Search Results (595)

The Internet of Vehicles (IoV) relies on Basic Safety Messages (BSMs) for cooperative awareness, yet these broadcasts remain vulnerable to falsification, replay, flooding, Sybil-based, and motion-manipulation attacks. This paper proposes a temporal-window-aware physics-informed edge-oriented Intrusion Detection System (IDS) for 20-class IoV misbehavior detection under two simulation-based BSM observability regimes: ideal noise-free kinematics and realistic noise-inclusive observables reconstructed using the sensor-error components supplied separately by VeReMi Extension. Accordingly, “realistic” denotes a noise-inclusive simulation condition rather than real-world validation. From VeReMi Extension streams, the framework derives a compact 20-feature representation capturing kinematics, timing, replay cues, pseudonym dynamics, position-consistency residuals, zero-pattern behavior, and long-horizon motion indicators. These features are normalized with a training-only robust scaler, organized into sender-specific temporal windows, and classified using a lightweight three-layer stacked Long Short-Term Memory (LSTM) with residual temporal pooling. Four implementation variants are evaluated: dense Keras, default-optimized TensorFlow Lite, pruning-only Keras, and pruning-plus-compression TensorFlow Lite. Temporal sensitivity identifies $T=40$ as the best robustness–latency compromise under the realistic noise-inclusive regime. At $T=40$, the final pruned-and-compressed TensorFlow Lite model achieves $99.60\%$ accuracy and $99.09\%$ macro-F1 under ideal observability, and $99.38\%$ accuracy and $98.67\%$ macro-F1 under realistic noise-inclusive observability, with an $88.38$ KB footprint and $0.1283$ ms controlled-runtime latency. Large-scale Central Processing Unit (CPU) benchmarks on $\mathrm{150,000}$ noise-inclusive test sequences provide a platform-dependent runtime reference, with the pruned TensorFlow Lite model reaching $99.14\%$ accuracy, $98.18\%$ macro-F1, and $3.544$ ms average latency on a multi-core Intel Xeon CPU. To complement this high-throughput evaluation, edge-deployment potential is profiled using the official C++ TensorFlow Lite benchmark tool. When evaluated using a single CPU thread without batching, the final artifact achieves an unbatched per-sequence latency of $1.356$ ms, corresponding to less than $1.4\%$ of the standard 100 ms BSM generation interval. An architecture-width ablation identifies the $64/32/32$ recurrent stack as the performance–resource knee point: expanding it to $128/64/64$ improves validation macro-F1 by only $0.0019$ percentage points while increasing TensorFlow Lite footprint and latency by factors of $2.46$ and $2.32$, respectively. A training-time architecture-preserving feature-family ablation confirms that engineered descriptors are essential: raw kinematics alone reduce noise-inclusive macro-F1 from $98.67\%$ to $67.49\%$, with pseudonym dynamics and position-consistency cues producing the largest individual degradations. Full article

Vehicular ad hoc networks (VANETs) have become a vital component of intelligent transport systems, with their security concerns increasingly drawing attention. To safeguard user privacy and ensure data authenticity and integrity, researchers have devised numerous certificateless conditional privacy-preserving authentication (CLCPPA) schemes. However, existing schemes generally suffer from insufficient security or high computational and communication overhead. Moreover, most implicitly assume the existence of a secure channel between vehicles and trusted entities during pseudonym generation and transmission, making it difficult to meet the real-time demands and practical deployment requirements of VANETs. To address these issues, this paper constructs a certificateless aggregated conditional privacy-preserving authentication (CL-ACPPA) scheme under elliptic curve cryptography that does not require bilinear operations. Formal security analysis demonstrates that, under the Random Oracle Model and the elliptic curve discrete logarithm problem assumption, the proposed scheme resists adaptive chosen-message attacks from adversaries with varying capabilities. Performance analysis and experimental results demonstrate that, compared with existing schemes, the proposed scheme achieves higher security while maintaining low communication and computational overhead. Full article

Most studies on vehicular cybersecurity focus on classification accuracy but fail to evaluate the applicability of the models to the edge. This paper proposes a secure edge intelligence framework for V2X/IoV intrusion and misbehavior detection, called TrustEdge-V2X, which is deployment-aware. The framework combines dataset-role qualification, attack taxonomy, AI model benchmarking, feature-budget analysis, deployment ranking based on EdgeScore, offline risk-aware orchestration, external validation, robustness testing, explainable AI, repeated-run statistical analysis and ablation studies. Three datasets are assigned different experimental roles: VeReMi_NextGen is used for core V2X/VANET misbehavior detection, CICIoT2023 is used for supporting edge/IoT intrusion experiments and HCRL_CarHacking is used for external IoV/CAN validation. LightGBM outperformed all other AI models in EdgeScore (0.9383), F1-score (0.9878), MCC (0.9758), and inference latency (0.009419 ms per sample) across all eight AI models and six scenarios on VeReMi_NextGen for binary detection. In five dataset-task cases, the accuracy-best model was different from the EdgeScore-best model, which is the most important point to note: the best model in terms of accuracy is not necessarily the best model in terms of EdgeScore. Compact feature subsets were competitive, and robustness testing demonstrated an average F1 decrease of 0.1423 when tested under stress. The orchestration layer was found to be beneficial for the tasks, but it did not always perform better than the best fixed policy. As a whole, TrustEdge-V2X offers a systematic approach to the assessment and selection of vehicular cybersecurity models based on the operational and deployment conditions, not only on the classification accuracy. Full article

UAV-assisted VANETs are a key component of the 6G vision, yet their practical deployment is hindered by the fundamental conflict between network Quality of Service (QoS) and the high aerodynamic power required for rotary-wing flight. This paper proposes SAVIOR (Survivable Aerial-Vehicular Intelligent Optimization and Routing), a Deep Reinforcement Learning (DRL) framework that jointly optimizes multi-UAV activation and packet routing. Unlike existing approaches that rely on oversimplified linear energy models, SAVIOR integrates a rigorous three-component aerodynamic power model and introduces an M/M/1 queuing-based Survivability Score (S-score) to explicitly quantify packet delivery before Time-to-Live (TTL) expiration. Through a high-fidelity co-simulation using SUMO and Python-TraCI, the SAVIOR agent is able to handle stress-test situations where network demand is higher than capacity ($\rho >1.0$). A comparative analysis shows that SAVIOR is Pareto-optimal, with a total reward that is 65% higher than that of a static energy-saving policy and a survivability that is 24% higher. Crucially, compared to a performance-maximizing greedy policy, SAVIOR maintains comparable safety-critical QoS while reducing total energy consumption by 19.8%, thereby preventing premature battery depletion and mitigating co-channel interference. Full article

Mobile Ad Hoc Networks (MANETs) or Wireless Sensor Networks (WSNs) have a “decentralized” architecture and are very susceptible to sophisticated attacks, such as identity forgery attacks using deep learning (DL) methods. Secure routing and intrusion detection systems have been developed but they are usually not scalable, consume too much power, introduce too much communication overhead and do not provide enough privacy protection, or are not resilient to changes in adversarial behavior. This paper introduces a Federated Pyramid Swin Vision Transformer (FPSViT) framework that enhances the routing optimization and energy-efficient communication for MANET–WSN networks with the support of Generative AI (GAI), addressing these challenges. The proposed framework incorporates three modules: federated averaging for privacy-preserving distributed learning, Pyramid Swin Vision Transformer (PSViT) for extracting Sybil attack characteristics at multiple scales, and a GAI-based adversarial pattern generation module to boost the robustness of the detection process in the presence of evolving attack patterns. Energy-aware routing optimization: It takes into account the energy level of the nodes, power consumption of the links, link reliability and trust values to optimize the routing for minimum power consumption with secure communication. Results of experimental evaluations on various Sybil attack scenarios show that the proposed FPSViT is able to achieve 98.84%, 98.52%, 98.21%, and 98.36% detection accuracy, precision, recall, and F1-score, respectively, and consume 0.381 J/node on average and increase the lifetime of the network to 2876 rounds. The power consumption analysis demonstrates that FPSViT consumes 12–21% less energy than other methods such as Federated CNN, FL-LSTM, Lightweight Standalone Swin Detector, and RL-based Secure Routing, thanks to optimized routing decisions and avoiding unnecessary transmissions, as well as adaptive trust-based communication. Moreover, the proposed framework achieves an improvement in the packet delivery ratio to 98.24%, decreases communication overhead by 9–17% and increases network lifetime by 10–19%. The results have also validated that FPSViT is a scalable, privacy-preserving, and power-saving security solution for dynamic MANET–WSN environments and is able to successfully resist advanced DL-driven Sybil attacks. Full article

Vehicular Ad Hoc Networks (VANETs) are essential components of Intelligent Transportation Systems (ITS), allowing communication exchanges between vehicles and road infrastructure elements. These networks face challenges from vehicular mobility, including frequent topology changes, link instability, and variable wireless channel quality. This paper presents an extensive evaluation of Software-Defined Networking (SDN) integrated with two traffic engineering technologies, Multi-Protocol Label Switching (MPLS) and Segment Routing (SR), applied to the AODV and OLSR routing protocols. Nine incremental configurations are evaluated for each protocol, ranging from the default protocol through MPLS-enhanced forwarding, SDN-based centralized optimization, combined SDN-MPLS and SDN-SR integration, to advanced configurations using distance-based IS-IS weighted topology metrics with both Fixed and Adaptive metric computation approaches. Two distinct SDN topology construction methods are compared: a Protocol-based approach that uses routing table entries with equal hop-count metrics, and a distance-based approach using IS-IS weighted metrics. The simulation uses a realistic urban topology with 50 vehicles and 5 RSUs, evaluated across several traffic patterns, representing different application types. Results demonstrate that SR with distance-based IS-IS metrics achieves the highest Packet Delivery Ratio (PDR) and lowest delay by leveraging RSU infrastructure as reliable forwarding relays. Moreover, the proposed SDN-SR framework reduces routing overhead and control-plane signaling, improving network resource utilization and thereby indicating its potential to enhance the energy efficiency of vehicular communication infrastructures. Full article

Renewable energy system (RES)-based electric vehicle (EV) charging infrastructure enhances energy security. The need for intelligent routing techniques that achieve low latency, high reliability and adaptive path selection under extremely dynamic traffic situations has increased owing to the quick growth of Internet of Things (IoT)-enabled vehicular networks. With an emphasis on recurrent neural networks (RNNs), deep belief networks (DBNs), radial basis function neural networks (RBFNNs), and long short-term memory (LSTM) networks, in addition to convolutional neural networks (CNNs), this analysis looks at cutting-edge AI-based models used for shortest-path routing in IoT-driven vehicular ad hoc networks (VANETs). The paper examines how various designs handle issues such as connection instability, heterogeneous sensor data, quick topological changes, and real-time decision making. A comparative analysis shows that DBN and CNN display strong feature learning for intricate mobility patterns and congestion recognition, while sequence-aware techniques like RNN and LSTM advance spatiotemporal traffic estimation. For low-latency route evaluation, RBFNN compromises rapid nonlinear representation. The examination shows that CNN models greatly improve the scalability, adaptability and optimality of routing, confirming AI-enabled structures as a promising path for next-generation IoT-based vehicular routing methods. Full article

Vehicular ad hoc networks (VANETs), as a key communication component of the Internet of Vehicles (IoV), enable vehicles and roadside infrastructure to exchange information efficiently, thereby supporting road safety and traffic management. However, because these communications take place over open wireless channels, VANETs are exposed to message forgery, replay, identity disclosure, and unauthorised access by revoked vehicles. To address these issues, this paper proposes EPAF, an efficient privacy-preserving batch authentication scheme with revocation support for fog-enabled VANETs. EPAF uses roadside fog nodes to distribute update keys and report information related to misbehaving vehicles, thereby reducing reliance on remote centralised processing. Rather than assuming ideal tamper-proof devices that store system-wide secrets, EPAF requires protected storage only for vehicle-local certificates, limiting the impact of compromising an individual vehicle device. The scheme employs batch verification to authenticate multiple messages from different vehicles in a single procedure, reducing verification overhead in message-intensive traffic conditions. It further introduces an update-key mechanism through which legitimate vehicles obtain current authentication keys, whereas revoked vehicles are prevented from generating valid authentication messages in subsequent revocation periods. Under the honest-authority model, the security analysis establishes the EUF-CMA security of an authentication packet in the random-oracle model and separately addresses conditional identity privacy, traceability, and unlinkability across different pseudonym periods. Performance evaluation examines the trade-off among authentication efficiency, communication overhead, and revocation performance, showing that EPAF is a practical solution for fog-enabled vehicular communication. Full article

Vehicle Ad Hoc Networks (VANETs) face critical challenges in trust management, privacy preservation, and scalability, particularly with the integration of 5G networks in Intelligent Transportation Systems (ITS). Traditional centralized trust models present single points of failure and privacy concerns that compromise network security and user anonymity. This paper presents a novel decentralized trust model leveraging blockchain technology, Interplanetary File System (IPFS) integration, and post-quantum cryptographic algorithms to address these limitations. Our proposed TrustChain-VANET framework implements advanced privacy-preserving encryption techniques including threshold and homomorphic encryption, geographical sharding for scalability, and edge-assisted consensus mechanisms. Performance evaluation demonstrates significant improvements: 40% reduction in authentication latency (90–120 ms vs. 150–300 ms), 90% malicious node detection rate (+15% improvement), 300% increase in transaction throughput (2000–2150 TPS), and 100% scalability enhancement supporting up to 5000 nodes. The system integrates seamlessly with 5G network slicing (URLLC, eMBB, mMTC) while maintaining quantum resistance through CRYSTALS-Dilithium, KYBER, and FALCON algorithms. Real-world deployment considerations including OBU computational constraints, standardization gaps, and energy efficiency are comprehensively analyzed. Results indicate that the proposed decentralized approach provides robust security, enhanced privacy, and improved scalability for next-generation vehicular networks, making it suitable for large-scale ITS deployment. The main contribution of this work is the development of a unified TrustChain-VA 48NET framework. The proposed framework integrates blockchain-based trust management, IPFS-assisted storage, 5G network slicing, Mobile Edge Computing (MEC), geographical sharding, and post-quantum cryptographic mechanisms within a single architecture for next-generation VANET environments. While these technologies have been investigated separately in previous studies, this work presents a consolidated framework that analyzes their interoperability, identifies integration challenges, and evaluates their combined impact on trust management, scalability, privacy preservation, and deployment feasibility in Intelligent Transportation Systems. Full article

With the rapid development of Vehicular Ad Hoc Networks (VANETs), Vehicle-to-Infrastructure (V2I) communication plays a critical role in Intelligent Transportation Systems (ITS). However, existing authentication and key exchange protocols face challenges such as high computational cost, large communication overhead, and security and privacy risks in high-speed mobile environments. To address these problems, this paper proposes a lightweight V2I authentication key exchange and ticket-based fast handover authentication protocol based on consortium blockchain and a Physical Unclonable Function (PUF). The proposed framework integrates PUF-based device binding, biometric-assisted user binding, PRF-based dynamic pseudonym update, target-RSU-bound handover tickets, and consortium blockchain-assisted auditability. To avoid privacy leakage on immutable ledgers, the blockchain stores only keyed pseudonym indexes, cryptographic commitments, timestamps, revocation states, and audit records, whereas biometric helper information, PUF-derived values, long-term secrets, handover keys, and session keys are protected in TPM/HSM or encrypted off-chain storage. Formal verification using ProVerif indicates that the revised protocol satisfies the modeled secrecy properties, injective mutual authentication for initial authentication and handover, and non-injective ticket origin authenticity for accepted handover tickets. In addition, the Real-or-Random (RoR) model is used to prove fresh session key indistinguishability under explicit pre- and post-Test freshness, PUF unpredictability, fuzzy extractor security, and hardware-protected secret assumptions. Analytical performance evaluation further shows the core cryptographic cost of the proposed scheme while explicitly separating and parameterizing deployment-dependent TPM/HSM, AEAD, blockchain lookup, and PBFT confirmation costs. Full article

After an earthquake or major traffic accident, emergency rescue vehicles may be unable to access the accident site or communicate with the survey drones at the scene. In such cases, an emergency rescue vehicle can select any nearby drone as a communication relay to establish a connection with survey drones at the accident site, thereby forming a drone-assisted Vehicular Ad Hoc Network (VANET) to break through geographical constraints. These networks are characterized by flexible deployment, rapid survey, and real-time transmission of rescue data. However, existing authentication protocols for drone-assisted VANETs are insufficient in security and privacy protection, making them vulnerable to side-channel attacks, device capture attacks, and identity impersonation attacks. In addition, the excessively high computational overhead makes it difficult for them to meet the requirements of low latency and high security for real-time communication in emergency rescue. Therefore, this paper proposes a lightweight drone-assisted emergency rescue authentication protocol based on Physical Unclonable Functions (PUF) and the Chebyshev chaotic map. In this protocol, the authenticating entities, including emergency rescue vehicles, relay drones, and survey drones, register with the Trusted Authority (TA) in a symmetric manner and also authenticate each other’s identities and establish session keys symmetrically. The proposed scheme not only resists side-channel attacks and device capture attacks but also addresses the single point of failure problem of relay drones. Both formal and informal security analyses verify its security, and comparative experiments show that it has lower computational and communication overhead, making it more suitable for emergency rescue application scenarios. Full article

The recent advances in Vehicular Ad Hoc Networks (VANETs) can have a tremendous positive impact on vehicle safety and traffic flow. In VANETs, vehicles communicate wirelessly with each other and with roadside infrastructure nodes to improve awareness of neighboring vehicles and traffic conditions. However, such communication also increases the potential for various safety and security challenges, such as the threat of false reporting attacks. In these attacks, malicious or compromised nodes inject alert notifications that report fictitious traffic incidents that may trigger unnecessary evasive actions and increase the risk of collisions. This research addresses false alert attacks in VANETs by developing a machine learning-based detection framework that leverages innovative feature engineering and model assessment strategies. The proposed framework designs new features that capture vehicle kinematics to improve the detection of malicious alerts. The performance of multiple ML models is then analyzed in terms of both detection effectiveness and computational requirements to determine their suitability for different deployment scenarios. Our simulation results demonstrate that the proposed framework can achieve significant improvements compared to existing techniques for false alert detection. In addition, the study highlights the critical role of feature engineering in improving detection performance. Full article

Vehicular Ad Hoc Networks (VANETs) are mobile networks that offer new services and communication between moving vehicles, roadside infrastructure, and a trusted authority. With the development of autonomous and connected vehicles, the issue of authentication in VANETs has become increasingly prominent due to the lack of mutual trust among network entities. However, standard authentication models for VANETs must account for total computational and communication overhead, regardless of the timing of authentication message generation. To address this limitation, this work proposes an advanced authentication paradigm for VANETs called the online/offline VANET framework, and formalizes this novel framework to realize lightweight authentication by shifting heavy computational overhead to the offline phase. The proposed model is divided into an offline phase and an online phase. In the offline phase of the free time before the message becomes available, it allows more powerful trusted authority to pre-compute, and in the online phase, resource-constrained devices only execute a small set of residual operations. Based on this model and a new identity-based signature, we give an efficient instantiation and use a mobile platform to evaluate it. The experimental results demonstrate that our construction achieves low online computational and communication overhead. Full article

A blockchain-based framework is proposed for secure vehicle registration and real-time authenticity verification in vehicular networks. To mitigate the risks of fake and stolen license plates, vehicle identification data is protected using a modular arithmetic-based cryptographic mechanism and indexed within an on-chain hash table structure. Role-based access control ensures system integrity by restricting all registration and modification operations to authorized government entities, while enabling public verifiers to validate vehicle legitimacy through privacy-preserving verification. Experimental evaluation demonstrates that the system achieves low verification latency, minimal storage overhead, and stable throughput. Furthermore, scalability and denial-of-service (DoS) resilience analyses confirm consistent performance under high verification demand. This framework offers an efficient and privacy-preserving solution for the secure and real-time verification of vehicle legitimacy in vehicular networks. Full article

Despite significant improvements in vehicle management efficiency achieved through the integration of VANET and Internet of Things technologies, Internet of Vehicles (IoV) networks remain vulnerable to cyberattacks. This is because the wireless data exchange channel in IoV has several vulnerabilities that are exploited to carry out cyberattacks. The article suggested using the symmetric block cipher GOST 34.12-2015 (SBCG) to combat a variety of cyberattacks. This cipher was chosen because it can be efficiently implemented on low-power platforms and offers high cryptographic strength and encryption speed. Furthermore, implementing SBCG in polynomial modular codes (PMCs) enables detection of encryption errors caused by faults in encoder/decoder operation. The scientific novelty of the proposed solution is that it is the first method to increase the fault tolerance of an SBCG encoder, enabling real-time, effective countermeasures against faults caused by both Differential Fault Analysis (DFA) attacks and natural faults. The originality of the solution lies in the integration of cryptographic theory and the theory of constructing correcting modular codes. The goal of this study is to improve the resilience of SBCG encryptors/decoders to faults by using polynomial modular codes. Imparting fault-tolerant properties to SBCG encryption systems implemented in PMC will enable them to effectively mitigate real-time faults arising from both Differential Fault Analysis (DFA) attacks and natural faults. Full article