possible variants of known vulnerability exploits being attempted on any component of wireless networks. Vulnerability Exploit Pattern Analyzer presented in this paper can prevent possible zero-day attacks by learning from the available known exploits from published databases. There have been incidents like WannaCry ransomware attack, where a known Operating Systems vulnerability was exploited sometime after it was published,
and even the patches were available in public. In 5G wireless networks, the number of network functions and devices are
expected to be in millions. For most of the CVEs published, different exploits are also published, and available in online
databases like Exploit DB. It is likely that attackers take such exploits, manipulate them to create different variants of such
exploits and launch attacks on networks. For example, https://www.exploit-db.com/ has more than 8000 exploits published only for SQL injection kind of
vulnerabilities. Older vulnerability exploits can inspire creation of newer ones for other products. 5G and future wireless networks having service-based architecture at the core will require more proactive approaches to predict any misuse of emerging or manipulated variants of known exploits. This paper proposes one possible solution for the same and presents results from experiments done using patterns generated from a remote command injection vulnerability exploit.
Speaker Wiktor Sedkowski

Wiktor Sędkowski graduated in Teleinformatics at the Wrocław University of Science and Technology, specialized in cybersecurity field. He is an expert on cyber threats. CISSP, CCSP, OSCP, OSWE and MCTS certified security engineer. Works as security researcher at Nokia. Ph.D. candidate at Opole University of Technology.

CVCA: A Complex-Valued Classifiable Autoencoder for MmWave Massive MIMO Physical Layer Authentication

Xinyuan Zeng and Chao Wang (Xidian University, China); Chengcai Wang (School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China); Zan Li (Xidian University, China)

0
For protecting millimeter wave (mmWave) communications from clone attacks, this paper employs the deep learning to propose a physical layer authentication (PLA) approach for detecting attackers and classifying multiple legitimate nodes simultaneously. Different from conventional upper-layer authentication mechanisms, the proposed PLA approach exploits the spatial and temporal characteristics of mmWave channels to extract the unique fingerprints for building a lightweight channel-based authentication method. However, the existing threshold-based PLA methods could not discriminate multiple nodes, and supervised learning based approaches have limited application due to the unavailability of attackers' channel state information (CSI) in practice. Besides, traditional real-valued deep neural networks cannot exploit the phase information of complex channels efficiently, which is unsuitable for designing the PLA scheme. Considering these, we propose a complex-valued classifiable autoencoder induced PLA scheme that includes a novel complex-valued long short-term memory (LSTM) module. Simulation results validate the superiority of our proposed PLA approach by comparing it with existing approaches and demonstrate that the detection probability of clone attacks positively correlates with antenna number. The classification performance is satisfactory even under the challenging experimental condition.
Speaker Xinyuan Zeng (Xidian University)

Xinyuan Zeng received the B.S. degree in communication engineering from Yangzhou University, Yangzhou, China, in 2020. He is currently pursuing the Ph.D. degree in information and communication engineering with the National key laboratory of integrated service, Xidian University, China. His current research interests include wireless communications, physical layer authentication, and deep learning.

A Lightweight Preprocessing Scheme for Secret Key Generation from mmWave Massive MIMO Channel Measurements

Lijun Yang, Xinchao Ge, Qianyi Zhu and Lin Guo (Nanjing University of Posts and Telecommunications, China)

0
Due to the new channel characteristics, previous preprocessing methods for physical layer key generation cannot be directly applied in mmWave Massive MIMO systems. Actually, due to the intolerable high computation complexity and time consumption caused by large dimension of Massive MIMO channel, even the previous optimal decorrelation method principal component analysis (PCA) is not suitable for mmWave Massive MIMO system. To address the problem, in this paper, we propose a lightweight preprocessing approach for secret key generation suitable for mmWave Massive MIMO channel by exploiting its unique channel characteristics namely sparsity and high directivity. The proposed scheme brings the following advantages. 1) It is lightweight. The computational complexity is quiet low, which is dependent on the number of paths, and independent of the number of antennas. This property makes our proposal very suitable for mmWave Massive MIMO channel. 2) It is robust against noise; thus, it can be used to generate secret key with quiet high key agreement ratio and good randomness even in low SNR regimes. Analysis and numerical results show that the proposed approach outperforms the previous optimal method PCA in terms of bit agreement ratio and computational complexity, and is on par with PCA in terms of randomness and key generation rate (KGR).
Speaker Lijun YANG (Nanjing University of Posts and Telecommunications)

Lijun YANG received the bachelor's degree in Information Engineering and the Ph.D. degree in Information Security from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2007 and 2015, respectively. She is currently a Lecturer at Nanjing University of Posts and Telecommunications. Her current research interests include the Internet of Things, 5G/B5G, physical layer security, wireless security, and deep learning.

Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks

Yi Shi, Yalin E Sagduyu and Tugba Erpek (Virginia Tech, USA)

0
Decentralized federated learning (DFL) is an effective approach to train a deep learning model at multiple nodes over a multi-hop network, without the need of a server having direct connections to all nodes. In general, as long as nodes are connected potentially via multiple hops, the DFL process will eventually allow each node to experience the effects of models from all other nodes via either direct connections or multi-hop paths, and thus is able to train a high-fidelity model at each node. We consider an effective attack that uses jammers to prevent the model exchanges between nodes. There are two attack scenarios. First, the adversary can attack any link under a certain budget. Once attacked, two end nodes of a link cannot exchange their models. Secondly, some jammers with limited jamming ranges are deployed in the network and a jammer can only jam nodes within its jamming range. Once a directional link is attacked, the receiver node cannot receive the model from the transmitter node. We design algorithms to select links to be attacked for both scenarios. For the second scenario, we also design algorithms to deploy jammers at optimal locations so that they can attack critical nodes and achieve the highest impact on the DFL process. We evaluate these algorithms by using wireless signal classification over a large network area as the use case and identify how these attack mechanisms exploits various learning, connectivity, and sensing aspects. We show that the DFL performance can be significantly reduced by jamming attacks launched in a wireless network and characterize the attack surface as a vulnerability study before the safe deployment of DFL over wireless networks.
Speaker Yi Shi (Virginia Tech)

Dr. Yi Shi is a Research Associate Professor in Commonwealth Cyber Initiative and a Faculty (by Courtesy) in the Bradley Department of Electrical and Computer Engineering, Virginia Tech. He is a Senior Member of IEEE. Dr. Shi's research focuses on machine learning, algorithm design, and optimization for 5G and NextG networks, cognitive radio networks, MIMO and cooperative communication networks, sensor networks, ad hoc networks, satellite networks, and social networks. His work has appeared in leading IEEE and ACM journals and top-tier international conferences. He is a recipient of Test of Time Paper Award at IEEE INFOCOM 2023, a recipient of Best Paper Award at IEEE HST 2018, a recipient of Best Student Paper Award at ACM WUWNet 2014, a recipient of the only Best Paper Award Runner-Up at IEEE INFOCOM 2011, and a recipient of Best Paper Award at IEEE INFOCOM 2008. Dr. Shi was IEEE INFOCOM 2021 Distinguished TPC member and IEEE Communications Surveys and Tutorials Exemplary Editor in 2014. Dr. Shi is an Editor for IEEE Communications Surveys and Tutorials and IEEE Transactions on Cognitive Communications and Networking, and was a Co-Chair for several IEEE and ACM Workshops, Conference Tracks, and Symposia.

Session Chair

Danda B. Rawat (Howard University, United States); Min Song (Stevens Institute of Technology, United States)

Session Wireless-Sec-II

Wireless Security II

Conference
4:00 PM — 5:30 PM EDT
Local
May 20 Sat, 1:00 PM — 2:30 PM PDT

A Finite Blocklength Approach for Wireless Hierarchical Federated Learning in the Presence of Physical Layer Security

Honan Zhang (Southwest Jiaotong University, China); Chuanchuan Yang (State Key Laboratory of Advanced Optical Communication Systems and Networks, Peking University, China); Bin Dai (Southwest Jiaotong University, China)

0
The wireless hierarchical federated learning (HFL) in the presence of physical layer security (PLS) issue is revisited. Though a framework of this problem has been established in the previous work, practical secure finite blocklength (FBL) coding scheme remains unknown. In this paper, we extend the already existing FBL coding scheme for the white Gaussian channel with noisy feedback to the wireless HFL with quasi-static fading duplex channel, and derive achievable rate and upper bound on the eavesdropper's uncertainty of the extended scheme. The results of this paper are further explained via simulation results.
Speaker Haonan Zhang (Southwest Jiaotong University, China)

Haonan Zhang received the B.S. degree in communication engineering from Southwest Jiaotong University, in 2017, and the M.S. degree in communication engineering from Southwest Jiaotong University, in 2020. He is currently pursuing the Ph.D. degree in communication engineering with Southwest Jiaotong University, Chengdu, China. His research interests include physical layer security and wireless communication.

A Secure RPL Rank Computation and Distribution Mechanism for Preventing Sinkhole Attack in IoT-based Systems

Alekha Kumar Mishra (National Institute of Technology Jamshedpur, India); Deepak Puthal (Khalifa University, United Arab Emirates); Asis Kumar Tripathy (Vellore Institute of Technology, Vellore, India)

0
The sinkhole attack detection mechanisms that have been reported till date are distributed in nature. Defending a sinkhole attack requires protecting the act of bypassing of forging the rank computation task. This paper proposes a secure centralized sinkhole detection mechanism to ensure protection of rank computation process against sinkhole nodes in the IoT-Based Systems. The expectation maximization algorithm is used to cluster the nodes into groups bearing the same rank. The distribution of ranks to the nodes is protected by the signature of the sink from forging. The experimental results show that the optimal rank computation is computationally efficient and secure against sinkhole nodes.
Speaker Alekha Mishra (NIT Jamshedpur)

Alekha Kumar Mishra has received his Ph.D degree from NIT Rourkela, India in the year of 2014. He has also received his M.Tech degree in Information Security from NIT Rourkela in the year of 2009. Currently, Dr. Mishra is working as a faculty member in the department of computer science and engineering, NIT Jamshedpur, India. His research interests include IoT, Network Security, Security Threat Modelling and Analysis, Energy-efficient Routing in LLN, and Cybersecurity.



Wireless Signal Denoising Using Conditional Generative Adversarial Networks

Haolin Tang and Yanxiao Zhao (Virginia Commonwealth University, USA); Guodong Wang (MCLA, USA); Changqing Luo (Virginia Commonwealth University, USA); Wei Wang (San Diego State University, USA)

0
Wireless signal strength plays a critical role in wireless security. For example, we can intentionally reduce transmission power at a transmitter to prevent eavesdropping. Later the receiver will employ signal denoising techniques to enhance the Signal-to-noise ratio (SNR). Also, signals are deteriorated by noise and interference during transmission. Therefore, wireless signal enhancement or denoising is a critical challenge. This paper tackles this challenge and investigates an adversarial learning-based approach for wireless signal denoising, which will correspondingly enhance signal strength. Specifically, we design a conditional generative adversarial network at the receiver to establish an adversarial game between a generator and a discriminator. The generator receives the noisy signal and aims to generate the denoised signal, while the discriminator aims to force the denoised signal to match the noisy signal exactly. Unlike traditional signal denoising methods that estimate the noise or interference in the noisy signals, our proposed method estimates and learns the features of real noise-free signals, which is more adaptive to dynamic wireless communication environments. We conduct simulations on signals with four different modulations to evaluate the performance. The results demonstrate that our method can generate denoised signals effectively and outperforms other traditional methods.
Speaker Haolin Tang (Virginia Commonwealth University)

Haolin Tang is currently pursuing the Ph.D. degree with the Department of Electrical and Computer Engineering at Virginia Commonwealth University. His research interests include next-generation wireless communication, cyber security, computer vision, and machine learning.  

Performance Evaluation of Quantum-resistant Open Fronthaul Communications in 5G

Ricardo Harrilal-Parchment, Isabela Fernandez Pujol and Kemal Akkaya (Florida International University, USA)

0
As 5G is offering new services that include improved security for its core functions, there is an effort to secure all domains in 5G including control, data, and synchronization. This has turned the attention to 5G Fronthaul communication security, which has not been considered crucial for past generations of cellular technologies. With overall information security efforts increasing preparation for the deployment of post-quantum cryptographic algorithms, there is also a need to assess the feasibility and overhead when such algorithms are considered for 5G Open Fronthaul communications between the radio heads in the base stations and distributed units within the network. This is crucial for protocols such as eCPRI/CPRI which has certain real-time requirements to meet. To this end, this paper first proposes an integrated security solution that combines IEEE 802.11AE (MACsec) along with a post-quantum-based EAP- TLS authentication within a typical Ethernet-based Fronthaul topology. We then implement a proof of concept to integrate all these components in a virtualized setting for the first time and evaluate the associated transmission delay with the eCPRI/CPRI messages under various settings. The results demonstrate that MACsec can be a viable option that can satisfy the real-time requirements even if it is used with post-quantum-based EAP- TLS1.3 that offers perfect forward secrecy.
Speaker Ricardo Harrilal-Parchment (Florida International University)

Ricardo is currently pursuing his Masters in Cybersecurity with Florida International University while simultaneously conducting research with the Advanced Wireless & Security (AdWise Lab). His current research interests include 5G communications, post-quantum cryptography, payment channel systems, and IoT security.

Session Chair

Danda B. Rawat (Howard University, United States); Min Song (Stevens Institute of Technology, United States)


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