ABSTRACT Smart cities are digitally advanced urban environments that are equipped with sensor networks to gather, share, and analyze extensive data across interconnected systems. Among various smart city applications, the intelligent transportation system represents one of the most critical and securityâsensitive domains. An intelligent transportation system relies heavily on continuous vehicular communication, a lowâlatency decisionâmaking process, as well as realâtime traffic monitoring. Existing Internet of Things security methods encounter significant computational overhead and limited scalability, making them unfit for realâtime applications. To address these issues, this paper proposes a novel security model, named Deep Residual Stacked Bidirectional Network. The proposed system is integrated into a blockchainâsupported hybrid system to ensure security and privacy for users and systems in smart cities. This enhanced DeepâLearning model combines the residual learning power with bidirectional long shortâterm memory layers. To effectively manage deeper networks, residual connections help mitigate the vanishing gradient problem, while bidirectional long shortâterm memory provides sequential dependencies in backward and forward directions. This allows the model to detect patterns in data, especially in security environments where data is highly dynamic and timeâsensitive. Four Internet of Thingsârelated datasets are used to evaluate the efficiency of the developed algorithm. These datasets offer various realâworld network traffic and attack scenarios that allow comprehensive performance evaluation of the proposed approach in comparison with existing methods. The test outcomes revealed that the blockchainâsupported proposed method outperforms traditional methods with an accuracy of 98.21%, specificity of 97.39%, and F1âscore of 97.46%.
Wisam Makki Alwash, Weam Husham Aljabbari, Muhammed Ali Aydın, Hasan H. Balık
Internet of Vehicles (IoV) intrusion detection systems (IDSs) require collaborative learning that preserves raw-data locality while producing independently checkable post-run evidence. This paper presents FL-BC-IDS, an evidence-native, privacy-aware hierarchical federated IDS in which vehicles train Differentially Private XGBoost models, roadside units perform deterministic admission and tree-bagging aggregation, and the GLOBAL stage forms an equal-weight ensemble over validated RSU models. Signed reports, privacy records, SHA-256/Poseidon commitments, scoped Groth16 proofs, reconstructable public inputs, and digest-pinned blockchain receipts provide a unified verification path. Across 10 seed-controlled runs, the mean ± SD accuracy/F1 values were 0.998021±0.000246/0.983597±0.002053 on CSE-CIC-IDS2018 and 0.999867±0.000152/0.999495±0.000579 on CICIoV2024. With thresholds fixed exclusively from development data, the strict held-out-attack macro recall was 0.8031 and 0.9090 on CSE-CIC-IDS2018 and CICIoV2024, respectively, indicating residual attack-specific generalization limitations; supervised rolling-origin temporal refresh on CSE-CIC-IDS2018 achieved 0.984788 pooled seen-attack recall at a 0.005700 test FPR. A controlled 20-vehicle, eight-round heterogeneity and participation stress test retained 0.998151 accuracy and 0.984782 F1-score. Verification rejected invalid or context-mismatched artifacts and independently checked modelâanchor consistency, RSU aggregation replay, commitments, and public inputs. The reported DP budgets are conditional learner-stage bounds for learner-input record instances, not end-to-end guarantees for original pre-preprocessing records.
Internet of Vehicles (IoV) security mechanisms often classify isolated messages or assign node-level trust scores, yet these decisions do not explain whether a malicious but authenticated event has distorted the intended evolution of traffic. This paper proposes IntentProv-IoV, a causally grounded provenance framework for traffic-intent preservation in V2X environments. Traffic intent is modeled as the short-horizon collective state expected under non-adversarial conditions, and deviation is measured between predicted and observed traffic states. The framework constructs temporal provenance graphs linking vehicles, roadside units (RSUs), cooperative perception outputs, prediction nodes, and traffic-control decisions. To remove the ambiguity of marginal contribution, node contribution is formalized as an interventional effect in a structural causal model and estimated through Monte Carlo counterfactual edge-weight attenuation, with a linear sensitivity fallback for real-time edge deployment. A calibrated composite score integrates anomaly evidence, traffic-intent deviation, trust risk, and provenance contribution. The evaluation design compares IntentProv-IoV with detection, trust, blockchain trust, graph anomaly, Granger causal, structural causal, and counterfactual GNN baselines and includes predictor sensitivity, adaptive adversaries, prediction noise, packet loss, trajectory-only real-data validation, and edge overhead. Simulation-scale results indicate improved attribution precision, stronger traffic-intent deviation reduction, and edge-suitable latency. By shifting V2X security from message-level detection to causally explainable traffic-intent assurance, IntentProv-IoV provides a more accountable security objective for cooperative vehicular systems.
Roadside edge infrastructure is becoming an active execution layer in connected transportation. Roadside units, edge servers, and vehicular gateways not only relay safety messages, but also run local services and support traffic-control decisions. Once these nodes are compromised, data authentication, blockchain logging, or forensic evidence management cannot by itself guarantee service trustworthiness, because the node that senses, signs, forwards, or processes the information may already be running an unauthorized software stack. This paper presents RISE-CT, a runtime-integrity assurance and self-healing recovery framework for roadside edge infrastructure. RISE-CT models each node through a layered security-state graph covering hardware roots, boot chains, firmware images, runtime processes, key usage, and service status. It combines boot-time, periodic, and event-triggered attestation with runtime drift diagnosis to detect firmware deviation, process injection, abnormal key access, replayed evidence, and service-behavior changes. When the risk score exceeds policy thresholds, a risk-aware admission state machine moves the affected node into degraded, quarantined, or recovering states, and coordinates service migration, key renewal, firmware rollback, and re-attestation before safe re-admission. Seed-controlled emulation under firmware tampering, malicious OTA updates, runtime injection, key misuse, replayed attestation, and roadside-service hijacking shows that, under the evaluated emulation settings, RISE-CT reduces the representative detection delay to 0.98 s and service interruption to 0.82 s, while achieving a 0.94 re-admission success rate with controlled attestation overhead. The results suggest that runtime node integrity can provide a potential security mechanism for improving the trustworthiness of roadside infrastructure under the evaluated conditions.
The advancement in smart mobility communication technology enables intelligent vehicles to interconnect and communicate with each other, improving traffic safety and efficiency. However, the highly dynamic nature of the smart mobility network and vehicle behaviour creates the requirement for effective authentication systems to establish secure and reliable communication between vehicles. The implementation of a reputation system has been proposed to establish trust among untrusted vehicles, where the reliability of a propagated message is assessed based on the reputation of the sender vehicle. Thus, it prevents malicious vehicles from potentially broadcasting misleading messages that can cause accidents or disrupt the network. This paper proposes a novel decentralised and dynamic reputation management and computation model based on a consortium blockchain and a multi-signature smart contract. The implementation of blockchain and a smart contract provides a secure and transparent framework for registering vehicles, submitting events, voting feedback, evaluating reputation, and blocking malicious vehicles. To demonstrate the feasibility of the proposed model, we conducted security and performance analyses. The results show how our model can provide resistance against various system and reputation manipulation attacks, such as data tampering, message forging, self-promotion, vote duplication, bad-mouth, on-off, time-dependent, and collusion attacks. Additionally, the smart contract performance illustrates execution time within a millisecond or less, which demonstrates the practicality of our design for the smart mobility applications.
K. Satheshkumar, S. Ramalingam, A. Suresh Babu, S. Murugesan
ABSTRACT Vehicular ad hoc networks (VANETs) are essential components of intelligent transportation systems that facilitate realâtime communication between vehicles (V2V) and between vehicles and infrastructure (V2I). Despite their importance, VANETs face challenges, such as high node mobility, energy limitations, security risks, and everâchanging network topologies. Existing clustering and routing algorithms often struggle to manage the instability caused by mobility, energy disparities, and secure congestionâfree communication simultaneously. To address these challenges, this work introduced an integrated crossâlayer framework featuring three innovative algorithms: mobilityâaware black hole clustering (MâBHC), energyâaware piranha optimization algorithm (EPOA), and crossâlayer multiâattribute blockchain routing with congestion control (CLâMABRC). The MâBHC algorithm enhances the stability of clusters and counters blackâhole attacks by forming clusters dynamically based on realâtime vehicle mobility patterns. EPOA optimizes the selection of cluster heads (CHs) by reducing energy consumption through a bioâinspired resource allocation strategy modeled on piranha predation behavior. CLâMABRC addresses network congestion and security using blockchainâbased verification and crossâlayer routing decisions informed by multiâattribute metrics. Extensive simulations were conducted with a setting of 100 veh/km 2 . The proposed framework showed significant performance improvements over benchmark protocols, such as optimal securityâaware clusterâbased hybrid geographical and opportunistic routing (OSCâGOR), enhanced locationâaided ant colony routing (ELAACR), trustâbased multiâobjective honey badger algorithm (TMOHBA), and robust cryptographic scheme for reliable data communication (RCSRC). It achieved a throughput of 99.89 Kbps, endâtoâend delay of 3.9 ms, collision rate of 21.8%, energy consumption of 41.98%, and jitter of 0.05 ms. Together, the MâBHC, EPOA, and CLâMABRC algorithms create a robust, energyâefficient, and secure communication framework for VANETs, enhancing scalability, reliability, and realâtime performance in transportation systems.
Ravindra Janardan Lawande, Sudhir Bapurao Lande, Manisha Lande
Internet of Vehicle (IoV) uses heterogeneous access technologies to link automobiles and their surroundings. Effective methods are essential for safeguarding data confidentiality and privacy during communication among the roadside unit (RSU), the control room, and vehicles. Many vehicle-to-infrastructure authentication-based approaches have been developed to secure the IoV environment. However, efficiency and security are challenged by instability, decentralization, and transaction-tracking features. To resolve this, a secure, lightweight, and scalable communication protocol was developed for a 5G-enabled SDN-IoV environment. Efficient block verification is achieved through the Joint-Graph Delegated Practical Byzantine Fault Tolerance (JtGr-DPBFT) mechanism, in which validators create subgraphs to reduce communication overhead. JtGr-DPBFT is combined with an Improved Gossip Algorithm (IGA) to minimize message redundancy and optimize bandwidth utilization. Moreover, a lightweight hierarchical authentication mechanism, assisted by a Merkle Tree with Boneh-Lynn-Shacham (HAMT-BLS) signatures, enables compact block verification and minimizes computational and communication costs. The proposed model achieves tamper-proof, efficient, and scalable block verification by incorporating hierarchical authentication with consensus optimization. This approach is simulated in the NS3 tool, and performance is evaluated in terms of propagation delay, transaction confirmation latency, throughput, communication cost, and network delay. Thus, secure and tamper-proof communication is developed to ensure integrity, trust, and dependability in the SDN-enabled IoV environment.
Patikiri Arachchige Don Shehan Nilmantha Wijesekara, Kalupahana Liyanage Kushan Sudheera, Harsha S. Gardiyawasam Pussewalage, Geeth P. Wijesiri N. B. A
Incorporating connected mobility data into decision-making brings about significant data security and privacy challenges in next generation transportation systems. The problem becomes worse when the raw traffic data stream contains commuters' sensitive information that could be extracted by successful attackers. The challenges of data security and privacy must be addressed in order to create a secure information sharing environment for next generation transportation systems. â Blockchain technology, which provides a tamper-resistant journal of state transition events, becomes an ideal candidate for realizing the goals of creating secure information sharing frameworks. However, existing blockchain technology and deployments also have their limitations, especially in data privacy. Because the saved data on the blockchain ledger can not be altered, we do not want to make sensitive information publicly available or record false information permanently without an authentication protocol. â Innovations are needed to overcome barriers in blockchain technology for enabling secure and privacy-preserving information sharing in next generation transportation systems. This Ph.D. dissertation introduces three main innovations: (1) a zero-knowledge and Byzantine fault tolerant consensus that brings privacy-preserving to the blockchain consensus level for verifying and processing transactions, Chapter 2; (2) novel privacy-preserving authentication schemes for blockchain networks based on zero-knowledge proofs to increase security and safety in traffic management, autonomous truck fleets and ridesharing, Chapters 3, 4 and 5; and (3) blockchain-inspired architecture designs with access control policies that protect huge amounts of traffic and users' data and log access events into blockchain for traceability and accountability, Chapters 4 and 5.
The automotive industry is transitioning to Zonal-oriented Architectures (ZoA) for Software-Defined Vehicles (SDVs), enabling frequent over-the-air (OTA) updates for 100+ Electronic Control Units (ECUs). While OTA updates improve efficiency, they introduce safety-critical security risks. Current standards like Uptane and AUTOSAR Adaptive rely on Public-Key Infrastructure (PKI). However, PKI-based authentication creates bandwidth bottlenecks in in-vehicle and vehicle-to-cloud (V2I) communication as ECU density increases. It also risks exposing sensitive vehicle configurations and passenger privacy due to centralized architectures. Next-generation Zonal SDVs require decentralized, scalable authentication with data privacy. To address this, we propose zk-ScalHard, a hardware-rooted, privacy-preserving authentication protocol. We introduce a decentralized, hierarchical trust-promotion model utilizing Silicon Physical Unclonable Functions (PUFs) and two novel Zero-Knowledge Proof (ZKP) circuits: (1) Zonal Identity and Integrity (ZIDI) and (2) High-Performance Computing Aggregation (HPCA). These circuits employ multi-party computation (MPC) and recursive aggregation to achieve decentralization and scalability. The integration of ZKPs and PUFs ensures 100% vehicle-level data sovereignty. Benchmarked against Uptane, zk-ScalHard achieves constant O(1) communication and verification complexity, improving upon the linear O(n) complexity of current systems. Evaluation shows a 99.2% reduction in authentication bandwidth and a 99.9% reduction in the temporal attack surface. Our results demonstrate that zk-ScalHard provides a scalable, secure, and GDPR-compliant architecture for future Zonal SDVs.
Open access
3 source records
cs.CR
Physical Unclonable Functions (PUFs) and Hardware Security
Federated Learning (FL) enables privacy-preserving collaborative learning for Internet of Vehicles (IoV) scenarios, but extreme heterogeneity of vehicular-edge-cloud resources severely limits system efficiency. Dynamic scheduling strategies mitigate this issue but introduce new trust concerns: verifying fair scheduling decisions and faithful client execution of compression instructions without privacy leakage remains an open challenge. We propose Nautilus, a verifiable efficient federated learning framework. First, a multi-dimensional resource-aware scheduling algorithm dynamically allocates compression ratios and training tasks based on vehicle bandwidth, latency and computing power, improving training efficiency. Second, a Zero-Knowledge Proof (ZKP) mechanism ensures scheduling fairness and execution compliance while preserving privacy. Experiments show the framework reduces communication overhead and accelerates convergence with guaranteed system integrity.
X M Liu, Yilai Lian, Likai Jia, F H Wang · 8 authors
With the rapid development of the Internet of Vehicles (IoV), achieving trustworthy vehicle position verification while preserving location privacy has become a key requirement in intelligent traffic supervision scenarios such as defense control zones and urban restricted-access areas. Existing privacy-preserving schemes have difficulty simultaneously supporting accurate determination of complex-shaped prohibited areas and efficient computation, and still face malicious attacks such as interference with verification procedures, tampering with communication processes, and privacy inference when determining the positional relationship between vehicles and prohibited areas. To address these issues, this paper proposes an efficient privacy-preserving position verification (PPPV) scheme based on secure multi-party computation (MPC). The scheme supports arbitrary polygonal prohibited areas, including convex, concave, and self-intersecting polygons, thereby improving its applicability in complex IoV supervision scenarios. Based on an improved cross-product determination method, this paper constructs an efficient PPPV protocol under the semi-honest model, achieving near-plaintext computational efficiency while protecting the privacy of both vehicle locations and area boundaries. To resist malicious attacks, this paper further combines Paillier homomorphic encryption, the cut-and-choose method, and zero-knowledge proof to construct a secure PPPV protocol under the malicious model, which can effectively prevent protocol deviations, result tampering, and inference attacks. This paper also conducts formal security proof based on the real/ideal model paradigm, and evaluates the performance of the scheme through benchmark experiments and attack experiments. Experimental results show that the scheme achieves a good balance among efficiency, applicability, and security, providing a deployable trustworthy position verification mechanism for next-generation IoV intelligent supervision applications.
Proof-of-Stake DAG-BFT consensus faces a trilemma between sybil resistance, reward fairness, and plutocracy. Existing protocols prioritize liveness over fair stake-based selection, driving longitudinal centralization. FairWave is a dual-channel DAG-BFT protocol that separates anchor selection from reward distribution. The selection channel is super-linear in stake, guaranteeing Sybil gain < 1 for K > 1; the reward channel is sub-linear via square-root stake normalization. DAG-derived uptime and latency metrics eliminate external oracles,and lagged reputation breaks circular dependency between selection outcomes and weights. Evaluated through approximately 550,000 Monte Carlo rounds against eight baselines, FairWave shows Gini 0.140 (vs. Pure-PoS 0.490, monotone HHI reduction from 0.039 to 0.020 over 50,000 epochs, and optimal Sybil split K * = 1. Safety follows unconditionally from the 2f + 1 commit rule; the liveness model predicts monotone degradation from 94.0% at b = 0.20 to 74.0% at b = 1/3, consistent with the architectural expectation of no discontinuous cliff.
Mohammad Alsaffar, Eman Abouelkheir, Wedad Alawad, Majed S. Alsayfi · 8 authors
The ultra-dense vehicle scenarios envisioned in 6G put high requirements on ultra-low latency, secure cooperation, and efficient task offloading decisions. Existing systems usually optimize latency or energy independently but ignore joint privacy problems and long-term trust sustainability. In this work, a distributed intelligence architecture based on the combination of federated learning (FL) and blockchain based trust management for vehicle-to-vehicle (V2V) edge computing is proposed. The proposed architecture enables collaborative prediction and decentralized incentive enforcement in a privacy-preserving manner without revealing raw vehicle data. In this paper, task allocation is defined as a multi-objective optimization problem, which jointly considers latency, energy consumption, communication stability and privacy exposure. The resultant problem is addressed by a learning-coupled primal-dual optimization, where the federated prediction is used to drive the offloading decisions and the dual update is used to impose the limitations of the system. A light-weight distributed ledger layer ensures secure coordination, automatic incentive allocation and reliable detection of fraudulent nodes. The extensive simulations in the integrated traffic-network-blockchain environments show that the proposed method outperforms the state-of-the-art baselines, achieving up to 30-40% reduction in the service latency, approximately 25% improvement in task completion rate, enhanced privacy preservation by the gradient-based learning, and up to 95% accuracy in detecting the malicious nodes. These results validate the efficacy of the suggested framework for attaining scalable, privacy-aware, and trustworthy distributed intelligence for next-generation 6G vehicular edge networks.
The Internet of Vehicles (IoV) is changing the contemporary mobility, as it allows real-time communication between vehicles, infrastructure, and cloud services. Nevertheless, such growing connectivity brings on serious privacy, regulatory, and trust issues especially because sensitive behavioral and location information is exposed. The current IoV-security systems tend to be based on identity-based checks, or centralized trust authorities, which can lead to infringement of user privacy and cause surveillance and profiling threats. The paper is inspired by privacy-preserving architectures in the Metaverse to suggest a decentralized trust system of IoV systems on the basis of zero-knowledge proofs, namely zk-SNARKs. The suggested solution allows vehicles to cryptographically verify that they meet regulatory or operational regulations- i.e. valid insurance, safety test, or emissions- without revealing personal identifiers or raw information. The framework enables building scalable, low-latency and audible trusts and following data minimization principles through combining zk-SNARK verification and Layer 2 blockchain solutions.
In the Internet of Vehicles (IoV), the large-scale deployment of smart vehicles has triggered new road traffic safety challenges. Particularly, existing vehicle accident report transmission schemes still face challenges such as privacy leakage, Single Point of Failure(SPOF), physical cloning attacks, and excessive computational overhead. To address these issues, this paper proposes a secure accident report transmission scheme that uses Non-Interactive Zero-Knowledge Proof (NIZKP) and Physically Unclonable Functions (PUF). This paper designs a decentralized authentication scheme for vehicle registration that prevents SPOF and privacy leakage. We also use the PUF to realize two-factor authentication login, which effectively resists physical cloning attacks. In addition, the authentication process uses NIZKP based on the Pedersen commitment to realize authentication for accident report coordination. At the end of the accident report coordination, it is passed into the blockchain for storage, realizing the secure transmission of accident reports. To reduce the storage as well as computation overhead, this paper uses a key derivation function to update the key. Finally, formal security analysis was conducted using the Real or Random (ROR) model and the ProVerif tool, the results prove that the proposed protocol meets security requirements. Comparing our proposed scheme with related schemes, the computational overhead of our V2V scheme is reduced by 42.4%, with higher security and lower communication overhead.
Open access
Vehicular Ad Hoc Networks (VANETs)
Physical Unclonable Functions (PUFs) and Hardware Security