Pub Date : 2025-09-17DOI: 10.1016/j.vehcom.2025.100974
Sunil Prajapat , Dheeraj Kumar , M. Shamim Hossain
The rapid evolution of smart cities and intelligent transportation systems has led to the widespread deployment of Vehicular Ad Hoc Networks (VANETs), enabling real-time inter-vehicular communication and data sharing related to traffic congestion, vehicle location, and road safety. However, the open and dynamic nature of VANETs makes them vulnerable to various security threats, including identity tracing and message forgery. To address these concerns, this paper proposes a quantum aggregate signature scheme that leverages quantum teleportation and untraceable identity mechanisms for secure and privacy-preserving communication in VANETs. The proposed scheme enables the aggregation of multiple signatures into a single, compact signature while preserving the anonymity of vehicular nodes through pseudo-identities and quantum-enhanced privacy techniques. By incorporating quantum teleportation, our scheme ensures quantum-level security for message transmission without directly transferring the secret state. Furthermore, it supports efficient batch verification to authenticate messages from multiple vehicles with minimal computational and communication overhead. The protocol's correctness and security are validated using both Scyther tool-based formal verification and informal analysis, demonstrating strong resistance against existential forgery, impersonation, and traceability attacks. Compared to existing schemes, our approach reduces computational time by 0.053 ms and communication overhead by 778 bytes, making it scalable, efficient, and highly applicable for real-world VANET deployments.
{"title":"Secure quantum aggregate signature scheme for vehicular ad-hoc networks","authors":"Sunil Prajapat , Dheeraj Kumar , M. Shamim Hossain","doi":"10.1016/j.vehcom.2025.100974","DOIUrl":"10.1016/j.vehcom.2025.100974","url":null,"abstract":"<div><div>The rapid evolution of smart cities and intelligent transportation systems has led to the widespread deployment of Vehicular Ad Hoc Networks (VANETs), enabling real-time inter-vehicular communication and data sharing related to traffic congestion, vehicle location, and road safety. However, the open and dynamic nature of VANETs makes them vulnerable to various security threats, including identity tracing and message forgery. To address these concerns, this paper proposes a quantum aggregate signature scheme that leverages quantum teleportation and untraceable identity mechanisms for secure and privacy-preserving communication in VANETs. The proposed scheme enables the aggregation of multiple signatures into a single, compact signature while preserving the anonymity of vehicular nodes through pseudo-identities and quantum-enhanced privacy techniques. By incorporating quantum teleportation, our scheme ensures quantum-level security for message transmission without directly transferring the secret state. Furthermore, it supports efficient batch verification to authenticate messages from multiple vehicles with minimal computational and communication overhead. The protocol's correctness and security are validated using both Scyther tool-based formal verification and informal analysis, demonstrating strong resistance against existential forgery, impersonation, and traceability attacks. Compared to existing schemes, our approach reduces computational time by 0.053 ms and communication overhead by 778 bytes, making it scalable, efficient, and highly applicable for real-world VANET deployments.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100974"},"PeriodicalIF":6.5,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-17DOI: 10.1016/j.vehcom.2025.100972
Sawera Aslam, Daud Khan, Sudeb Mondal, KyungHi Chang
Autonomous driving systems rely heavily on effective data fusion from Vehicle-to-Everything (V2X) networks, where accurate decisions depend on integrating diverse messages from multiple communication interfaces. However, current single-interface communication methods, either PC5 or Uu, limit the achievable autonomy level due to insufficient reliability and situational awareness. To address these limitations, this paper proposes an efficient RSU-centered Message-level fusion framework tailored for intersection-based autonomous driving. The proposed approach strategically assigns CAM, CPM, and SPATEM to the PC5 interface, while DENM and MAPEM are assigned to the Uu interface. A confidence-weighted fusion algorithm is implemented at the RSU aligns timestamps, filters inconsistent inputs, and resolves conflicts to generate unified situational awareness messages every 100 ms. The onboard decision-making model employs a CNN–GRU enhanced Actor–Critic network to optimize decisions for intelligent lane changing, collision avoidance, and traffic flow management. Simulation outcomes confirm that the proposed dual-interface fusion significantly enhances performance compared to single-interface systems, improving the packet delivery ratio to 0.75 at 300 m and achieving decision accuracy improvements of approximately 14–25% across key use cases. Consequently, our framework meets the criteria for autonomy sub-level L4-C, providing a robust foundation for advanced intersection-based autonomous driving systems.
{"title":"RSU-assisted V2X message fusion via PC5 and Uu with actor–critic modeling for autonomous driving under intersection scenario","authors":"Sawera Aslam, Daud Khan, Sudeb Mondal, KyungHi Chang","doi":"10.1016/j.vehcom.2025.100972","DOIUrl":"10.1016/j.vehcom.2025.100972","url":null,"abstract":"<div><div>Autonomous driving systems rely heavily on effective data fusion from Vehicle-to-Everything (V2X) networks, where accurate decisions depend on integrating diverse messages from multiple communication interfaces. However, current single-interface communication methods, either PC5 or Uu, limit the achievable autonomy level due to insufficient reliability and situational awareness. To address these limitations, this paper proposes an efficient RSU-centered Message-level fusion framework tailored for intersection-based autonomous driving. The proposed approach strategically assigns CAM, CPM, and SPATEM to the PC5 interface, while DENM and MAPEM are assigned to the Uu interface. A confidence-weighted fusion algorithm is implemented at the RSU aligns timestamps, filters inconsistent inputs, and resolves conflicts to generate unified situational awareness messages every 100<!--> <!-->ms. The onboard decision-making model employs a CNN–GRU enhanced Actor–Critic network to optimize decisions for intelligent lane changing, collision avoidance, and traffic flow management. Simulation outcomes confirm that the proposed dual-interface fusion significantly enhances performance compared to single-interface systems, improving the packet delivery ratio to 0.75 at 300<!--> <!-->m and achieving decision accuracy improvements of approximately 14–25% across key use cases. Consequently, our framework meets the criteria for autonomy sub-level L4-C, providing a robust foundation for advanced intersection-based autonomous driving systems.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100972"},"PeriodicalIF":6.5,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-15DOI: 10.1016/j.vehcom.2025.100968
Mohsen Eskandari , Andrey V. Savkin , Mohammad Deghat
Low latency, reliable, and stable communication are essential for autonomous driving and mission accomplishment of Internet-of-Vehicles (IoVs) in smart cities. Therefore, future wireless networks will work based on quasi-optic millimeter wave (mmWave) signals for high-rate data transfer. However, given the mobility of vehicles, the mmWave links are prone to outages as they intrinsically rely on directional beamforming to line-of-sight (LoS) paths. Notably, fragile wireless links in dense urban canyons expose autonomous vehicles to safety risks. An unmanned aerial vehicle (UAV) equipped with a reconfigurable holographic surface (RHS) is navigated for establishing aerial LoS links for IoVs. RHS performs beamforming by adjusting the radiation patterns through the holographic surface, so it is energy efficient. The UAV-RHS is supported by terrestrial reconfigurable intelligent surfaces (RISs) installed on building facades, which are utilized to improve coverage and link reliability. The UAV’s navigation objectives are maintaining valid LoS links for IoVs, ensuring quality of service, and minimizing energy consumption. However, an obstacle-free kinematics-aware smooth trajectory, subject to motion constraints, is required for UAV navigation in dense urban environments. Satisfying these navigation objectives and constraints makes the trajectory design with valid LoS links a non-convex NP-hard optimization problem. To address this, we propose, for the first time, training generative adversarial networks (GANs) to generate valid paths in real time. State feedback control with quadratic optimization is proposed to smooth the trajectory. Simulation results are provided to evaluate the proposed method.
{"title":"Joint smooth trajectory design and wireless communication control for mobile internet of vehicles assisted by a UAV and ground RISs","authors":"Mohsen Eskandari , Andrey V. Savkin , Mohammad Deghat","doi":"10.1016/j.vehcom.2025.100968","DOIUrl":"10.1016/j.vehcom.2025.100968","url":null,"abstract":"<div><div>Low latency, reliable, and stable communication are essential for autonomous driving and mission accomplishment of Internet-of-Vehicles (IoVs) in smart cities. Therefore, future wireless networks will work based on quasi-optic millimeter wave (mmWave) signals for high-rate data transfer. However, given the mobility of vehicles, the mmWave links are prone to outages as they intrinsically rely on directional beamforming to line-of-sight (LoS) paths. Notably, fragile wireless links in dense urban canyons expose autonomous vehicles to safety risks. An unmanned aerial vehicle (UAV) equipped with a reconfigurable holographic surface (RHS) is navigated for establishing aerial LoS links for IoVs. RHS performs beamforming by adjusting the radiation patterns through the holographic surface, so it is energy efficient. The UAV-RHS is supported by terrestrial reconfigurable intelligent surfaces (RISs) installed on building facades, which are utilized to improve coverage and link reliability. The UAV’s navigation objectives are maintaining valid LoS links for IoVs, ensuring quality of service, and minimizing energy consumption. However, an obstacle-free kinematics-aware smooth trajectory, subject to motion constraints, is required for UAV navigation in dense urban environments. Satisfying these navigation objectives and constraints makes the trajectory design with valid LoS links a non-convex NP-hard optimization problem. To address this, we propose, for the first time, training generative adversarial networks (GANs) to generate valid paths in real time. State feedback control with quadratic optimization is proposed to smooth the trajectory. Simulation results are provided to evaluate the proposed method.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100968"},"PeriodicalIF":6.5,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.vehcom.2025.100966
Abishek Subramanian, Aurenice Oliveira
Vehicle to Infrastructure (V2I) connectivity has historically relied on Dedicated Short Range Communication (DSRC) and more recently Cellular Vehicle to Everything (C-V2X). However, DSRC adoption has slowed due to high deployment costs, whereas C-V2X, limited to the 5.9 GHz sub 6 GHz band, provides modest data rates mainly suitable for safety critical messages. Emerging V2I services, such as high resolution sensor sharing and cooperative perception, demand multi gigabit throughput to transfer large volumes of data (4–10 GB) between vehicles and Mobile Edge Computing (MEC) servers, requirements exceeding the capacity of sub-6 GHz technologies. This study explores a novel distributed architecture utilizing a federated learning paradigm for optimizing mmWave beamforming processes in V2I communication systems. By leveraging multiple non-RF modality sensors (GPS and LiDAR) and deep learning models, this approach aims to enhance the global model's adaptability and reduce the sub-6 GHz channel usage. The proposed system uses client-biased selection strategies, including MaxLoss and Heuristic Multi-Arm Bandit, to train and update the global model, demonstrating significant improvements in convergence rates and overall performance. Simulation results using the Infocom FLASH dataset validate the framework's efficiency, highlighting its potential for real-world deployment in dynamic environments.
{"title":"A novel distributed architecture incorporating deep learning and biased selection for vehicular communication mmWaves beamforming","authors":"Abishek Subramanian, Aurenice Oliveira","doi":"10.1016/j.vehcom.2025.100966","DOIUrl":"10.1016/j.vehcom.2025.100966","url":null,"abstract":"<div><div>Vehicle to Infrastructure (V2I) connectivity has historically relied on Dedicated Short Range Communication (DSRC) and more recently Cellular Vehicle to Everything (C-V2X). However, DSRC adoption has slowed due to high deployment costs, whereas C-V2X, limited to the 5.9 GHz sub 6 GHz band, provides modest data rates mainly suitable for safety critical messages. Emerging V2I services, such as high resolution sensor sharing and cooperative perception, demand multi gigabit throughput to transfer large volumes of data (4–10 GB) between vehicles and Mobile Edge Computing (MEC) servers, requirements exceeding the capacity of sub-6 GHz technologies. This study explores a novel distributed architecture utilizing a federated learning paradigm for optimizing mmWave beamforming processes in V2I communication systems. By leveraging multiple non-RF modality sensors (GPS and LiDAR) and deep learning models, this approach aims to enhance the global model's adaptability and reduce the sub-6 GHz channel usage. The proposed system uses client-biased selection strategies, including MaxLoss and Heuristic Multi-Arm Bandit, to train and update the global model, demonstrating significant improvements in convergence rates and overall performance. Simulation results using the Infocom FLASH dataset validate the framework's efficiency, highlighting its potential for real-world deployment in dynamic environments.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100966"},"PeriodicalIF":6.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145009039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-21DOI: 10.1016/j.vehcom.2025.100967
Tanja Pavleska , Massimiliano Masi , Giovanni Paolo Sellitto , Helder Aranha
Cooperative Intelligent Transport Systems (C-ITS) involve a complex network of diverse components that communicate with each other and with their environment. These systems are essential for improving transport efficiency, enabling smoother movement of people and goods, and supporting economic growth. However, due to their highly connected nature, C-ITS face major challenges related to cybersecurity and interoperability—both of which are directly linked to safety. Managing evolving software and standards while ensuring security places a heavy burden on architects, security experts, and organizational stakeholders.
In this work, we propose a methodology to support the secure design and deployment of C-ITS systems. The approach is based on established standards and adaptable to other critical sectors, such as healthcare, energy and smart cities, but is here tailored to the specific context of the transport domain. Our main contribution is a governance-based framework for secure deployment of standards, aimed at addressing the problem of standards maintenance, interoperability, and architectural sustainability. We demonstrate its application through a real-world use case involving secure vehicle-to-infrastructure (V2I) communication.
{"title":"Architecture-based governance for secure-by-design Cooperative Intelligent Transport Systems","authors":"Tanja Pavleska , Massimiliano Masi , Giovanni Paolo Sellitto , Helder Aranha","doi":"10.1016/j.vehcom.2025.100967","DOIUrl":"10.1016/j.vehcom.2025.100967","url":null,"abstract":"<div><div>Cooperative Intelligent Transport Systems (C-ITS) involve a complex network of diverse components that communicate with each other and with their environment. These systems are essential for improving transport efficiency, enabling smoother movement of people and goods, and supporting economic growth. However, due to their highly connected nature, C-ITS face major challenges related to cybersecurity and interoperability—both of which are directly linked to safety. Managing evolving software and standards while ensuring security places a heavy burden on architects, security experts, and organizational stakeholders.</div><div>In this work, we propose a methodology to support the secure design and deployment of C-ITS systems. The approach is based on established standards and adaptable to other critical sectors, such as healthcare, energy and smart cities, but is here tailored to the specific context of the transport domain. Our main contribution is a governance-based framework for secure deployment of standards, aimed at addressing the problem of standards maintenance, interoperability, and architectural sustainability. We demonstrate its application through a real-world use case involving secure vehicle-to-infrastructure (V2I) communication.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100967"},"PeriodicalIF":6.5,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144885569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-12DOI: 10.1016/j.vehcom.2025.100965
Ndeye Penda Fall , Cherif Diallo , Adel Mounir Said , Michel Marot , Hossam Afifi , Hassine Moungla
Intelligent Reflecting Surfaces or IRSs are energy-efficient technologies used as “passive” relays to increase coverage. Often fixed, they enable connectivity to users in positions that are difficult for the base station to access, or that are blind. Most studies propose fixed IRS positioning, with the disadvantage of covering only a fixed zone. Therefore, in the vehicular environment, it would be interesting to see the feasibility of placing IRSs following traffic. We, therefore, propose to study the mobile placement of IRSs in a vehicular context first by using an optimizer and then by relying on heuristics. In the first part, we compare fixed and mobile IRS positioning. Then, for the heuristic part, we present different IRS election strategies, which we have compared. Performances are compared between fixed and mobile placement in the first part, and between one and two hops in the second part, all while analyzing the impact of different parameters on these results. We also evaluated the impact of a trajectory predictor and the dataset on these results.
{"title":"Exploring the use of mobile IRS in a vehicular context","authors":"Ndeye Penda Fall , Cherif Diallo , Adel Mounir Said , Michel Marot , Hossam Afifi , Hassine Moungla","doi":"10.1016/j.vehcom.2025.100965","DOIUrl":"10.1016/j.vehcom.2025.100965","url":null,"abstract":"<div><div>Intelligent Reflecting Surfaces or IRSs are energy-efficient technologies used as “passive” relays to increase coverage. Often fixed, they enable connectivity to users in positions that are difficult for the base station to access, or that are blind. Most studies propose fixed IRS positioning, with the disadvantage of covering only a fixed zone. Therefore, in the vehicular environment, it would be interesting to see the feasibility of placing IRSs following traffic. We, therefore, propose to study the mobile placement of IRSs in a vehicular context first by using an optimizer and then by relying on heuristics. In the first part, we compare fixed and mobile IRS positioning. Then, for the heuristic part, we present different IRS election strategies, which we have compared. Performances are compared between fixed and mobile placement in the first part, and between one and two hops in the second part, all while analyzing the impact of different parameters on these results. We also evaluated the impact of a trajectory predictor and the dataset on these results.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100965"},"PeriodicalIF":6.5,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-11DOI: 10.1016/j.vehcom.2025.100964
E. Soleimani-Nasab , S. Coleri
Next generation intelligent transportation systems (ITS) are expected to use visible light communications (VLC) as a complementary technology to the existing radio frequency (RF)-based technologies in vehicle-to-everything (V2X) communication to provide secure and reliable transmission by exploiting the directivity and impermeability of light. Moreover, reconfigurable intelligent surfaces (RIS) are a promising solution to enhance the coverage and reliability of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications by modifying the phase, amplitude and polarization of incoming electromagnetic waves. Most previous works assumed double Rayleigh and Rayleigh fading channels for the RF links, with RIS-assisted setup lacking direct links between vehicles, and non-random distributions for vehicle movement in the VLC links. In this paper, we analyze the physical layer security performance of RIS-assisted hybrid RF/VLC links for both V2V and V2I scenarios. We also assume a direct line-of-sight (LoS) link between legitimate vehicles. In the existence of co-channel interference (CCI), an eavesdropper attempts to receive the information. We employ an accurate method to derive an exact expression for the cumulative distribution function (CDF) of RIS-assisted links combined with a direct link. More specifically, we derive closed-form expressions of secrecy outage probability (SOP), average secrecy capacity (ASC), probability of strictly positive secrecy capacity (PSPSC), effective secrecy throughput (EST), and intercept probability (IP). We assume double Nakagami-m fading for the V2V links, Nakagami-m channel for the V2I links, and log-Normal fading and uniform distribution for both longitude separation of Tx and Tx-Rx distance, leading to random path-loss. The correctness of the derivations is verified by using extensive Monte Carlo simulations for both V2V and V2I scenarios.
{"title":"Hybrid RF/VLC intelligent vehicular communications: A secrecy analysis","authors":"E. Soleimani-Nasab , S. Coleri","doi":"10.1016/j.vehcom.2025.100964","DOIUrl":"10.1016/j.vehcom.2025.100964","url":null,"abstract":"<div><div>Next generation intelligent transportation systems (ITS) are expected to use visible light communications (VLC) as a complementary technology to the existing radio frequency (RF)-based technologies in vehicle-to-everything (V2X) communication to provide secure and reliable transmission by exploiting the directivity and impermeability of light. Moreover, reconfigurable intelligent surfaces (RIS) are a promising solution to enhance the coverage and reliability of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications by modifying the phase, amplitude and polarization of incoming electromagnetic waves. Most previous works assumed double Rayleigh and Rayleigh fading channels for the RF links, with RIS-assisted setup lacking direct links between vehicles, and non-random distributions for vehicle movement in the VLC links. In this paper, we analyze the physical layer security performance of RIS-assisted hybrid RF/VLC links for both V2V and V2I scenarios. We also assume a direct line-of-sight (LoS) link between legitimate vehicles. In the existence of co-channel interference (CCI), an eavesdropper attempts to receive the information. We employ an accurate method to derive an exact expression for the cumulative distribution function (CDF) of RIS-assisted links combined with a direct link. More specifically, we derive closed-form expressions of secrecy outage probability (SOP), average secrecy capacity (ASC), probability of strictly positive secrecy capacity (PSPSC), effective secrecy throughput (EST), and intercept probability (IP). We assume double Nakagami-<em>m</em> fading for the V2V links, Nakagami-<em>m</em> channel for the V2I links, and log-Normal fading and uniform distribution for both longitude separation of Tx and Tx-Rx distance, leading to random path-loss. The correctness of the derivations is verified by using extensive Monte Carlo simulations for both V2V and V2I scenarios.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100964"},"PeriodicalIF":6.5,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-05DOI: 10.1016/j.vehcom.2025.100962
Gaber A. Al-Absi , Yong Fang , Adnan A. Qaseem , Huda Al-Absi
The development of the Internet of Vehicles (IoV) has greatly increased connectivity, making the In-Vehicle Network (IVN) more susceptible to intrusions. Furthermore, the utilization of Electronic Control Units (ECUs) in current vehicles has experienced a significant increase, establishing the Controller Area Network (CAN) as the widely used standard in the automotive field. However, it lacks provisions for authentication. The attackers have exploited these weaknesses to launch various attacks on CAN-based IVN. Sequential data approaches such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) have emerged as prominent approaches in this domain, contributing significantly to the evolution of the Intrusion Detection System (IDS). However, these methods are limited in feature extraction as they depend solely on previously interacted hidden states, potentially overlooking critical features. Additionally, capturing the complex spatial-temporal dynamics of CAN messages remains a significant challenge.
In response to these challenges, we propose the Dynamic Spatial-Temporal Graph-Transformer Network for In-vehicle Network Intrusion Detection System, denoted as the “DST-IDS”. It comprises three modules: a graph spatial-temporal embedding module that converts the row CAN messages correlation into latent graph representations, a spatial-temporal learning module, and a classification module. The second module utilizes a graph-transformer network to capture and learn the dynamic spatial-temporal dependencies between CAN messages. The last module classifies the learnt features into either normal or attack messages. The model was evaluated on two publicly available datasets (CAR-Hacking and IVN-IDS), achieving exceptionally high accuracy scores of 0.999999 and 0.9996, respectively. These results demonstrate that the proposed model significantly outperforms state-of-the-art methods in detection accuracy and false alarm rate for in-vehicle network intrusion detection.
{"title":"DST-IDS: Dynamic spatial-temporal graph-transformer network for in-vehicle network intrusion detection system","authors":"Gaber A. Al-Absi , Yong Fang , Adnan A. Qaseem , Huda Al-Absi","doi":"10.1016/j.vehcom.2025.100962","DOIUrl":"10.1016/j.vehcom.2025.100962","url":null,"abstract":"<div><div>The development of the Internet of Vehicles (IoV) has greatly increased connectivity, making the In-Vehicle Network (IVN) more susceptible to intrusions. Furthermore, the utilization of Electronic Control Units (ECUs) in current vehicles has experienced a significant increase, establishing the Controller Area Network (CAN) as the widely used standard in the automotive field. However, it lacks provisions for authentication. The attackers have exploited these weaknesses to launch various attacks on CAN-based IVN. Sequential data approaches such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) have emerged as prominent approaches in this domain, contributing significantly to the evolution of the Intrusion Detection System (IDS). However, these methods are limited in feature extraction as they depend solely on previously interacted hidden states, potentially overlooking critical features. Additionally, capturing the complex spatial-temporal dynamics of CAN messages remains a significant challenge.</div><div>In response to these challenges, we propose the Dynamic Spatial-Temporal Graph-Transformer Network for In-vehicle Network Intrusion Detection System, denoted as the “DST-IDS”. It comprises three modules: a graph spatial-temporal embedding module that converts the row CAN messages correlation into latent graph representations, a spatial-temporal learning module, and a classification module. The second module utilizes a graph-transformer network to capture and learn the dynamic spatial-temporal dependencies between CAN messages. The last module classifies the learnt features into either normal or attack messages. The model was evaluated on two publicly available datasets (CAR-Hacking and IVN-IDS), achieving exceptionally high accuracy scores of 0.999999 and 0.9996, respectively. These results demonstrate that the proposed model significantly outperforms state-of-the-art methods in detection accuracy and false alarm rate for in-vehicle network intrusion detection.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100962"},"PeriodicalIF":6.5,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-05DOI: 10.1016/j.vehcom.2025.100963
Naeem Ahmed, Farman Ali, Qingzhe Deng, Qiuming Zhu, Xiaomin Chen, Boyu Hua, Junwei Bao, Kai Mao
Unmanned aerial vehicles (UAVs) are increasingly integrated into maritime communication systems, presenting unique challenges due to complex maritime scenario. By considering six-dimensional (6D) motion of both UAV and ship alongside sea cluttering and wave shadowing phenomena, this paper presents a novel non-stationary 6D geometry-based multiple-input multiple-output (MIMO) channel model for UAV to ship (U2S) communications for maritime scenario. Besides, the dynamic interactions between UAV and ship motions and maritime environments are also described in the proposed model. The time-variant channel coefficient and channel parameters like, path loss (PL), shadow fading (SF), Doppler frequencies, wave shadowing, sea cluttering, time-variant distances, time-variant delay, time-variant power, time-variant angles, are derived and analyzed thoroughly in this proposed method. Additionally, the theoretical and statistical properties like, probability density function (PDF), autocorrelation function (ACF), level crossing rate (LCR), Doppler power spectral density (DPSD), and signal to clutter noise ratio (SCNR) are investigated with the effect of sea cluttering and wave shadowing. Finally, the validation of the channel model and its theoretical derivations highlight its suitability for evaluating and designing U2S communication systems in maritime environments. The suggested model can be useful for improving U2S communication systems, to enhance reliability and performance in maritime communication environments.
{"title":"Impact of sea cluttering and wave shadowing on U2S MIMO channel model incorporating UAV-ship 6D motion in maritime environments","authors":"Naeem Ahmed, Farman Ali, Qingzhe Deng, Qiuming Zhu, Xiaomin Chen, Boyu Hua, Junwei Bao, Kai Mao","doi":"10.1016/j.vehcom.2025.100963","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100963","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are increasingly integrated into maritime communication systems, presenting unique challenges due to complex maritime scenario. By considering six-dimensional (6D) motion of both UAV and ship alongside sea cluttering and wave shadowing phenomena, this paper presents a novel non-stationary 6D geometry-based multiple-input multiple-output (MIMO) channel model for UAV to ship (U2S) communications for maritime scenario. Besides, the dynamic interactions between UAV and ship motions and maritime environments are also described in the proposed model. The time-variant channel coefficient and channel parameters like, path loss (PL), shadow fading (SF), Doppler frequencies, wave shadowing, sea cluttering, time-variant distances, time-variant delay, time-variant power, time-variant angles, are derived and analyzed thoroughly in this proposed method. Additionally, the theoretical and statistical properties like, probability density function (PDF), autocorrelation function (ACF), level crossing rate (LCR), Doppler power spectral density (DPSD), and signal to clutter noise ratio (SCNR) are investigated with the effect of sea cluttering and wave shadowing. Finally, the validation of the channel model and its theoretical derivations highlight its suitability for evaluating and designing U2S communication systems in maritime environments. The suggested model can be useful for improving U2S communication systems, to enhance reliability and performance in maritime communication environments.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"14 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Blockchain Networks (BCNs) have become critical in various applications, and ensuring their security against cyber threats is essential for maintaining their reliability and confidentiality. This paper investigates the crucial role of Intrusion Detection Systems (IDS) in enhancing the security of BCNs, particularly in the context of real-time vehicle monitoring. The study begins with a thorough overview of blockchain technology, highlighting key security challenges such as vulnerabilities in smart contracts, the risk of 51% attack, and regulatory compliance issues. It emphasizes the need for robust security measures, with IDS emerging as a vital defense mechanism. IDS employs advanced techniques, including signature-based detection, anomaly detection, and behavioral analysis, to monitor network traffic and user activities, thereby improving the resilience of BCNs by identifying and addressing potential threats in real-time. For real-time vehicle monitoring, IDS is essential for ensuring the integrity and security of data, preventing unauthorized access, and maintaining user trust in blockchain-enabled transportation systems. This paper provides a comprehensive analysis of IDS's role in securing blockchain networks for real-time vehicle monitoring, offering valuable insights into enhancing the security of these systems in a dynamic cyber environment.
{"title":"Blockchain-enabled intrusion detection systems for real-time vehicle monitoring","authors":"Mritunjay Shall Peelam, Vinay Chamola, Brijesh Kumar Chaurasia","doi":"10.1016/j.vehcom.2025.100961","DOIUrl":"https://doi.org/10.1016/j.vehcom.2025.100961","url":null,"abstract":"Blockchain Networks (BCNs) have become critical in various applications, and ensuring their security against cyber threats is essential for maintaining their reliability and confidentiality. This paper investigates the crucial role of Intrusion Detection Systems (IDS) in enhancing the security of BCNs, particularly in the context of real-time vehicle monitoring. The study begins with a thorough overview of blockchain technology, highlighting key security challenges such as vulnerabilities in smart contracts, the risk of 51% attack, and regulatory compliance issues. It emphasizes the need for robust security measures, with IDS emerging as a vital defense mechanism. IDS employs advanced techniques, including signature-based detection, anomaly detection, and behavioral analysis, to monitor network traffic and user activities, thereby improving the resilience of BCNs by identifying and addressing potential threats in real-time. For real-time vehicle monitoring, IDS is essential for ensuring the integrity and security of data, preventing unauthorized access, and maintaining user trust in blockchain-enabled transportation systems. This paper provides a comprehensive analysis of IDS's role in securing blockchain networks for real-time vehicle monitoring, offering valuable insights into enhancing the security of these systems in a dynamic cyber environment.","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"15 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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