Vehicular Ad Hoc Networks Environment Research Articles

Enhancing Black Hole Attack Detection in VANETs: A Hybrid Approach Integrating DBSCAN Clustering with Decision Trees

Ensuring the security of communication is crucial in Vehicular Ad Hoc Networks (VANETs) to protect the integrity of information sharing among cars. To implement VANET communication as an answer for the different uses, secure communication is necessary. The unreliability of VANET environments is caused by message delays or tampering in VANET applications. Finding the sweet spot between VANET security and performance and dependability is the primary goal. This project’s overarching goal is to fortify VANETs against Blackhole Routing Attacks and, by identifying and blocking harmful nodes, to mitigate the blackhole impact. This paper proposes a robust hybrid approach for the detection of black hole attacks in VANETs, leveraging the synergy between DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering and Decision Trees. DBSCAN, a density-based clustering algorithm, is employed to identify spatial clusters of vehicles, while Decision Trees are utilized to discern normal communication patterns from malicious ones within these clusters. The integration of these two techniques enhances the accuracy and efficiency of black hole attack detection in the dynamic and resource-constrained VANET environment. Experimental results demonstrate the effectiveness of the proposed hybrid approach, providing a promising solution for bolstering the security of VANETs against emerging threats. Here in result 73.89% improvement is received in Packet Drop Rate using DBSCAN, also minor improvement over Throughput and Average end to end delay and major improvement in terms of Network Routing Load.

Blockchain-based conditional privacy-preserving authentication scheme using PUF for vehicular ad hoc networks

Vehicular ad hoc networks (VANET) have been the key indispensable module of the future intelligent transportation system. Security and privacy are two essential attributes that protect the safe driving of vehicles. Over the last two decades, numerous conditional privacy-preserving authentication schemes have been presented for the VANET environment. However, existing schemes have various limitations, including security issues, high storage overhead, and frequent interactions. In order to bridge these difficulties, this work combines physically unclonable function and blockchain technology to construct a conditional privacy-preserving authentication scheme for the VANET environment. Specifically, we combine physical unclonable function and dynamic pseudonym techniques to generate unique pseudonym IDs dynamically and private keys using physical unclonable function to enhance privacy protection and resist physical attack. To reduce the number of communication rounds during the verification process, we deployed lightweight blockchain nodes to avoid direct communication between the receiver and the blockchain network. The proposed scheme demonstrates resilience against various potential attacks through comprehensive security analysis and proof. Furthermore, performance metrics indicate that our scheme outperforms similar schemes, making it suitable for resource-constrained VANET.

An efficient model of enhanced optimization and dilated-GRU based secured multi-access edge computing with blockchain for VANET sector

The establishment of Vehicular Ad Hoc Networks (VANETs) has brought significant advantages to humans, yet it also raises crucial safety considerations. Security is one of the major challenges in VANETs and is receiving significant attention. Hostile Onboard Units (OBUs) can use a variety of tactics including blocking, monitoring and tampering to harm neighboring OBUs to make illicit profits. The widespread use of the decentralized architecture built around blockchain technology has enabled the clear, safe, and dispersed storage and transmission of VANET application-related information without the need for an administrative center of authority. Perhaps the most important parts of the blockchain-driven VANET system involve implementing an efficient and adaptable consensus system, which remains an open academic issue. The objective of the suggested solution is to create a deep learning model for blockchain that will guarantee VANET security. This network topology is comprised of three layers: “perception, edge computing, and services”. The initial layer that comprises the blockchain operation is employed to demonstrate the privacy of VANET information. Additionally, cloud-based services as well as edge computing are used by the perception layer. The data is safeguarded by the service layer through the application of the blockchain system and storage in the cloud. The last layer aids in meeting user criteria for throughput and QoS. The main objective of this model is to evaluate the reliability of vehicle nodes maintained on the blockchain. Here, the node authentication is handled by an Adaptive Dilated Gated Recurrent Unit (AD-GRU), where the hyper-parameters of the recommended AD-GRU model are optimally tuned by an Enhanced Osprey Optimization Algorithm (EOOA). Finally, the proposed system is simulated and its extensive results are carried out. In contrast with other approaches, the novel system delivers the superior results of securing the data over the VANET environment.

A Secure Authentication Scheme with Local Differential Privacy in Edge Intelligence-Enabled VANET

Edge intelligence is a technology that integrates edge computing and artificial intelligence to achieve real-time and localized model generation. Thus, users can receive more precise and personalized services in vehicular ad hoc networks (VANETs) using edge intelligence. However, privacy and security challenges still exist, because sensitive data of the vehicle user is necessary for generating a high-accuracy AI model. In this paper, we propose an authentication scheme to preserve the privacy of user data in edge intelligence-enabled VANETs. The proposed scheme can establish a secure communication channel using fuzzy extractor, elliptic curve cryptography (ECC), and physical unclonable function (PUF) technology. The proposed data upload process can provide privacy of the data using local differential privacy and symmetric key encryption. We validate the security robustness of the proposed scheme using informal analysis, the Real-Or-Random (ROR) model, and the Scyther tool. Moreover, we evaluate the computation and communication efficiency of the proposed and related schemes using Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL) software development kit (SDK). We simulate the practical deployment of the proposed scheme using network simulator 3 (NS-3). Our results show that the proposed scheme has a performance improvement of 10∼48% compared to the state-of-the-art research. Thus, we can demonstrate that the proposed scheme provides comprehensive and secure communication for data management in edge intelligence-enabled VANET environments.

Main Primitive and Cryptography Tools for Authentication in VANET Environment: Literature Review

Vehicular ad hoc networks (VANETs) provide the potential to improve transportation efficiency by facilitating the sharing of traffic information among vehicles. Acceptance of VANET depends on communication speed and accuracy as well as privacy protection guaranteeing an individual's safety. Vehicle authentication is necessary to ensure message correctness. This necessitates the implementation of an effective privacy-preserving authentication scheme, as well as the need for both secrecy and timebound delivery of messages. Various privacy-preserving authentication schemes have been suggested to guarantee the integrity of messages in communications. However, most of the schemes are not able to solve issues related to computing costs, communication, security, privacy, threats, and vulnerabilities. In this review, we focus on cryptographic strategies that are suggested to accomplish privacy and authentication, such as identity-based, public key cryptography-based, pseudonym-based, and blockchain-based schemes. We provide a thorough analysis of schemes, including their categorizations, advantages, and drawbacks. The study demonstrates that the majority of current authentication techniques necessitate trusted authorities that lack transparency in their operations. Additionally, authentication process incurs substantial computational and communication overhead, leading to a considerable impact on the timely delivery of messages. More efforts are required to enhance the development of efficient authentication schemes in VANETs.

Design of CSKAS-VANET model for stable clustering and authentication scheme using RBMA and signcryption

A public key infrastructure-enabled system authentication model is developed to provide essential security functions for Vehicular ad hoc networks (VANETs). An intelligent transportation system is provided by VANET, an emerging technology. Dedicated short-range communication is used to disseminate messages wirelessly. Communications may be hacked, and messages can be stolen or fabricated. Hence, authenticated communication is crucial in the VANET environment. Some parameters such as trust, authentication, privacy, and security are at high risk. This article suggests a VANET with secure authentication and trust-based clustering mechanisms to provide stable and secure communication. Initially, the Restricted Boltzmann Machine learning algorithm (RBMA) is used to select the cluster head, which depends upon trust, vehicle lifetime, and buffer level. Then, cluster members are formed, followed by grouping. Diffie–Hellman Hyperelliptic Curve Cryptography and cryptographic hash functions are used by signcryption for secure communication in VANET. Therefore, the essential component of the key agreement strategy that will give superior authentication is this signcryption mechanism. Over the medium access protocol layer, all of these security characteristics are updated. The proposed method of clustering signcryption key agreement scheme (CSKAS) approach reduces time complexity and increases packet delivery ratio which is vital in providing stable, secure communication.

A quantum-secure certificateless aggregate signature protocol for vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) have revolutionized communication between vehicles and infrastructure, notably enhancing traffic management and passenger safety. However, VANETs are vulnerable to security threats, especially regarding data authenticity. Aggregate signature is a powerful technique that reduces computational and communication burdens by aggregating multiple signatures from different signers into a single signature. Traditional aggregate signature schemes, based on large prime number decomposition and the discrete logarithm problem, cannot effectively resist quantum attacks. This paper introduces a novel quantum secure certificateless aggregate signature (QSCLAS) scheme designed to enhance data security and privacy in VANETs. Our proposed scheme employs the number theory research unit (NTRU) algorithm. As a lattice-based cryptographic algorithm, NTRU is renowned for its security against quantum computer attacks, making it an essential component of our quantum-secure solution. By eliminating the need for expensive bilinear pairing operations, our proposed scheme achieves high efficiency and practicality in resource-limited VANETs environments. The security analysis demonstrates our scheme's resilience against both Type-I and Type-II adversaries in the random oracle model under the small integer solution (SIS) problem on the NTRU lattice. Furthermore, compared with existing approaches, the results illustrate that our proposed scheme offers significant advantages in signature generation and verification cost, as well as lower transmission overhead than other lattice-based schemes, thereby making it highly suitable for VANETs environments.

Application of Fuzzy Computing for VANET Routing

In the realm of vehicular ad hoc networks (VANETs), routing plays a pivotal role in ensuring efficient communication among vehicles and infrastructure. Traditional routing protocols often struggle to adapt to the dynamic and uncertain environment of VANETs, characterized by rapidly changing network topologies and unpredictable traffic conditions. In any case, the without a doubt solid and stunning nature of vehicular conditions presents superb difficulties for controlling shows, including unusual straightforwardness, changing association geologies, and dynamic traffic conditions [1, 2]. By incorporating fuzzy logic into routing algorithms, vehicles can make more informed decisions regarding route selection, considering factors such as traffic congestion, road conditions, and vehicle mobility patterns. This paper provides an overview of the key concepts of fuzzy computing and discusses its potential applications in VANET routing. Furthermore, it explores existing research efforts in this area, highlighting the benefits and challenges associated with fuzzy based routing approaches. Through a comprehensive review of the literature, this paper identifies current trends and future directions for leveraging fuzzy computing in VANET routing, aiming to improve the reliability, scalability, and efficiency of communication in vehicular networks.

Vehicle Identity Anonymity Framework with Accountability for VANET Environment

<p>In recent years, there has been rapid development in vehicle safety technology, with the emergence of various active safety systems including blind spot information systems, adaptive cruise control, and front collision warning systems. Simultaneously, car manufacturers and technology companies are actively exploring technologies in the realm of autonomous driving. To facilitate such applications, vehicles are required to communicate with each other, exchanging vital information such as position, speed, and acceleration. However, this exchange of information poses a potential risk of drivers’ personal data being compromised. For safety purposes, vehicles must undergo appropriate authentication before engaging in communication with other vehicles. Ensuring this authentication process maintains the anonymity of the vehicles is crucial. Yet, striking a balance between protecting vehicle anonymity and enabling vehicle identification when necessary remains a challenging issue. This paper introduces a multi-tier Vehicular Ad-Hoc Network (VANET) framework designed to uphold the conditional anonymity and traceability of vehicles. The implementation of a group signature mechanism facilitates anonymous authentication, thereby enabling the realization of conditional anonymity and traceability. Moreover, comprehensive simulations and security analyses were conducted to validate the effectiveness of the proposed framework, demonstrating its efficiency while incorporating robust safety considerations.</p> <p> </p>

Advancements and Challenges in IoT Simulators: A Comprehensive Review.

The Internet of Things (IoT) has emerged as an important concept, bridging the physical and digital worlds through interconnected devices. Although the idea of interconnected devices predates the term "Internet of Things", which was coined in 1999 by Kevin Ashton, the vision of a seamlessly integrated world of devices has been accelerated by advancements in wireless technologies, cost-effective computing, and the ubiquity of mobile devices. This study aims to provide an in-depth review of existing and emerging IoT simulators focusing on their capabilities and real-world applications, and discuss the current challenges and future trends in the IoT simulation area. Despite substantial research in the IoT simulation domain, many studies have a narrow focus, leaving a gap in comprehensive reviews that consider broader IoT development metrics, such as device mobility, energy models, Software-Defined Networking (SDN), and scalability. Notably, there is a lack of literature examining IoT simulators' capabilities in supporting renewable energy sources and their integration with Vehicular Ad-hoc Network (VANET) simulations. Our review seeks to address this gap, evaluating the ability of IoT simulators to simulate complex, large-scale IoT scenarios and meet specific developmental requirements, as well as examining the current challenges and future trends in the field of IoT simulation. Our systematic analysis has identified several significant gaps in the current literature. A primary concern is the lack of a generic simulator capable of effectively simulating various scenarios across different domains within the IoT environment. As a result, a comprehensive and versatile simulator is required to simulate the diverse scenarios occurring in IoT applications. Additionally, there is a notable gap in simulators that address specific security concerns, particularly battery depletion attacks, which are increasingly relevant in IoT systems. Furthermore, there is a need for further investigation and study regarding the integration of IoT simulators with traffic simulation for VANET environments. In addition, it is noteworthy that renewable energy sources are underrepresented in IoT simulations, despite an increasing global emphasis on environmental sustainability. As a result of these identified gaps, it is imperative to develop more advanced and adaptable IoT simulation tools that are designed to meet the multifaceted challenges and opportunities of the IoT domain.

Reliable data transmission for a VANET-IoIT architecture: A DNN approach

The challenges and resilience of vehicular ad hoc network (VANET) and deep neural network (DNN) hybrid architectures in terms of reliability in smart cities have attracted much global interest stemming from the rollout of the next generation of intelligent networks. In this paper, we propose a novel distributed DNN (D-DNN) scheme with blockchain to support the Internet of Intelligent Things (IoIT) infrastructure in the VANET environment of the future. In particular, because the communication links between edge nodes are very unstable in VANETs, a new neuro-fuzzy server that serves the dual roles of finding reliable links between edge nodes and performing optimal routing path selection is proposed. Next, a blockchain layer is employed at the edge nodes, which are initially scrutinized before establishing communication links to ensure reliability during data transfer. Then, the proposed D-DNN (PD-DNN) scheme is applied to enhance the performance of the VANET-IoIT architecture by improving the data flow and convergence rate and mitigating erratic variations in output. To address reliability concerns, the coverage probability (CP) metric is investigated as a measure of network connectivity. Furthermore, we present an analysis of the PD-DNN scheme in comparison with the traditional DNN (T-DNN) scheme. Finally, simulation results for VANET-IoIT scenarios show that, subject to data protection and privacy constraints, the CP values corresponding to different communication links are improved to a greater extent under our scheme than under the traditional scheme, demonstrating the feasibility of the proposed scheme.

Enhancing Privacy in the Internet of Vehicles via Hyperelliptic Curve Cryptography

The Internet of Things (IoT) is a technological paradigm that has gained significant momentum the last decade and, among other features, enables the development of intelligent and interoperable device networks. In this respect, it has triggered the creation and evolution of vehicular ad-hoc networks (VANETs), which are initially implemented in order to guarantee the safety of drivers and the avoidance of traffic accidents. The drawback is that this fast evolution comes with serious concerns in terms of the privacy of users, while the population of attackers or entities that try to eavesdrop and intercept information has significantly increased. This imposes a serious risk for drivers moving across a Smart City. The research presented in this paper aims to evaluate privacy protection mechanisms in VANET environments, based on the efficiency and security level they ensure, considering the fact that VANETs provide limited resources to users/drivers. Moreover, the usage of elliptic curve cryptography in reduced resources environments is discussed. Finally, this paper compares the performance of three cryptographic algorithms, elliptic curve cryptography (ECC), hyperelliptic curve cryptography genus 2 (HECC-2) and HECC genus 3 (HECC-3), employed for an efficient authentication and safe message transmission mechanism in VANETs, aimed at reaching conclusions related to the implementation of each cryptographic scheme in this specific application area. The evaluation results indicate that ECC supersedes HECC-2 and HECC-3 in most metrics. However, HECC-2 and HECC-3 demonstrate better responses than ECC does in selected energy metrics. Overall, it is observed that HECC algorithms are not yet mature enough to compete with ECC. This is due to the fact that the research community has not sufficiently progressed toward the optimization of HECC, and moreover, HECC builds on quite complex mathematics. There are indications, however, that once HECC curves are indeed optimized, HECC will outperform ECC in speed as well as in other metrics, sinceHECC-2 and HECC-3 use a significantly smaller key size with the same level of security as that of ECC.

Big data vehicle density management in vehicular ad-hoc network

<span>Smart city project is today a domain of interest to community research which play well-known role in road traffic management. Data exchange became complicated in terms of capacity in the intelligent transport system (ITS), and without the raise of big data, the treatment is very difficult to manage. vehicular ad-hoc network (VANETs) faces many challenges mainly the voluminous data generated by different actors of VANET environment. <br /> We propose a real time anomalies detection system in an instantaneous way with parallel data treatment. The system method intends to compute precisely vehicle density at each section on each road, which help to handle the traffic and forward to vehicles information about the road and the best safe path to reach their destination. Also, we build anomalies prediction system based on machine learning framework, it is a good solution for avoiding traffic congestion and limiting the risk of accidents. The simulation results demonstrate that the proposed system method reduces congestion greatly by taking into account the load balancing and therefore avoids saturation and reduces accidents. It should also be noted that the results obtained show that the system is characterized by low latency and high accuracy.</span>

An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs

Unexpected traffic incidents cause safety concerns and intense traffic congestion on crowded urban road networks. Vehicular ad-hoc network (VANET)-aided Intelligent Transport Systems (ITS) aim to mitigate these risks through timely dissemination of alert messages. However, conventional Radio frequency (RF) mobile ad-hoc routing protocols are ill-suited for dynamic VANET environments due to high mutual interference, packet collisions, high end-to-end delay from frequent route discoveries, and periodic beaconing requirements. Fortunately, the quickly emerging Visible Light Communications (VLC) provide complementary short-range connectivity with high bandwidth and low interference. This paper proposes an efficient adaptive routing protocol for emergency messages in dense VANET scenarios leveraging a hybrid RF/VLC system. When an incident or congestion happens, the source vehicle disseminates the information to the incoming vehicles as quickly as possible using a combination of VLC and RF communication networks. Multi-hop relays extend the connectivity if the direct links are blocked. The coverage area is partitioned into zones based on road segments, intersections, and traffic flows. The Road Side Units (RSU)s are intelligently assigned to zones and they analyze the historical traffic data to characterize each zone and decide a response strategy. We also propose a congestion detection scheme that utilizes traffic simulations to forecast the clearance times under different response strategies. The highest-scoring strategy is selected based on the predicted impacts on travel time, emissions, and driver stress levels. The proposed algorithm adaptively uses the selected strategy to proactively alleviate the predicted congestion through optimized routing and control. Overall, the protocol maximizes safety and efficiency during emergencies by leveraging the hybrid RF/VLC, incorporating real-time congestion forecasting and dynamic rerouting into the response strategies.

Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks.

With the rapid development of the intelligent transportation system (ITS), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. The high mobility of vehicles in urban streets poses serious challenges to routing protocols and has a significant impact on network performance. Existing topology-based routing is not suitable for highly dynamic VANETs, thereby making location-based routing protocols the preferred choice due to their scalability. However, the working environment of VANETs is complex and interference-prone. In wireless-network communication, the channel contention introduced by the high density of vehicles, coupled with urban structures, significantly increases the difficulty of designing high-quality communication protocols. In this context, compared to topology-based routing protocols, location-based geographic routing is widely employed in VANETs due to its avoidance of the route construction and maintenance phases. Considering the characteristics of VANETs, this paper proposes a novel environment-aware adaptive reinforcement routing (EARR) protocol aimed at establishing reliable connections between source and destination nodes. The protocol adopts periodic beacons to perceive and explore the surrounding environment, thereby constructing a local topology. By applying reinforcement learning to the vehicle network's route selection, it adaptively adjusts the Q table through the perception of multiple metrics from beacons, including vehicle speed, available bandwidth, signal-reception strength, etc., thereby assisting the selection of relay vehicles and alleviating the challenges posed by the high dynamics, shadow fading, and limited bandwidth in VANETs. The combination of reinforcement learning and beacons accelerates the establishment of end-to-end routes, thereby guiding each vehicle to choose the optimal next hop and forming suboptimal routes throughout the entire communication process. The adaptive adjustment feature of the protocol enables it to address sudden link interruptions, thereby enhancing communication reliability. In experiments, the EARR protocol demonstrates significant improvements across various performance metrics compared to existing routing protocols. Throughout the simulation process, the EARR protocol maintains a consistently high packet-delivery rate and throughput compared to other protocols, as well as demonstrates stable performance across various scenarios. Finally, the proposed protocol demonstrates relatively consistent standardized latency and low overhead in all experiments.

5G handover issues and techniques for vehicular communications

<span lang="EN-US">Vehicular communication is gaining popularity, and seamless handover is critical to maintaining a stable and uninterrupted network connection between vehicles and roadside units. This paper investigates the advancements in handover approaches in vehicular networks, with a specific focus on 5G technology. Vehicular Ad-hoc Networks (VANETs) face challenges due to high mobility, dynamic network topology, and frequent information exchange. The paper discusses handover issues in 5G VANET environments, such as too-late and too-early handovers, wrong handover decisions, and unnecessary handovers. It also explores key performance indicators (KPIs) used in handover evaluation. The advancements in handover approaches presented in this paper pave the way for enhanced connectivity and communication management in 5G VANETs, contributing to the development of safer and more efficient intelligent transportation systems.</span>

An anonymous authentication scheme with conditional privacy-preserving for Vehicular Ad hoc Networks based on zero-knowledge proof and Blockchain

Recently, Vehicular Ad hoc Networks (VANETs) have gained extensive attention in both academia and industry. VANETs play a seminal role in smart transportation by enhancing driving convenience and traffic efficiency through real-time information interaction. Despite this, ensuring secure authentication and privacy preservation remains two challenging issues in VANETs. The existence of security schemes that are vulnerable to privacy and security issues or have substantial computation and communication overheads shows the necessity for further research in this area. Therefore, in this article, we present an anonymous authentication scheme based on Zero-Knowledge Proof. To be more specific, we adopted Simulation Extractable Zero-Knowledge SNARKs (SE zk-SNARKs) to achieve anonymity and conditional privacy. Although zk-SNARK proofs are succinct and fast to verify, the time required for proof generation poses a major obstacle in using zk-SNARKs in a time-sensitive VANET environment. To overcome this drawback, we focus on key components of the proof statement, separating the repetitive calculations, and archiving in the Blockchain, which serves as an immutable data storage. By omitting repetitive calculations, our proposed scheme becomes lightweight enough to run efficiently on VANETs. Security analysis showed that the proposed protocol satisfied security and privacy requirements. Additionally, a computation cost analysis is provided to demonstrate the significant advantage of our proposed scheme. ProVerif is used to verify the strong secrecy of the protocol, and the results show that privacy can be guaranteed in the proposed scheme.

Ensemble Learning Based Malicious Node Detection in SDN-Based VANETs

Background: The architecture of Software Defined Networking (SDN) integrated with Vehicular Ad-hoc Networks (VANETs) is considered a practical method for handling large-scale, dynamic, heterogeneous vehicular networks, since it offers flexibility, programmability, scalability, and a global understanding. However, the integration with VANETs introduces additional security vulnerabilities due to the deployment of a logically centralized control mechanism. These security attacks are classified as internal and external based on the nature of the attacker. The method adopted in this work facilitated the detection of internal position falsification attacks. Objective: This study aimed to investigate the performance of k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest machine learning (ML) algorithms in detecting position falsification attacks using the Vehicular Reference Misbehavior (VeReMi) dataset. It also aimed to conduct a comparative analysis of two ensemble classification models, namely voting and stacking for final decision-making. These ensemble classification methods used the ML algorithms cooperatively to achieve improved classification. Methods: The simulations and evaluations were conducted using the Python programming language. VeReMi dataset was selected since it was an application-specific dataset for VANETs environment. Performance evaluation metrics, such as accuracy, precision, recall, F-measure, and prediction time were also used in the comparative studies. Results: This experimental study showed that Random Forest ML algorithm provided the best performance in detecting attacks among the ML algorithms. Voting and stacking were both used to enhance classification accuracy and reduce time required to identify an attack through predictions generated by k-NN, SVM, Naïve Bayes, Logistic Regression, and Random Forest classifiers. Conclusion: In terms of attack detection accuracy, both methods (voting and stacking) achieved the same level of accuracy as Random Forest. However, the detection of attack using stacking could be achieved in roughly less than half the time required by voting ensemble. Keywords: Machine learning methods, Majority voting ensemble, SDN-based VANETs, Security attacks, Stacking ensemble classifiers, VANETs,

An Efficient and Provably Secure Identity Authentication Scheme for VANET

In recent years, many researchers have applied aggregated signature techniques to resource-constrained vehicular Ad Hoc networks (VANET) authentication scenarios. We reviewed two recent VANET authentication schemes based on certificateless aggregated signatures (CLAS) while demonstrating that neither of them is resistant to public key replacement attacks under their security models. An eavesdropper can successfully forge a legitimate signature to perform malicious operation. To further address the above security flaws, we propose an improved CLAS scheme for VANET. Considering that some researchers have briefly or even incorrectly used forking lemmas in their security proofs to prove an insecure CLAS scheme, we further improve and refine the security model and security proof method for CLAS, which make the proof process transparent by using general forking lemma. Based on these improvements, our scheme can resist attacks from two types of adversaries in the CLAS security model. In the final performance analysis, the improved CLAS scheme outperforms the secure related scheme of recent years in terms of both computational and communication efficiency. Therefore, our proposal is more suitable for resource-constrained VANET environments.

Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks

In urban Vehicular Ad Hoc Network (VANET) environments, buildings play a crucial role as they can act as obstacles that attenuate the transmission signal between vehicles. Such obstacles lead to multipath effects, which could substantially impact data transmission due to fading. Therefore, quantifying the impact of buildings on transmission quality is a key parameter of the propagation model, especially in critical scenarios involving emergency vehicles where reliable communication is of utmost importance. In this research, we propose a supervised learning approach based on Artificial Neural Networks (ANNs) to develop a predictive model capable of estimating the level of signal degradation, represented by the Bit Error Rate (BER), based on the obstacles perceived by moving emergency vehicles. By establishing a relationship between the level of signal degradation and the encountered obstacles, our proposed mechanism enables efficient routing decisions being made prior to the transmission process. Consequently, data packets are routed through paths that exhibit the lowest BER. To collect the training data, we employed Network Simulator 3 (NS-3) in conjunction with the Simulation of Urban MObility (SUMO) simulator, leveraging real-world data sourced from the OpenStreetMap (OSM) geographic database. OSM enabled us to gather geospatial data related to the Two-Dimensional (2D) geometric structure of buildings, which served as input for our Artificial Neural Network (ANN). To determine the most suitable algorithm for our ANN, we assessed the accuracy of ten learning algorithms in MATLAB, utilizing five key metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Correlation Coefficient (R), and Maximum Prediction Error (MaxPE). For each algorithm, we conducted fifteen iterations based on ten hidden neurons and gauged its accuracy against the aforementioned metrics. Our analysis highlighted that the ANN underpinned by the Conjugate Gradient With Powell/Beale Restarts (CGB) learning algorithm exhibited superior performance in terms of MSE, RMSE, MAE, R, and MaxPE compared to other algorithms such as Levenberg–Marquardt (LM), Bayesian Regularization (BR), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Fletcher–Powell Conjugate Gradient (CGF), Polak–Ribiére Conjugate Gradient (CGP), One-Step Secant (OSS), and Variable Learning Rate Backpropagation (GDX). The BER prediction by our ANN incorporates the TWO-RAY Ground (TRG) propagation model, an adjustable parameter within NS-3. When subjected to 300 new samples, the trained ANN’s simulation outcomes illustrated its capability to learn, generalize, and successfully predict the BER for a new data instance. Overall, our research contributes to enhancing the performance and reliability of communication in urban VANET environments, especially in critical scenarios involving emergency vehicles, by leveraging supervised learning and artificial neural networks to predict signal degradation levels and optimize routing decisions accordingly.