Vehicular Communications最新文献

{"title":"DST-IDS: Dynamic spatial-temporal graph-transformer network for in-vehicle network intrusion detection system","authors":"Gaber A. Al-Absi ,&nbsp;Yong Fang ,&nbsp;Adnan A. Qaseem ,&nbsp;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":"2025-10-01","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}

{"title":"Energy and experimental trust-based task offloading in the domain of connected autonomous vehicles","authors":"Sachin Kumar Gupta ,&nbsp;Anuradha Banerjee","doi":"10.1016/j.vehcom.2025.100954","DOIUrl":"10.1016/j.vehcom.2025.100954","url":null,"abstract":"<div><div>Task offloading among connected and autonomous vehicles (CAVs) has recently gained much attention. The current literature in this context mostly optimizes only the criterion of energy and latency. Further, issues like connectivity and spontaneous attitude of selflessness have remained unexplored despite their importance and probable contribution to preserving vehicles' energy and reducing overall delay in completing the tasks. Therefore, the key objectives of the present study are maximization of residual energy and percentage of successful offloading, as well as minimization of energy consumption and delay. We have also considered trust, which has two components; efficiency and certainty. Efficiency is defined as the inverse of the estimated time duration required to complete the execution of the current task based on the history of the previous sessions. Certainty is related to the stability of the connection between the server and task off-loader vehicles and the selfless cooperation of the server, as revealed from the history of communication with the off-loader. Experimental results show that our proposed method of offloading tasks based on energy and experiential trust (OTEET) increases the offload success percentage and reduces cost by approximately 40%, which can be considered a significant improvement.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100954"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563852","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}

{"title":"Exploring the use of mobile IRS in a vehicular context","authors":"Ndeye Penda Fall ,&nbsp;Cherif Diallo ,&nbsp;Adel Mounir Said ,&nbsp;Michel Marot ,&nbsp;Hossam Afifi ,&nbsp;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":"2025-10-01","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}

{"title":"MetaCAN: An optimized adaptive hybrid metaheuristic-based intrusion detection system for CAN bus security","authors":"Kadir Ileri ,&nbsp;Abdur Rakib ,&nbsp;Soufiene Djahel","doi":"10.1016/j.vehcom.2025.100956","DOIUrl":"10.1016/j.vehcom.2025.100956","url":null,"abstract":"<div><div>The Controller Area Network (CAN) bus is a message-based protocol widely used in modern vehicles to facilitate communication between various Electronic Control Units (ECUs). However, its simplistic design lacks fundamental security measures, making it highly susceptible to cyberattacks. These vulnerabilities pose significant risks to vehicle safety, highlighting the critical need for implementation of effective intrusion detection systems (IDS). Therefore, in this paper, a machine learning based IDS optimized through an adaptive hybrid metaheuristic approach, named MetaCAN, is proposed to secure the CAN bus. MetaCAN leverages the complementary strengths of particle swarm optimization (PSO) for fast convergence and cuckoo search (CS) for robust global search to ensure effective hyperparameter tuning and model optimization. MetaCAN is evaluated using three real-world datasets including Survival Analysis, Car Hacking: Attack &amp; Defense Challenge 2020, and OTIDS. Unlike traditional binary detection systems, MetaCAN offers multi-class attack detection by identifying five distinct attack types including Denial of Service (DoS), fuzzy, masquerade, malfunction, and replay attacks. Moreover, the detection accuracy of the system is enhanced through a feature engineering process that introduces two effective features such as Time Interval and ID Repetition Count. The experimental results show that MetaCAN consistently outperforms existing IDS solutions targeted the same datasets, making it a promising solution for securing the CAN bus in real-world vehicular environments.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100956"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614566","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}

{"title":"Fault tolerant mission management for UAV under random threat using Markov decision process","authors":"Md Muzakkir Quamar ,&nbsp;Ali Nasir ,&nbsp;Sami ELFerik","doi":"10.1016/j.vehcom.2025.100959","DOIUrl":"10.1016/j.vehcom.2025.100959","url":null,"abstract":"<div><div>This research introduces a comprehensive framework for mission accomplishment of unmanned aerial vehicles (UAVs) operating in threat-prone zones. Leveraging a Markov Decision Process (MDP), the proposed model ensures mission accomplishment and optimal resource utilization by incorporating UAV state-of-charge, post-fault capabilities, and threat navigation strategies. The framework addresses sensor, actuator, and vision camera faults, enabling dependable operations even under adverse conditions. A key feature of the model is the integration of UAV battery levels to evaluate operational range relative to mission objectives, optimizing task distribution while conserving energy. Additionally, the incorporation of adaptive navigation modes enhances UAV agility and robustness by enabling threat avoidance during operation, closely emulating real-world scenarios. By synthesizing repair protocols, recharging strategies, and stochastic modeling of recurrent goals and threats, the framework offers a holistic solution to improve resilience and mission success in hostile environments. Stochastic dynamic programming ensures the rapid application of precomputed optimal policies during mission execution. A simulation-based case study demonstrates the framework's effectiveness in navigating threats, mitigating faults, and ensuring mission reliability in energy-constrained scenarios.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100959"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696823","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}

{"title":"Securing the unforeseen: Enhancing VANET security with dynamic honeypots and attack rate analysis","authors":"Mohammed A. Abdelmaguid,&nbsp;Hossam S. Hassanein,&nbsp;Mohammad Zulkernine","doi":"10.1016/j.vehcom.2025.100946","DOIUrl":"10.1016/j.vehcom.2025.100946","url":null,"abstract":"<div><div>Addressing known threats constitutes the foundational layer of cybersecurity defenses. However, the real challenge emerges in anticipating and mitigating unforeseen attacks. Current security methodologies work well against familiar threats but often struggle with new or unforeseen attacks. This paper examines the Trust Origin within Trust Management Systems (TMS) by linking it to the network attack rate, thereby refining trust assessments and predicting new attacks. Combining Machine Learning (ML) algorithms with honeypots, we offer a comprehensive defense for Vehicular Ad-hoc Networks (VANETs), adept at detecting anticipated and unexpected attacks through attack rate analysis. Our methodology evaluates the network's security status by examining its ability to identify known attacks, referred to as prepared-for attacks. Subsequently, this information serves as a foundation to predict future attacks that still need to be identified, termed unprepared-for attacks. Through extensive testing, we demonstrate the viability of a dual strategy that encompasses the detection of prepared-for attacks and the prediction of unprepared-for ones. Experimental results reveal a significant improvement in predicting unprepared-for attacks, evidenced by enhanced accuracy, precision, and recall. Additionally, we conduct experiments to determine the optimal deployment of honeypots for maximum efficiency.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100946"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337779","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}

{"title":"A secure and efficient lattice-based conditional privacy-preserving authentication protocol for the VANET","authors":"Dongxian Shi ,&nbsp;Xuwen Nie ,&nbsp;Ming Xu ,&nbsp;Hongbing Cheng ,&nbsp;Muhammad Alam","doi":"10.1016/j.vehcom.2025.100958","DOIUrl":"10.1016/j.vehcom.2025.100958","url":null,"abstract":"<div><div>On the benefit of the prosperous development of the communication techniques and automatic driving, the vehicle ad hoc network (VANET) is becoming more and more commonplace. The information transmitted in the VANETs is exposed in the open wireless communication environment, so it is vulnerable to several types of attacks. To balance the information security and privacy in the VANETs, there are a great number of conditional privacy-preserving authentication protocols proposed. However, only a few of them are resistant to quantum attacks, and these existing quantum-resistant works are unsatisfactory, ether are insecure or suffer from other problems. In this paper, we propose a secure and efficient lattice-based conditional privacy-preserving authentication protocol for the VANETs, which can achieve authentication and privacy protection, and a batch verification method is provided to further optimize the performance. Compared with the existing counterparts, our protocol is secure, efficient, and achieving lowest communication overhead. We provide several parameter sets, and the protocol achieves least execution time under some of them. We also show a security proof of the protocol in the random oracle model, based on the assume of inhomogeneous small integer solution problem.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100958"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703540","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}

{"title":"Architecture-based governance for secure-by-design Cooperative Intelligent Transport Systems","authors":"Tanja Pavleska ,&nbsp;Massimiliano Masi ,&nbsp;Giovanni Paolo Sellitto ,&nbsp;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":"2025-10-01","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}

{"title":"BFP-Net: A DL-based ISAC beamforming prediction method for extended vehicle","authors":"Peng Chen ,&nbsp;Ting Zhou ,&nbsp;Zhimin Chen ,&nbsp;Fan Meng ,&nbsp;Jun Liu","doi":"10.1016/j.vehcom.2025.100945","DOIUrl":"10.1016/j.vehcom.2025.100945","url":null,"abstract":"<div><div>To enable the next generation of connected autonomous vehicles, the millimeter wave (mmWave)-based integrated sensing and communication (ISAC) system will be a critical technology in future vehicle-to-everything (V2X) networks. However, the rapid mobility of vehicles and the narrow beamwidth of mmWave signals present significant challenges for beam alignment, and point-target modeling methods often lead to substantial overhead, high latency, and complications. To address these issues, in this paper, a hybrid analog-digital (HAD) multi-input multi-output (MIMO) ISAC framework is adopted for the mmWave-based V2X network to reduce hardware costs and power consumption. Then, considering the narrow beamwidth of the mmWave system, the vehicle is modeled as an extended surface target with multiple scattering points, and a new association technique for these points is developed to improve prediction accuracy. Hence, a deep learning (DL)-based beamforming prediction network, namely beamforming prediction network (BFP-Net), is designed according to the ISAC signal beam prediction protocol and enables roadside units (RSUs) to transmit ISAC signals effectively for both downlink communication and sensing operations. The BFP-Net leverages a convolutional neural network long-short-term memory (CNN-LSTM) architecture to capture spatial and temporal correlations, providing enhanced modeling capabilities for beam prediction. Moreover, for highly dynamic vehicles, the BFP-Net predicts optimal beams for future time slots by extracting features from the received echo signals and eliminates the repetitive beam training inherent in the traditional communication protocol. Simulation results demonstrate that the proposed method significantly outperforms extended Kalman filter (EKF)-based methods in the mmWave V2X scenario, achieving higher beam gains and better performance for high-speed vehicles, and substantially reduces the overhead associated with beam training compared to the conventional neural network relying on pilot signals.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100945"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243172","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}

{"title":"Energy efficiency optimization for UAV-mounted IRS assisted ISAC systems under statistical CSI","authors":"Peng Wang ,&nbsp;Huizhi Tang ,&nbsp;Demin Li ,&nbsp;Yihong Zhang ,&nbsp;Xuemin Chen","doi":"10.1016/j.vehcom.2025.100953","DOIUrl":"10.1016/j.vehcom.2025.100953","url":null,"abstract":"<div><div>Integrated Sensing and Communication (ISAC) systems are advantageous for enhancing both communication and sensing capabilities, but their performance is significantly impacted by signal blockages in dynamic vehicular environments. An Unmanned Aerial Vehicle (UAV)-mounted Intelligent Reflective Surface (IRS) for air-to-ground communication and sensing can significantly enhance coverage and deployment flexibility. However, the additional power consumption of the UAV-mounted IRS (UIRS) remains a challenge. To mitigate this, we propose a novel UIRS-assisted ISAC system that aims to maximize communication energy efficiency (EE) while meeting sensing quality-of-service (QoS) requirements by optimizing the UAV trajectory, IRS passive beamforming, and base station (BS) active beamforming. Due to the complex and dynamic nature of wireless channels, acquiring Channel State Information (CSI) is challenging, especially with the UAV's mobility and the passive mode of IRS. Therefore, statistical CSI is adopted in the proposed scheme. The optimization problem is reformulated into a tractable form and solved by decomposing it into three subproblems, which include using the Dinkelbach transformation for fractional programming in EE calculation, Successive Convex Approximation (SCA) for UAV trajectory optimization, and Semi-Definite Relaxation (SDR) for both active and passive beamforming designs. An alternating optimization (AO)-based framework iteratively solves all subproblems, with proven algorithm convergence and computational efficiency. Simulation results demonstrate that the proposed UIRS-assisted ISAC system significantly improves both communication and sensing performance compared to benchmark schemes.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100953"},"PeriodicalIF":5.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570530","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}