Vehicular Communications最新文献_第6页

MIEC: A magnetism-inspired framework for MS deployment and joint task offloading and resource allocation optimization in LMREC MIEC：一个磁力启发的框架，用于mrec中的MS部署和联合任务卸载和资源分配优化

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-06-25 DOI: 10.1016/j.vehcom.2025.100948

Mingyu Zhang , Zhibo Sun , Fengjie Li , Hong Zhang

With the rapid growth of Internet of Things (IoT) devices, Mobile Edge Computing (MEC) faces challenges in meeting increasing computational demands, especially in resource-constrained environments. To address this issue, we propose the LEO Satellite-MS-RSU Edge Computing (LMREC) framework, which integrates Mobile Servers (MSs), Low Earth Orbit (LEO) satellite networks, and Roadside Units (RSUs) into an innovative edge computing architecture. We first introduce “attraction” and “repulsion” metrics to model the willingness of vehicular satellite servers to serve specific users. Subsequently, we design a Magnetic Equilibrium Algorithm (MEA), which dynamically adjusts the MS deployment and service allocation by balancing user-driven attraction and server repulsion. To address the latency sensitivity of task scheduling and user satisfaction in LMREC, we formulate a mixed-integer nonlinear programming (MINLP) optimization problem for task offloading and resource allocation. Since this optimization problem is intractable to solve in polynomial time, we propose a Magnetic Domain Migration Algorithm (MDMA) to obtain a near-optimal solution. In MDMA, tasks are modeled as magnetic domains migrating in a magnetic field, and the optimization problem is decomposed into subproblems, which are solved using Exact Potential Game Theory, convex optimization, and a hybrid genetic algorithm. Finally, simulation results validate the effectiveness of the LMREC framework, demonstrating its superiority over existing methods and its potential to enhance collaboration among end devices, RSUs, and LEO satellite networks.

随着物联网（IoT）设备的快速增长，移动边缘计算（MEC）在满足日益增长的计算需求方面面临挑战，特别是在资源受限的环境下。为了解决这一问题，我们提出了低轨道卫星- ms - rsu边缘计算（LMREC）框架，该框架将移动服务器（ms）、低地球轨道（LEO）卫星网络和路边单元（rsu）集成到一个创新的边缘计算架构中。我们首先引入“吸引力”和“排斥力”指标来模拟车载卫星服务器为特定用户服务的意愿。随后，我们设计了一个磁平衡算法（MEA），该算法通过平衡用户驱动的吸引力和服务器排斥来动态调整MS的部署和服务分配。为了解决任务调度的延迟敏感性和用户满意度问题，我们提出了一个混合整数非线性规划（MINLP）的任务卸载和资源分配优化问题。由于该优化问题难以在多项式时间内解决，我们提出了一种磁域迁移算法（MDMA）来获得近似最优解。在MDMA中，将任务建模为磁场中的磁域迁移，并将优化问题分解为子问题，利用精确势博弈论、凸优化和混合遗传算法求解子问题。最后，仿真结果验证了LMREC框架的有效性，表明其优于现有方法，并具有增强终端设备、rsu和LEO卫星网络之间协作的潜力。

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引用次数: 0

Dynamic offloading strategy in SAGIN-based emergency VEC: A multi-UAV clustering and collaborative computing approach 基于sagin的应急VEC动态卸载策略：一种多无人机聚类与协同计算方法

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-07-03 DOI: 10.1016/j.vehcom.2025.100952

Zhenzheng Shi, Liang Wang, Yaguang Lin, Anna Cai, Jiamin Fan, Cong Liu

Mobile edge computing (MEC) technology can provide stable and efficient computing services for ground vehicles and users. However, maintaining stable MEC services becomes challenging in scenarios where ground MEC servers are damaged or unavailable, such as in post-disaster or remote areas. To tackle this issue, this paper proposes a novel space-air-ground integrated network (SAGIN) based emergency vehicular edge computing (VEC) framework, leveraging the rapid deployment characteristic of unmanned aerial vehicle (UAV) to provide VEC services for ground vehicles. A distance-based UAV clustering (DUC) algorithm is designed for efficient multi-UAV collaboration, executed by low earth orbit (LEO) satellite with wide coverage. Within each cluster, a task splitting algorithm based on a novel expected computing delay (ECD) metric is performed by the cluster-head UAV (CHU). Focusing on the issue of limited line-of-sight (LoS) range of UAV and computing sustainability during vehicle moving, we propose a dynamic offloading strategy. Simulation results show that the proposed framework enhances UAV utilization by 60% and significantly reduces task process delays across varying scenarios.

移动边缘计算（MEC）技术可以为地面车辆和用户提供稳定高效的计算服务。然而，在地面MEC服务器损坏或不可用的情况下，例如灾后或偏远地区，维持稳定的MEC服务变得具有挑战性。针对这一问题，本文提出了一种新的基于天空地一体化网络（SAGIN）的应急车辆边缘计算（VEC）框架，利用无人机（UAV）的快速部署特性，为地面车辆提供VEC服务。设计了一种基于距离的无人机聚类（DUC）算法，以实现覆盖范围广的近地轨道（LEO）卫星上的多无人机高效协同。在每个簇内，簇头无人机（CHU）基于一种新的期望计算延迟（ECD）度量执行任务分割算法。针对无人机有限视距和车辆移动可持续性计算问题，提出了一种动态卸载策略。仿真结果表明，该框架提高了60%的无人机利用率，显著降低了不同场景下的任务过程延迟。

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引用次数: 0

A comprehensive survey of deep reinforcement learning in UAV-assisted IoT data collection 深度强化学习在无人机辅助物联网数据采集中的综合研究

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-06-25 DOI: 10.1016/j.vehcom.2025.100949

Oluwatosin Ahmed Amodu , Huda Althumali , Zurina Mohd Hanapi , Chedia Jarray , Raja Azlina Raja Mahmood , Mohammed Sani Adam , Umar Ali Bukar , Nor Fadzilah Abdullah , Nguyen Cong Luong

Unmanned Aerial Vehicles (UAVs) play a critical role in data collection for a wide range of Internet of Things (IoT) applications across remote, urban, and marine environments. In large-scale deployments, UAVs often face complex decision-making challenges, for which Deep Reinforcement Learning (DRL) has emerged as a promising solution. This paper presents a comprehensive review of research on UAV-assisted IoT utilizing DRL, covering key research questions relating to DRL algorithm variants, deployment objectives, architectural features, integrated technologies, UAV roles, optimization constraints, energy management strategies, and performance metrics. Findings indicate that value-based and actor-critic algorithms are the most commonly employed, targeting objectives such as path planning, transmit power control, scheduling, velocity and altitude control, and charging optimization. Other architectural considerations include clustering, security, obstacle avoidance, buffered sensors, and multi-UAV coordination. Beyond data collection, UAVs are also used for tasks such as device selection, data aggregation, and sensor charging, with energy management primarily achieved through charging and energy harvesting techniques. Performance is typically assessed using metrics like energy efficiency, throughput, latency, packet loss, and Age of Information (AoI). The paper concludes by outlining several promising research directions and open challenges critical to the successful deployment of UAVs as aerial communication platforms, especially in IoT data collection. By organizing existing work across key themes and outlining promising future directions, this review offers a valuable reference for researchers and technology professionals alike.

无人机（uav）在远程、城市和海洋环境中广泛的物联网（IoT）应用的数据收集中发挥着关键作用。在大规模部署中，无人机经常面临复杂的决策挑战，深度强化学习（DRL）已成为一种有前途的解决方案。本文全面回顾了利用DRL的无人机辅助物联网应用，涵盖了关键研究问题、DRL算法变体、部署目标、架构特征、集成技术、无人机角色、优化约束、能源管理策略和性能指标。研究结果表明，基于价值的算法和行为者批评算法是最常用的，针对的目标包括路径规划、传输功率控制、调度、速度和高度控制以及充电优化。架构考虑包括集群、安全性、避障、缓冲传感器和多无人机协调。除了数据收集，无人机还用于设备选择、数据聚合和传感器充电等任务，主要通过充电和能量收集技术实现能量管理。通常使用能效、吞吐量、延迟、数据包丢失和信息年龄（Age of Information, AoI）等指标来评估性能。最后，本文概述了几个有前途的研究方向和开放的挑战，这些挑战对无人机在物联网数据收集中的成功部署至关重要。

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引用次数: 0

DST-IDS: Dynamic spatial-temporal graph-transformer network for in-vehicle network intrusion detection system 用于车载网络入侵检测系统的动态时空图变换网络

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-08-05 DOI: 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.

车联网（IoV）的发展极大地增加了连接，使车载网络（IVN）更容易受到入侵。此外，电子控制单元（ecu）在当前车辆中的使用率也有了显著的提高，使控制器局域网（CAN）成为汽车领域广泛使用的标准。但是，它缺乏认证的规定。攻击者利用这些弱点对基于can的IVN发起各种攻击。序列数据方法如循环神经网络（rnn）和长短期记忆（LSTM）已成为该领域的突出方法，对入侵检测系统（IDS）的发展做出了重大贡献。然而，这些方法在特征提取方面受到限制，因为它们仅仅依赖于先前交互的隐藏状态，可能会忽略关键特征。此外，捕获CAN消息的复杂时空动态仍然是一个重大挑战。针对这些挑战，我们提出了用于车载网络入侵检测系统的动态时空图变换网络，简称“DST-IDS”。它包括三个模块：将行CAN消息相关性转换为潜在图表示的图时空嵌入模块、时空学习模块和分类模块。第二个模块利用图形转换器网络来捕获和学习CAN消息之间的动态时空依赖关系。最后一个模块将学习到的特征分为正常信息和攻击信息。该模型在两个公开可用的数据集（CAR-Hacking和IVN-IDS）上进行了评估，分别获得了0.999999和0.9996的极高准确率分数。这些结果表明，该模型在检测精度和误报率方面明显优于当前的车载网络入侵检测方法。

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引用次数: 0

Exploring the use of mobile IRS in a vehicular context 探索在车辆环境中使用移动IRS

IF 6.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-08-12 DOI: 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.

智能反射面（IRSs）是一种节能技术，用作“被动”中继以增加覆盖范围。它们通常是固定的，可以连接到基站难以访问的位置或失明的用户。大多数研究提出固定IRS定位，缺点是只覆盖固定区域。因此，在车辆环境中，将irs放置在交通之后的可行性将是一件有趣的事情。因此，我们建议首先通过使用优化器，然后依靠启发式来研究irs在车辆环境中的移动放置。在第一部分中，我们比较了固定和移动IRS定位。然后，在启发式部分，我们提出了不同的IRS选举策略，并对其进行了比较。第一部分比较了固定和移动放置的性能，第二部分比较了一跳和两跳之间的性能，同时分析了不同参数对这些结果的影响。我们还评估了轨迹预测器和数据集对这些结果的影响。

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引用次数: 0

Energy and experimental trust-based task offloading in the domain of connected autonomous vehicles 互联自动驾驶汽车领域中基于能量和实验信任的任务卸载

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-07-04 DOI: 10.1016/j.vehcom.2025.100954

Sachin Kumar Gupta , Anuradha Banerjee

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.

最近，自动驾驶汽车（cav）之间的任务卸载备受关注。目前在这方面的文献大多只优化了能量和潜伏期的标准。此外，连通性和自发的无私态度等问题仍未得到探索，尽管它们对节省车辆能源和减少完成任务的总体延迟具有重要意义和可能的贡献。因此，本研究的关键目标是剩余能量和成功卸载百分比的最大化，以及能量消耗和延迟的最小化。我们还考虑了信任，它有两个组成部分；效率和确定性。效率的定义是根据以前的会话历史完成当前任务所需的估计时间的倒数。从与卸载器的通信历史可以看出，确定性与服务器与任务卸载器之间连接的稳定性以及服务器的无私合作有关。实验结果表明，基于能量和经验信任（OTEET）的任务卸载方法提高了任务卸载成功率，降低了约40%的成本，是一种显著的改进。

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引用次数: 0

MetaCAN: An optimized adaptive hybrid metaheuristic-based intrusion detection system for CAN bus security MetaCAN：一种基于优化自适应混合元启发式的CAN总线安全入侵检测系统

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-07-10 DOI: 10.1016/j.vehcom.2025.100956

Kadir Ileri , Abdur Rakib , Soufiene Djahel

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 & 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.

控制器局域网（CAN）总线是一种基于消息的协议，广泛应用于现代车辆中，用于促进各种电子控制单元（ecu）之间的通信。然而，其过于简单的设计缺乏基本的安全措施，使其极易受到网络攻击。这些漏洞对车辆安全构成了重大风险，强调了实施有效入侵检测系统（IDS）的迫切需要。因此，本文提出了一种基于机器学习的IDS，通过自适应混合元启发式方法进行优化，称为MetaCAN，以保护CAN总线。MetaCAN利用粒子群优化（PSO）的快速收敛和布谷鸟搜索（CS）的鲁棒全局搜索的互补优势，确保有效的超参数调谐和模型优化。MetaCAN使用三个真实世界的数据集进行评估，包括生存分析，汽车黑客攻击；《国防挑战2020》和OTIDS。与传统的二进制检测系统不同，MetaCAN通过识别五种不同的攻击类型提供多类攻击检测，包括拒绝服务（DoS）、模糊攻击、伪装攻击、故障攻击和重放攻击。此外，通过引入时间间隔和ID重复计数两个有效特征的特征工程过程，提高了系统的检测精度。实验结果表明，MetaCAN始终优于针对相同数据集的现有IDS解决方案，使其成为在实际车辆环境中保护CAN总线的有前途的解决方案。

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引用次数: 0

Fault tolerant mission management for UAV under random threat using Markov decision process 基于马尔可夫决策过程的无人机随机威胁容错任务管理

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-07-22 DOI: 10.1016/j.vehcom.2025.100959

Md Muzakkir Quamar , Ali Nasir , Sami ELFerik

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.

本文介绍了一种用于无人机在威胁易发区域执行任务的综合框架。利用马尔可夫决策过程（MDP），该模型通过结合无人机状态、故障后能力和威胁导航策略，确保任务完成和最佳资源利用。该框架解决了传感器、执行器和视觉相机故障，即使在恶劣条件下也能实现可靠的操作。该模型的一个关键特征是集成无人机电池水平，以评估相对于任务目标的作战范围，在节约能源的同时优化任务分配。此外，自适应导航模式的结合增强了无人机的敏捷性和鲁棒性，通过在操作过程中实现威胁规避，密切模拟现实世界的场景。通过综合修复协议、充电策略和反复出现的目标和威胁的随机建模，该框架提供了一个整体解决方案，以提高敌对环境下的恢复能力和任务成功率。随机动态规划保证了任务执行过程中预先计算的最优策略的快速应用。基于仿真的案例研究证明了该框架在导航威胁、减轻故障和确保能源受限场景下任务可靠性方面的有效性。

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引用次数: 0

Securing the unforeseen: Enhancing VANET security with dynamic honeypots and attack rate analysis 确保不可预见的安全：用动态蜜罐和攻击率分析增强VANET安全性

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-06-20 DOI: 10.1016/j.vehcom.2025.100946

Mohammed A. Abdelmaguid, Hossam S. Hassanein, Mohammad Zulkernine

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.

解决已知威胁构成了网络安全防御的基础层。然而，真正的挑战出现在预测和减轻不可预见的攻击。当前的安全方法可以很好地应对熟悉的威胁，但往往难以应对新的或不可预见的攻击。本文研究了信任管理系统（TMS）中的信任起源，将其与网络攻击率联系起来，从而改进信任评估和预测新的攻击。我们将机器学习（ML）算法与蜜罐相结合，为车载自组织网络（vanet）提供全面的防御，擅长通过攻击率分析检测预期和意外攻击。我们的方法通过检查其识别已知攻击的能力来评估网络的安全状态，称为准备攻击。随后，这些信息将作为预测未来仍需要识别的攻击的基础，称为未准备的攻击。通过广泛的测试，我们证明了双重策略的可行性，该策略包括检测准备好的攻击和预测未准备的攻击。实验结果显示，在预测未准备的攻击方面有显著的改进，证明了提高的准确性、精确度和召回率。此外，我们进行实验，以确定蜜罐的最佳部署，以获得最大的效率。

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引用次数: 0

A secure and efficient lattice-based conditional privacy-preserving authentication protocol for the VANET 一种安全高效的基于格的VANET条件隐私保护认证协议

IF 5.8 2区计算机科学 Q1 TELECOMMUNICATIONS

Vehicular Communications

Pub Date : 2025-10-01 Epub Date: 2025-07-22 DOI: 10.1016/j.vehcom.2025.100958

Dongxian Shi , Xuwen Nie , Ming Xu , Hongbing Cheng , Muhammad Alam

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.

随着通信技术和自动驾驶技术的蓬勃发展，车辆自组织网络（VANET）变得越来越普遍。在VANETs中传输的信息暴露在开放的无线通信环境中，因此容易受到多种类型的攻击。为了平衡vanet中的信息安全和隐私，人们提出了大量的条件隐私保护认证协议。然而，它们中只有少数能够抵抗量子攻击，而且这些现有的抗量子工作并不令人满意，要么是不安全的，要么是存在其他问题。本文提出了一种安全高效的基于格的vanet条件隐私保护认证协议，实现了认证和隐私保护，并提供了批验证方法进一步优化性能。与现有协议相比，我们的协议安全、高效、通信开销最小。我们提供了几个参数集，在其中一些参数集下，协议的执行时间最短。基于非齐次小整数解问题的假设，给出了该协议在随机oracle模型下的安全性证明。

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引用次数: 0