VTC 2025 Spring Conference’s Shorts

Fairness Optimization in Next-Generation Dense WLANs with ISAC

Dense wireless local area networks (WLANs) have been developed to enable high-capacity indoor wireless communications. Nevertheless, due to the inherent interference among densely deployed access points (APs) and the competitive channel access scheme, the balance of network load among APs may degrade significantly. This challenge motivates the development of a novel association policy exploiting the integrated sensing and communication (ISAC) technique to enhance network fairness. In this paper, we propose a novel optimization framework for ISAC resource allocation in the downlink of dense WLANs to maximize the total network fairness utility function while guaranteeing users’ quality-of-service (QoS) requirements. Unlike conventional association policies, our proposed approach effectively determines the associated APs based on signal-to-noise ratio (SNR) and measured angle of sensing signals to ensure network fairness. By leveraging coalition game and binary relaxation techniques, we further transform the non-convex resource allocation design problem and address them via the alternating optimization (AO) technique. Simulation results demonstrate that the proposed ISAC-based resource allocation framework can effectively improve the data rate over the network and, simultaneously ensure fairness among stations.Fairness Optimization in Next-Generation Dense WLANs with ISACLonghai Huang, Jing Zhang, Huazhong University of Science and Technology; Derrick Wing Kwan Ng, University of New South Wales

Cooperative Evolutionary Computation for Multi-RAT Edge Computing

Multi-radio access technology (multi-RAT) enabled mobile edge computing (MEC) has emerged as a promising paradigm for supporting heterogeneous applications. However, efficiently managing resources for both ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services in large-scale networks remains challenging. In this paper, we investigate a joint optimization problem involving user association and bandwidth allocation in multi-RAT-enabled MEC systems. We propose a novel cooperative evolutionary framework operated based on the interplay between inner and outer agents to efficiently optimize large-scale networks. Extensive simulation results demonstrate that the proposed approach significantly outperforms the conventional single-RAT MEC system and several representative evolutionary computation algorithms.Cooperative Evolutionary Computation for Multi-RAT Edge ComputingZhao-Kun Shao, Kangyu Gao, Hanyang University; Gyeong-June Hahm, Kyung-Yul Cheon, Hyenyeon Kwon, Seungkeun Park, Electronics and Telecommunications Research Institute; Changjun Zhou, Zhonglong Zheng, Zhejiang Normal University; Sang-Woon Jeon, Hanyang University

Secure Communication and Eavesdropper Localization via Channel Knowledge Map

This paper proposes an advanced framework utilizing channel knowledge map (CKM) to strengthen secure communication and facilitate eavesdropper localization. CKM is established to learn the spatially unique channel characteristics, enabling it to distinguish between legitimate user equipment (UE) and eavesdropper channels without requiring prior knowledge of the latter. This new capability addresses fundamental challenges in conventional physical-layer security methods that rely heavily on prior information of channel state information (CSI). By leveraging CKM, the framework enables multiple critical functionalities, including eavesdropper detection and localization, as well as beamforming design for secure communication. Simulation results validate the advantages of the proposed CKM-enabled approach over several benchmarks, such as statistical model-based method, particularly in terms of signal-to-leakage-and-noise ratio (SLNR) and localization performance.Secure Communication and Eavesdropper Localization via Channel Knowledge MapDi Wu, Southeast University, Purple Mountain Laboratories; Yong Zeng, Southeast University

Malware Containment Via Firewall Placement in IoT Networks

One of the biggest risks that wireless IoT networks encounter is malware or botnet epidemics. Malware can propagate from one device to another device that exists in its coverage range as long as there are no check points (firewalls) to protect that device. Firewalls can be hardware (special devices) or software licensed to be activated on a limited number of devices. Unfortunately, in both cases the number of firewalls that can be installed in any network is limited due to cost constraints. Therefore, it is mandatory to make efficient use of that available number of firewalls. In this paper we consider optimization of the firewall placement in a massive IoT network. The objective of the optimization problem is to reduce the number of firewalls required to divide the network into a given number of virtually isolated clusters. This clustering problems is non-convex and is known to be NP- hard. However, we provide an efficient algorithm to solve it, and we compare its performance to the well known K-Means clustering algorithm. Simulation results show that the average performance of the proposed algorithms outperforms performance of the the K-Means algorithm. Although many network clustering algorithms have been considered in the literature with different objectives, to the best of our knowledge, the objective of the clustering considered in this paper has not been considered before. Furthermore, the proposed clustering algorithm does not contradict with any other clustering objective. Once the firewalls are placed, any other clustering algorithm can be used to satisfy a different objective.Malware Containment Via Firewall Placement in IoT NetworksWessam Mesbah, King Fahd University of Petroleum and Minerals

LSHA: A Lightweight and Secure Handover Authentication Scheme Based on Wireless Key for 5G...

The advent of 5G-V2X technology has significantly enhanced the capabilities of Connected Automated Vehicles (CAVs). However, in high-mobility 5G-based vehicular network scenarios, CAVs need to frequently handover between base stations. Inadequate handover authentication schemes pose many security threats, including impersonation attacks, man-in-the-middle attacks, and desynchronization attacks, among others. To address these challenges, we propose a lightweight and secure handover authentication scheme called LSHA, which leverages physical layer wireless key generation (PLKG) technology to produce highly random temporary keys for handovers, preventing the leakage of critical parameters to potential attackers in the wireless network. Moreover, LSHA achieve mutual authentication between CAVs and base stations while ensuring message freshness using randomly generated numbers. Additionally, the use of temporary identity variable (CID) guarantees the privacy and untraceability of CAVs. Finally, we formally prove the security of the proposed scheme using the Scyther tool and compare the communication overhead, authentication delay, and robustness of different handover schemes through simulations. Compared to the standard 5G handover authentication scheme, LSHA reduces the transmitted data by 29%, lowers communication delay by 31.8%, and even under unknown attacks, reduces the average data for successful handovers by 25.38%, demonstrating significant performance and security advantages.LSHA: A Lightweight and Secure Handover Authentication Scheme Based on Wireless Key for 5G-V2XFan Xiao, Yuan Zhong, Long Li, Dongming Li, Southeast University

Exploring Multiclass Data Poisoning within an Industrial 5G Private Network

Machine Learning (ML) models have proven effective in optimizing wireless and private networks. However, recent research highlights the threat of data poisoning attacks on ML models. To analyze such a threat on an industrial 5G private network, this work investigates the effectiveness of data poisoning against it. We primarily focus on poisoning four ML models: Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), and Artificial Neural Networks (ANN), at three poisoning levels: 10%, 15%, and 20%. Our research shows that all models introduce instability in the network, rather than optimization, whereas neural networks are less affected by data poisoning compared to other models. At the 20% data poisoning level, model performance degrades by about 6-7% for SVM, RF, and DT, while ANN shows a minimal disruption of 2%.Exploring Multiclass Data Poisoning within an Industrial 5G Private NetworkAnum Paracha, Birmingham City University, UK; Oluwatobi Baiyekusi, Birmingham City University; Junaid Arshad, Birmingham City University, UK; De Mi, Birmingham City University; Chen Lu, Shenzhen Institute of Information Technology; Yunsheng Zhang, School of International Exchange and Cooperation; Fengwei Wang, Lei Chen, ZTE Corporation; Jintao Zhang, MarineSat Network Technology Co., Ltd

VehChain - An Experimental Study of a Blockchain-based CAN Bus Security Solution

The Controller-Area-Network (CAN} Bus has become a vulnerable target because of its ability to manage real-time plaintext vehicle data. Vulner-abilities include Man-in-the-Middle (MitM), Sniffing, Spoofing, Replay, Denial-of-Service (DoS), and other attacks originating from embedded malware or external attacks from the connected Internet. The existing work for CAN Bus enhancement relies on a centralized authority, which causes a single point of security failure. This work discusses decentralized CAN Bus security through a blockchain-reminiscent method, improving the security against CAN Bus attacks. A lab test-bed is built using different Electronic Control Units (ECUs} for performance validation, including ECUs manufactured by Microchip Technology. Instead of simulation, which is employed in other approaches, security attacks are conducted on a Buick Encore vehicle. The results from this study exhibit that VehChain protects CAN Frame data from Sniffing, Spoofing, Replay, and Denial of Service (DoS} attacks by utilizing secure Hashed Message Authentication Codes (HMACs}, message counters, and key synchronization.VehChain - An Experimental Study of a Blockchain-based CAN Bus Security SolutionAatman Joshi, University of Louisville; Anup, Kumar; Derock Xie, KCD; Yi Huang, InfoBeyond Technology LLC; Jayant K. Debnath, Bin Xie, InfoBeyond Technology

Privacy-Utility-Fairness: A Balanced Approach to Vehicular-Traffic Management System

Location-based vehicular traffic management faces significant challenges in protecting sensitive geographical data while maintaining utility for traffic management and fairness across regions. Existing state-of-the-art solutions often fail to meet the required level of protection against linkage attacks and demographic biases, leading to privacy leakage and inequity in data analysis. In this paper, we propose a novel algorithm designed to address the challenges regarding the balance of privacy, utility, and fairness in location-based vehicular traffic management systems. In this context, utility means providing reliable and meaningful traffic information, while fairness ensures that all regions and individuals are treated equitably in data use and decision-making. Employing differential privacy techniques, we enhance data security by integrating query-based data access with iterative shuffling and calibrated noise injection, ensuring that sensitive geographical data remains protected. We ensure adherence to epsilon-differential privacy standards by implementing the Laplace mechanism. We implemented our algorithm on vehicular location-based data from Norway, demonstrating its ability to maintain data utility for traffic management and urban planning while ensuring fair representation of all geographical areas without being overrepresented or underrepresented. Additionally, we have created a heatmap of Norway based on our model, illustrating the privatized and fair representation of the traffic conditions across various cities. Our algorithm provides privacy in vehicular traffic management by effectively balancing fairness and utility.Privacy-Utility-Fairness: A Balanced Approach to Vehicular-Traffic Management SystemPoushali Sengupta, Sabita Maharjan, Frank Eliassen, Yan Zhang, University of Oslo

Multi-RIS-Assisted Secure Communications in mmWave Vehicular Network

With the surge in wireless data traffic, integrating millimeter-wave (mmWave) technology into vehicular networks enables high-speed communication. Meanwhile, the rising demand for secure wireless communication drives the use of reconfigurable intelligent surfaces (RIS) to enhance physical layer security (PLS) through intelligent channel control. This paper investigates PLS approaches in multi-RIS-assisted mmWave vehicular communication under stochastic geometry architecture. Taking the dynamically changing and random nature of vehicular network topologies into account, we propose a vehicular network association scheme for a typical vehicle. In this scheme when the quality of the direct link deteriorates due to obstacles or other factors, RIS-assisted communication ensures a more stable connection. By leveraging stochastic geometry theory, a tractable analytical framework is established to evaluate the secrecy performance of the downlink transmission comprehensively. Specifically, the closed-form expressions of connection outage probability (COP) and secrecy outage probability (SOP) are derived. Simulation results demonstrate that introducing RIS into vehicular networks and utilizing the proposed association scheme can significantly improve the security of vehicular networks.Multi-RIS-Assisted Secure Communications in mmWave Vehicular NetworkPeiguo Sun, Ying Ju, Yiting Yan, Lei Huang, Lei Liu, Xidian University; Mian Ahmad Jan, University of Sharjah; Prof. Kok-Lim Alvin Yau, Universiti Tunku Abdul Rahman; Shahid Mumtaz, Institute of Telecommunications

Generalized Jamming Detection in NR V2X using Gaussian Mixture Model

In intelligent transportation systems, highly reliable information exchange via New Radio (NR) Vehicles-to-Everything (V2X) communications is pivotal for ensuring road safety and enhancing traffic efficiency. However, the open nature of wireless channels renders NR V2X communications highly vulnerable to interference, thereby presenting opportunities for potential attackers to exploit. This paper proposes a method based on Gaussian Mixture Models (GMM) for detecting potential jamming attacks in NR V2X communications. This method detects jamming employing parameters calculated by stochastic geometry, without relying on abundant datasets. By utilizing the expectation-maximization algorithm for iterative, this method is able to identify jamming attacks based on the measured power. Extensive numerical analysis has demonstrated that our proposed method exhibits superior accuracy compared to existing schemes. Besides, this method is general, and capable of detecting jamming attacks under unknown jammer characteristics and varying vehicle densities.Generalized Jamming Detection in NR V2X using Gaussian Mixture ModelQiang Fu, Mingkai Yu, Northwestern Polytechnical University; Fei Hui, Chang’an University; Jiajia Liu, Northwestern Polytechnical University