Network attacks on the Internet of Vehicles (IoV) can lead to catastrophic consequences such as traffic congestion, incorrect routing, and even accidents. Existing rule-based countermeasures are effective only in specific scenarios, while machine learning-based methods suffer from suboptimal performance due to data quality issues. Furthermore, the isolation between in-vehicle networks (IVN) and external vehicle networks (EVN) prevents independent intrusion detection systems (IDS) from detecting continuous attack behaviors. To address these issues, we propose a mechanism that characterizes attack behaviors solely based on network connection information. This mechanism downplays the specifics of the traffic itself, allowing for the integration of IVN and EVN through structure, thus breaking the internal-external boundary and providing relatively stable logical structure information. This approach also offers opportunities to address the scarcity of labeled data. Based on this characterization mechanism, we develop an intrusion detection system – One$^{2}$, utilizing a multi-attribute heterogeneous graph transformer to achieve accurate multi-classification of various types of attacks. To assess the compatibility of the proposed IDS for IVN and EVN, extensive experiments were conducted using six real-world datasets that accurately depict IVN and EVN. The results show that One$^{2}$improves average accuracy by 7.14% and F1 score by 6.97% compared to state-of-the-art methods. The source code of this work is available at:https://github.com/LouHerGetUp/One2

Digital Twin (DT) technology has gained significant attention for simulating and optimizing urban traffic systems, especially in intersection vehicle trajectory prediction. However, digital twin traffic system faces significant challenges due to privacy and security regulations that prevent the centralized storage of trajectory and semantic data, which are essential for training accurate predictive models using sensitive traffic information. To address these issues, we introduce the integration of federated learning spatio-temporal-semantic attention-based trajectory (FedSTAST) model into the DT framework for vehicle trajectory prediction. In our FedSTAST, edge servers in physical space utilize local sensor data to perform computations and train models without transmitting raw data, only the model parameters are sent to a cloud server in the twin space for aggregation. This decentralized approach ensures data privacy while enabling collaborative model training. The simulation results demonstrate that the FedSTAST model effectively handles co-training and multi-source semantic input processing within spatio-temporal-semantic attention-based trajectory (STSAT) models, enhancing trajectory prediction accuracy and robustness in the dynamic, real-time context of DT-based urban traffic systems.

V2X technology facilitates real-time communication between vehicles, enabling collision avoidance systems, proactive hazard warnings, and cooperative maneuvers to prevent potential accidents. Due to the inherent openness of wireless communication channels, vehicular networks are highly susceptible to various security threats. Digital signatures have been widely adopted as an effective verification mechanism to ensure message integrity and authenticity. However, in high-density traffic environments, the sheer volume of messages imposes a significant computational burden on the verification process, leading to excessive delays and potential packet loss which compromises the timeliness and reliability of safety-critical applications. To address this issue, we propose a priority-aware signature verification scheme DPBV that dynamically prioritizes V2X messages based on their urgency and relevance. By leveraging clustering-based classification and batch verification techniques, the proposed approach optimizes the processing efficiency of safety messages while maintaining stringent security guarantees. Simulation results demonstrate that our scheme significantly reduces verification latency and improves message authentication throughput, making it well-suited for real-time V2X communication in high-density vehicular networks.

With the accelerated growth of the Internet of Vehicles (IoV), secure and efficient task offloading of vehicle has emerged as a critical challenge, particularly in highway scenarios. Traditional mobile edge computing (MEC) solutions face significant limitations in these environments due to frequent link disruptions and the dynamic nature of vehicle movements. Platoon offloading is considered a feasible solution, to address these challenges, we propose a novel dynamic pricing-based task offloading and resource allocation scheme specifically tailored for vehicle platoons, integrating robust data security measures. Our scheme employs a Stackelberg game framework to model the interaction between task vehicles and platoon members (PMs), ensuring fair compensation for resource allocation while maintaining low latency. We introduce a personalized security layer utilizing advanced encryption standard (AES) encryption to safeguard platoon communications, a critical enhancement given the vulnerability of wireless channels. Our scheme not only proves the existence of a unique Nash equilibrium but also optimizes the utility for both task vehicles and PMs through a dynamic pricing-based Stackelberg game (DPSG) algorithm. Simulation results demonstrate that DPSG can substantially improve entire performance compared to other schemes, such as local execution, MEC offloading scheme, Hooke-Jeeves-based Stackelberg game algorithm, and reinforcement learning-based offloading optimal scheme.

As an emerging urban computing paradigm, vehicle crowd sensing (VCS) leverages ubiquitous vehicles as basic sensing units to achieve more efficient data collection. However, with the expansion of the sensing range, the tens of thousands of vehicles and the openness of urban road networks pose a huge challenge for real-time participant recruitment in online VCS systems. To achieve efficient city-scale VCS, this paper proposes Dec-Recruiter, a decentralized framework for online participant recruitment. Specifically, Dec-Recruiter adopts a novel decision-making mode based on virtual grid agents, where vehicles traveling in the same direction within the same grid are considered homogeneous, simplifying the recruitment of specific vehicles to the selection of the number of vehicles in each direction. Meanwhile, through policy sharing among grid agents with the same geographic features, the complexity of city-scale VCS participant recruitment is further reduced. The core of Dec-Recruiter is a multi-agent contextual double-deep Q-network algorithm, which enables grid agents with different geographic features to collaborate on network-wide sensing tasks through their asynchronous decision-making. In this process, the Gaussian function is employed to adjust the reward distribution to address cold-start and data integrity issues in VCS. In addition, to ensure the convergence and training efficiency of the model on large-scale road networks, a pre-training-based transfer learning paradigm is also introduced. We conduct extensive experiments on both synthetic and real-world datasets. The results demonstrate that Dec-Recruiter can effectively recruit appropriate participants in the large-scale VCS and outperforms all baselines.

Vehicular crowdsensing (VCS) has aroused extensive attention because of its ability to provide comprehensive data services for intelligent transportation systems. Wherein, secure data transmission is a prerequisite for realizing the above benefits of VCS. Unfortunately, although many works on secure data sharing have been proposed, these schemes suffer from practical weaknesses such as data source authentication, malicious identity traceability, and inefficiency. In this paper, we propose a practical conditional anonymization and leakage-resilient authentication solution for vehicular crowdsensing communication (CALRA). Our proposal not only resists the leakage of sensitive information about vehicles but also realizes the authentication of data senders, while guaranteeing the integrity, authenticity, and confidentiality of data. Besides, CALRA exploits traceability technology to pursue malicious/illegal participants, thus avoiding participants’ accountability evasion caused by absolute anonymity. Furthermore, our CALRA solution delegates complex computational processes into an offline formulation to reduce computational and communication overheads. Finally, our scheme is proved to be secure and unforgeable through the random oracle model, and the performance evaluation illustrates that our CALRA proposal is superior and practical.

Recently, 3D object detection techniques based on the fusion of camera and LiDAR sensor modalities have received much attention due to their complementary capabilities. How-ever, prevalent multi-modal models are relatively homogeneous in terms of feature fusion strategies, making their performance being strictly limited to the detection results of one of the modalities. While the latest data-level fusion models based on virtual point clouds do not make further use of image features, resulting in a large amount of noise in depth estimation. To address the above issues, this paper integrates the advantages of data-level and feature-level sensor fusion, and proposes MLF3D, a 3D object detection based on multi-level fusion. MLF3D generates virtual point clouds to realize the data-level fusion, and implements feature-level fusion through two key designs: VIConv3D and ASFA. VIConv3D reduces the noise problem and realizes deep interactive enhancement of features through cross-modal fusion, noise sensing, and cross-space fusion. ASFA refines the bounding box by adaptively fusing cross-layer spatial semantic information. Our MLF3D achieves 92.91%, 87.71% AP and 85.25% AP in easy, medium and hard scenarios on the KITTI’s 3D Car Detection Leaderboard, realizing excellent performance.

As increasingly prevalent technologies in autonomous driving, 5G and the Internet of Things (IoT), Internet of autonomous vehicle (IoAV) technology is recognized as a technique that is capable of disruptively changing the way people travel and greatly improving the travel experience. In the IoAV scenarios, information dissemination is inseparable from the interaction between autonomous vehicles and smart infrastructure. However, existing efforts rarely focus on the secrecy, authenticity of interactive data and flexible one-to-many communication between autonomous vehicles. In this paper, we propose a tamper-resistant broadcasting (TRBS) scheme for secure communication, which handles the inefficiencies and insecurity of existing identity-based broadcast signcryption solutions. Not only can our TRBS protect communication data from being illegally accessed, forged, or tampered with by malicious vehicles, but it can also enable efficient and flexible secure information dissemination between autonomous vehicles. We also exhibit strict security proofs and experimental evaluations to demonstrate our TRBS is secure and efficient for real-world applications.

The secure transmission of instructions among vehicles in a platoon is one of the most essential needs for a vehicle platoon. Despite the existence of cryptographic methods to securely share instructions, instruction sharing is still subject to forgery, tampering, and denial-of-service attacks. Therefore, it is urgent to find a solution to perform data source identification to filter out irrelevant information (not instructions) while ensuring the authenticity of encrypted instructions is urgent to address. In addition, immediate revocation of credentials is also a crucial requirement for a vehicle platoon when an authorized vehicle member misbehaves. In this paper, we propose the first Secure Source Identification Scheme for Revocable Instruction Sharing (SI-RIS) to securely simultaneously achieve bilateral fine-grained access control, data source identification, immediate vehicle user revocation, and efficient encryption in vehicle platoons. Specifically, our SI-RIS solution supports fine-grained access control for both the sender and receiver over the encrypted instructions. As a result, only authorized correspondents are able to access the commands. Furthermore, upon identification of malicious members in the platoon, our SI-RIS provides an efficient direct vehicle user revocation mechanism capable of immediate revocation credentials without affecting other vehicles. We prove the security of our SI-RIS via rigorous mathematical security proof. Moreover, performance evaluation and comparisons illustrate the feasibility and practicability of SI-RIS for vehicle platoon.

As an emerging field of communication, Vehicular Social Networks (VSNs) can reduce traffic congestion while enhancing road safety by sharing data among groups of commuters. In VSNs, Vehicular Cloud Server (VCS) based data sharing technology with encrypted primitives allows local users to outsource encrypted data for reducing the storage burden on the user side and sharing data without location restrictions. However, existing data encryption solutions that have been applied in VSNs environments still encounter weaknesses in efficiency, security, or privacy due to the following problems: (1) lack of effective access policies for flexible authorizing ciphertext to multiple data users; (2) data breaches caused by malicious data publishers; (3) necessity in hiding the private information of receivers. To date, no such solution has been available that securely enables one-to-many user authorization with privacy protection, while greatly resisting malicious data publishers. We propose a Sanitizable Access Control System with Policy-protection (SASP) for VSNs in this paper. Our SASP enables a sanitizer to test and sanitize encrypted data to defend against malicious data publishers, ensuring that the plaintext can only be recovered if an authorized user has a valid key. Furthermore, in our SASP system, the access policy is separated into attribute names and attribute values. Wherein, the attribute values contain a lot of private information, which is hidden in the ciphertext to guarantee data users’ privacy. Rigorous security analysis and performance evaluations demonstrate the practicality of SASP for VSNs.

Autonomous Vehicle Platooning (AVP) presents a promising approach to reducing energy expenses, improving traffic safety, and enhancing capacity. In multi-platoon vehicular networks, platoons can collaborate to ensure better data transfer reliability. However, the existing secure information-sharing methods between platoons face efficiency and security challenges. These challenges arise from (1) a lack of an efficient method to simultaneously ensure confidentiality, authenticity, and non-repudiation of the transmitted information; (2) difficulty in cross-domain dynamically sharing ciphertexts to multiple recipients is challenging. To overcome these challenges, we propose a Flexible Cross-domain Data Access Control (FC-DAC) system in this paper. Our FC-DAC system ensures confidentiality, authenticity, and non-repudiation of transmitted information while enabling high-efficiency ciphertext sharing between various platoons. Additionally, the FC-DAC solution allows vehicles with valid authorizations to access signcrypted information quickly and efficiently, even if they belong to different platoons. Our theoretical and experimental analysis demonstrates that the FC-DAC scheme is resistant to various common attacks and is applicable to multi-platoon vehicular networks.

Vehicular platooning is emerging as a promising method that can enhance road utilization, alleviate traffic congestion, and even decrease energy expenditure by shortening the distance between vehicles in the platoon. In platooning, a platoon leader (PL) is required to communicate with platoon members (PMs) to issue instructions. In this way, the PMs are simply expected to follow the command and enjoy their free time. However, when the same instruction is sent to different PMs in the form of single-hop unicast, the ciphertext will go up as the quantity of PMs in the platoon resulting in a dramatic increase in transmission time. Moreover, transmission instructions without security and authentication guarantees are easily intercepted, forged, or deleted. Therefore, in this article, we propose broadcast signcryption scheme for platoon communication (BSPC), a broadcast signcryption scheme for platoon communication based on identity. In our BSPC, with only one signcryption, the PL can generate a public verifiable ciphertext with fixed-length for PMs, while employing broadcast functionality to deliver the ciphertext to multiple PMs at once. In such a manner, the confidentiality, integrity, and authentication of transmitted commands are ensured, such that malicious vehicles cannot manipulate data and unauthorized vehicles have no way to access it. Besides, the instructions' transmit time is greatly reduced. Our BSPC proposal is proved to be secure and unforgeable through rigorous analysis. Furthermore, simulation results demonstrate our BSPC is practical and efficient for platoon communication.

Existing conditional anonymous authentication protocols to secure the group communication in VANETs (Vehicular Ad hoc Networks) render challenges such as dynamically updating membership in a domain and achieving vehicle user’s privacy preservation. This article elegantly addresses these challenges by proposing a novel conditional privacy-preserving authentication with dynamic membership for VANETs depending on chinese remainder theorem (CRT). Specifically, the CRT is utilized by a trusted authority to securely disseminate a domain key for the authorized vehicles in the same domain, where each vehicle in this domain is able to obtain the domain key by only performing one modulo division operation in case of domain key updating. Distinct from the previous works in this field, our proposed protocol not only achieves message authentication, anonymity and conditional privacy-preserving, but also provides forward security and backward security of vehicles. Theoretical analysis and experiment simulation demonstrate that the proposed protocol is provably secure and highly feasible.

Autonomous Vehicle Platoon (AVP) is conceived as a promising solution to enhance the traffic capacity and reduce the energy consumption in the intelligent transportation system. Nevertheless, AVP without security guarantees are prone to various attacks, which probably lead to life-threatening accidents. Motivated by solving this issue, an outsourced attribute-based access control mechanism with direct revocation for AVP (RFAP) is introduced in this paper. Among them, attribute-based encryption is utilized to implement fine-grained access control during the encryption process. Furthermore, RFAP can not only realize the immediate revocation of platoon member who is about to leave the platoon without affecting others, but also achieve secure outsourced decryption with the help of edge computing units for minimizing the computational overhead of decryption on the vehicle side. Security analysis and simulation results indicate that our RFAP mechanism is practical in aspects of security and efficiency.

The existing signcryption schemes with equality testing are aimed at a sole cryptosystem and not suitable for the sophisticated heterogeneous network of Industrial Internet of Things (IIoT). To deal with this challenge, we propose a heterogeneous signcryption scheme with equality test (HSC-ET) in this article. This scheme enables a sensor in public key infrastructure (PKI) to execute data encryption and deliver it to the semitrusted entity (cloud server). When a user in an identity-based cryptosystem (IBC) intends to search for some data stored on the cloud server. The delegated cloud server executes tests on ciphertexts for determining whether the same underlying plaintext exists between two ciphertexts. These two ciphertexts can be one signcrypted ciphertext and one encrypted ciphertext, or both encrypted/signcrypted ciphertext, thus achieving a flexible search to the ciphertext. HSC-ET is demonstrated to be secure by the rigorous and detailed analysis. The experimental simulation and analysis results show the efficiency of our scheme.