Vehicular Communications最新文献

Quantum-proof authentication is essential for vehicular communications as the threat of quantum computing attacks on traditional encryption methods grows. Several lattice-based authentication protocols have recently been developed to help with this issue, but they come with hefty storage and communication overheads. Most of them also fail to provide strong anonymity for vehicles and edge nodes and can not support authentication for vehicles from multiple domains. This study presents a new lattice-based authentication protocol for vehicular communications that addresses limitations of previous methods and offers advanced security features such as anonymity, and unlinkability. The protocol utilizes a distributed ledger to store public keys, making the system more secure, tamper-proof, and efficient for key revocation in large networks. Additionally, it allows for a multi-domain authentication system for vehicular communication with improved security and flexibility. The proposed protocol's security is evaluated both formally and informally to demonstrate its resistance against the well-known attacks. Additionally, the performance analysis indicates that the proposed protocol surpasses current protocols and is suitable for vehicular communications.

Device-to-Device (D2D) communication when used in conjugation with Unmanned Aerial Vehicle (UAV) Femtocell and unlicensed spectrum can effectively tackle the ever-increasing mobile data demands. However, D2D communication raises security and privacy concerns among users due to the absence of a centralized entity such as a base station. Therefore, exploring social connections among users becomes imperative to enable secure and trustworthy D2D communication. This paper seeks to improve the performance of a social-assisted UAV cellular network augmented by D2D communication by proposing a novel subchannel assignment technique employing hypergraph coloring. Considering the real-world scenario, we incorporate the user/UAV mobility by using dynamic hypergraph coloring for subchannel assignment instead of the static one. Our proposed technique shifts a set of cellular users from the licensed to the unlicensed band based on their social connection with other co-channel D2D users. Additionally, we assign subchannels to different users to optimize the throughput while minimizing overall interference. Our proposed technique demonstrates significant improvements in system throughput, energy efficiency, and interference efficiency compared to conventional techniques. For our proposed technique, we observed improvements of 69%, 42%, and 15% in the per user system throughput when compared with the conventional techniques (graph, hypergraph, and dynamic-hypergraph, respectively). Moreover, our technique achieves higher per user energy efficiency by 120%, 70%, and 25%, and higher per user interference efficiency by 92%, 43%, and 22%, respectively, compared to graph, hypergraph, and dynamic-hypergraph techniques.

To secure Vehicle-to-everything (V2X) communications, many Conditional Privacy-Preserving Authentication schemes (CPPA) use symmetric and asymmetric encryption during the authentication process. However, several existing schemes have some security limitations regarding VANET requirements. In many symmetric cryptography-based schemes, the participants are required to share the same keys which could compromise the security of the network in case the key of one participant is compromised, while many asymmetric cryptography-based schemes take much time during the authentication process, and don't address the denial-of-service attack. In this paper, we propose a certificateless scheme that does not require a certificate and prevents the escrow problem. Plus, it uses the elliptic curve cryptography and avoids bilinear pairing and Map-to-Hash functions. We call our scheme Hybrid Cryptography-Based Scheme with a Conditional Privacy-Preserving Authentication (HCBS-CPPA), as it uses both symmetric and asymmetric cryptography during the authentication process. Our scheme combines the strength of an asymmetric encryption that satisfies non-repudiation, and the strength of a symmetric encryption that allows to perform a lightweight authentication. In addition, we show that our scheme is resilient to memory-based Denial of Service (DOS) attack which occurs when an attacker floods the memory of a receiver with invalid messages. A security proof shows that HCBS-CPPA is secure in the random oracle. Regarding the simulation of our scheme, it turns out that HCBS-CPPA has the best performance when compared with several existing certificateless schemes. Additionally, it requires less execution time during the signing and verification process, as well as less communication overhead when compared to the existing schemes.

The convergence of mobile edge computing (MEC) network with unmanned aerial vehicles (UAVs) presents an auspicious opportunity to revolutionize wireless communication and facilitate high-speed internet access in remote regions for mobile devices (MDs) as well as large scale artificial intelligence (AI) models. However, the substantial amount of data produced by the UAVs-assisted MEC network necessitates the integration of efficient distributed learning techniques in AI models. In recent times, distributed learning algorithms, including federated reinforcement learning (FRL) and split learning (SL), have been explored for the purpose of learning machine learning (ML) models that are distributed by sharing model parameters, as opposed to large raw data-sets as seen in traditional centralized learning algorithms. To implement the hybrid method, the model is first trained locally on each UAV-assisted MEC network using SL. Subsequently, the model parameters that have been encrypted are sent to a central server for federated averaging. Finally, after the model has been updated, it is distributed to each UAV-assisted MEC network for local fine-tuning. Our simulations indicate that the proposed split and federated reinforcement learning (SFRL) framework yields comparable high-test accuracy performance while consuming less energy compared to extant distributed learning algorithms. Furthermore, the SFRL algorithm efficiently realizes energy-efficient selection between the SL and FRL methods under different distributions. Numerical results shows that the proposed scheme improves the accuracy by 29.31% and reduced the energy consumption by around 67.34% and time delay by about 7.37%. as compared to the existing baseline schemes.

Vehicular networks are vulnerable to Distributed Denial of Service (DDoS), an extension of a Denial of Service (DoS) attack. The existing solutions for DDoS detection in vehicular networks use various Machine Learning (ML) algorithms. However, these algorithms are applicable only in a single layer in a vehicular network environment and are incapable of detecting DDoS dynamics for different layers of the network infrastructure. The recently reported attacks on transport networks reveal the fact that a research gap exists between the existing solutions and the multi-layer DDoS detection strategy requirements. Additionally, the majority of the current detection methods fail in the consideration of traffic heterogeneity and are not rate-adaptive, where both the mentioned parameters are important for an effective detection system.

In this paper, we introduce a comprehensive ML-based Network Intrusion Detection System (NIDS) against DDoS attacks in vehicular networks. Our proposed NIDS combines a three-tier security model, traffic adaptivity, and heterogeneity traffic provisions. We call our model Vehicular Adaptive Intrusion Detection And Novel System for Heterogeneous Hosts (VAIDANSHH). As mentioned earlier, VAIDANSHH has a three-tier security system: at RSU's hardware, communication channel, and RSU application level. VAIDANSHH combines the Adaptive Alarming Module (AAM) and the Detection Module (DM) for data generation, collection of generated data, flow monitoring, pre-processing, and classification. We use the NS3 simulation tool for our experiments to generate synthetic data and apply ML with WEKA. We run a thorough set of experiments, which show that VAIDANSHH detects UDP flooding, a form of DDoS attack, with 99.9% accuracy within a very short time. We compare VAIDANSHH with other state-of-the-art models; the comparative analysis shows that VAIDANSHH is superior in terms of accuracy and its multi-tier workflow.

Vehicle-to-vehicle (V2V) communication is one of the promising communication applications designed to optimize traffic conditions and has played a crucial role in the improvement of intelligent transportation technologies. Since there is still some uncertainty regarding generalized models that provide a more accurate representation of propagation environments, the existing literature emphasizes the need for additional experimental studies in various countries and propagation environments. This study aims to investigate the low-density and high-density characteristics of V2V channels for highway, suburban, and urban propagation environments in Türkiye. Thus, first, channel measurements were conducted for all propagation scenarios. Then, after the estimation of path loss parameters, the best-fitted path loss model was determined for each propagation scenario by comparing log-distance, two-ray, and log-ray models. It was observed that the log-ray model offered remarkably better performance than the two-ray model, especially in the majority of scenarios with two-ray characteristics. In addition, small-scale modeling and shadowing were also examined, and the outcomes were compared to relevant literature. Last, generalized path loss models were developed for six propagation scenarios and compared with previous studies. Providing additional experimental data on the impact of traffic and road environments that vary across countries on the V2V channel, this study not only validated and compared existing propagation models but also improved the representing accuracy and generalizability of the newly proposed propagation models. Here, all findings were presented in detail to support the motivation of the research.

The Internet of vehicles (IoV) is an essential part of modern intelligent transportation systems (ITS). In the ITS, intelligent connected vehicle can access a variety of latency-sensitive cloud services through the vulnerable wireless communication channel, which could lead to security and privacy issues. To prevent access by malicious nodes, a large number of authentication schemes have been proposed. However, with the diversification of cloud services and the rapid development of quantum computing, there are many drawbacks remain, including timeliness of authentication and resisting quantum computing. In light of this, we propose a lattice-based secure and efficient multi-cloud authentication and key agreement scheme for quantum key distribution (QKD) enabled IoV. Its features are as follows: i) Security-enhanced and Efficient Authentication: We combine the lattice-based lightweight signatures and quantum authentication keys to guarantee security-enhanced authentication. Meanwhile, we propose the quantum security service cloud (QSC) to manage the authentication of all vehicles and cloud server providers (CSPs) to reduce the authentication rounds and improve efficiency. ii) Extended Quantum Key Distribution (eQKD): In wireless networks, quantum key agreement is achieved through the pre-filled quantum keys. In wired networks, quantum key is accomplished by QKD with Bennett-Brassard 1984 (BB84) protocol. Furthermore, formal and informal security demonstrates that the scheme could resist potential security attacks. The performance comparison illustrates that our scheme could decrease the computational overhead by 27.23%-81.78% and authentication rounds by 81.34%-93.10%.

Since resource allocation of cellular networks is not dynamic, some cells may experience unplanned high traffic demands due to unexpected events. Unmanned aerial vehicles (UAV) can be used to provide the additional bandwidth required for data offloading.

Considering real-time and non-real-time traffic classes, our work is dedicated to optimize the placement of UAVs in cellular networks by two approaches. A first rule-based, low complexity method, that can be embedded in the UAV, while the other approach uses Reinforcement Learning (RL). It is based on Markov Decision Processes (MDP) for providing optimal results. The energy of the UAV battery and charging time constraints have been taken into account to cover a typical cellular environment consisting of many cells.

We used an open dataset for the Milan cellular network provided by Telecom Italia to evaluate the performance of both proposed models. Considering this dataset, the MDP model outperforms the rule-based algorithm. Nevertheless, the rule-based one requires less processing complexity and can be used immediately without any prior data. This work makes a notable contribution to developing practical and optimal solutions for UAV deployment in modern cellular networks.

The rapid growth of Vehicular Ad-hoc Networks (VANET), fueled by advancements in the Internet-of-Things, cloud computing, Intelligent Transportation Systems, and fog computing, has led to the introduction of fog node-based VANET to serve resource-constrained devices. In the traditional security models of VANET, due to the use of a centralized trusted authority, there is a chance of single-point-of-failure and service unavailable with the increased service access requests. Also, there was one-to-one communication between each roadside unit and trusted authority. This may increase the system complexity and increase the traffic load. To address these issues, a novel authentication protocol for fog-enabled VANET based on multiple trusted authority model is discussed which reduces the chance of service unavailability and single-point-of-failure as the entire traffic load is distributed among multiple sub-trusted authority. Due to the incorporation of fog node, a group of roadside units can be controlled centrally, where trusted authority does not need to perform individual authentication for each roadside unit. The proposed protocol's security is rigorously examined through both informal and formal security analysis. Additionally, the protocol exhibits enhanced security features, as demonstrated in a performance comparison section, showcasing its ability to meet the security and privacy requirements while incurring relatively low communication and computation and storage costs. Thus, the proposed protocol offers a secure and efficient authentication protocol for fog-enabled VANET.

In the rapidly advancing field of automotive cybersecurity, the protection of In-Vehicle Networks (IVNs) against cyber threats is crucial. Current deep learning solutions offer robustness but at the cost of high computational demand and potential privacy breaches due to the extensive IVN data required for model training. Our study proposes a novel intrusion detection system (IDS) specifically designed for IVNs that prioritizes computational efficiency and data privacy. Utilizing fuzzy hashing techniques, we generate context-aware embeddings that effectively preserve the privacy of IVN data. Among the machine learning algorithms evaluated, the Support Vector Machine (SVM) emerged as the most effective, particularly when paired with TLSH hash embeddings. This combination achieved notable detection performance, as substantiated by T-SNE visualizations that demonstrate a distinct segregation of normal and attack traffic within the vector space. To validate the effectiveness and practicality of our proposed IDS, we conducted exhaustive experiments on the well-known car-hacking dataset and the more complex ROAD dataset, which includes diverse and sophisticated attack scenarios. Our findings reveal that the proposed lightweight IDS not only demonstrates high detection accuracy but also maintains this performance within the computational constraints of current IVN systems. The system's capability to operate effectively in real-time environments makes it a viable solution for modern automotive cybersecurity needs.