Introduction

The introduction of intelligent transportation systems (ITSs) has revolutionized the life of the modern world by making traffic more efficient, reducing the number of accidents, and, last but not least, providing autonomous vehicles with the ability to communicate with other cars with ease. The autonomous vehicle networks (AVNs) at the heart of the ITS rely on vehicle-to-everything (V2X) communication, which includes vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-cloud (V2C) communication to ensure the safety of autonomous vehicles and organize their activities. However, the enhanced network integration of AVNs has presented more complex cybersecurity risks. According to the U.S. National Highway Traffic Safety Administration (NHTSA, 2023), over 50 safety incidents associated with cybersecurity issues in connected cars have been reported in the past five years. Similarly, the European Union Agency on Cybersecurity (ENISA, 2022) documented an increase in cyberattacks on ITS infrastructures of 30% annually. Gartner (2023) also believes that nearly one-fourth of cyberattacks against critical infrastructure will be related to mobility systems in the future, which will likely be linked to connected systems and accordingly invoke the necessity of taking the initiative to protect the system. The threat environment has also increased with the emergence of quantum computing. Classic cryptography systems such as RSA and ECC, which were previously considered to be secure, have become vulnerable to quantum algorithms such as the Shor and Grover algorithms, which can be broken within polytimes. This is one of the basic threats to the confidentiality, authenticity, and integrity of V2X communications. Moreover, existing security solutions, such as intrusion detection systems and standard blockchain solutions, are scaled and have latency and quantum-capability adversary-resistance constraints. This susceptibility makes it clear how important the immediate need to introduce a set of quantum-resistant and real-time threat detection measures is. While prior studies, such as those by Su et al. (2024)1 on hybrid blockchain for VANET privacy and Liu et al. (2020)2 on deep learning for image recognition in autonomous driving, have addressed isolated aspects of blockchain or deep learning in vehicular security, they often overlook quantum threats or fail to integrate real-time anomaly detection with decentralized data integrity. In contrast, our work bridges this gap by proposing a holistic framework that combines postquantum lattice-based cryptography, the zk-SNARK-enhanced blockchain for scalable privacy, and adversarial-trained deep learning models (CNN-RNN hybrids) for proactive threat mitigation, achieving superior performance metrics (e.g., 95.2% detection accuracy and 99.9% data integrity) not demonstrated in the literature.

The originality of the current work is that it offers a comprehensive approach to cybersecurity:

  • A postquantum encrypted protocol is proposed for AVNs so that they are future proofs against quantum-enabled cyberattacks.

  • Architects a decentralized blockchain framework of secure, tamper-free and privacy-protected V2X data exchange.

  • Deep learning anomaly detectors and real-time threat mitigation systems with large vehicular data streams are combined.

  • Implementation of a hybrid architecture to balance multilayered threats in terms of scalability, efficiency and resilience.

Unlike Delgado et al.3 and Rayudu et al.4, who focus on deep learning for noncybersecurity applications such as motion estimation or weather prediction, or Masurkar et al.5 on decentralized AI without quantum resistance, our integration ensures resilience against both classical and quantum adversaries while maintaining real-time efficiency in ITS environments.

Literature review

Summarizing existing cybersecurity in ITSs

Vehicle-to-everything (V2X) interactions are secured by traditional mechanisms that include encryption algorithms that are symmetric, asymmetric, or hybrid and digital signatures and a public key infrastructure (PKI)6. The intrusion detection systems (IDSs) and firewalls serve as complementary defenses because they monitor unauthorized access and overt activity. Nevertheless, owing to the growing complexity and network connectivity of autonomous vehicle networks (AVNs), vulnerability to more advanced threat attacks, such as adversarial machine learning attacks, spoofing, ransomware, and cyber-physical coordinated attacks, has been introduced. According to recent activity, the industry has reported that cyberattacks on connected vehicles are increasing, with ENISA (2022) recording 30% annual growth in incident occurrences targeting ITSs, and as such, good cybersecurity measures are needed to mitigate threats and security breaches. A Constraint of Classical Cryptographic Algorithms in the Quantum Age.

Although classical cryptographic algorithms have in the past proven secure against classical attacks, the threat of quantum computing poses an extreme challenge7,8. Quantum algorithms such as the Shor algorithm and Grover algorithm can potentially factor the numbers much faster than a classical computer could use integer factorization and discrete logarithm problems and easily break the commonly used RSA and ECC encryption schemes in polynomial time, and quantum computers of a massively higher scale may be able to perform similarly with much more significantly advanced capabilities9,10. This makes ensuring the confidentiality, authenticity, and integrity of V2X communications via conventional methods of encryption ineffective. Therefore, quantum-resistant cryptography protocols need to be formulated and implemented for future-proof AVNs against all-time cyberattacks [ENISA, 2023; NHTSA, 2023]11.

Novel contribution

Although blockchain and deep learning have demonstrated potential individually in the securing of vehicular networks, their individual application has many gaps. Classical blockchain models have scalability problems, latency problems and quantum vulnerability problems, whereas deep learning models have adversarial attack problems, privacy problems, and computational overhead problems. To the best of our knowledge, no previous research has been able to integrate quantum-resistant blockchain schemes and adaptive deep learning in a single architecture with ITS cybersecurity. The originality of the given work is expressed through four major aspects:

Quantum-resistant Blockchain- The proposed system incorporates lattice-based cryptography and uses SHA-3 hashing in place of the classical blockchain to ensure that vehicular data are not subjected to quantum-vulnerable attacks to ensure that data integrity and privacy are maintained over an extended time. This differs from Ansari et al.12 and Chaudhari et al.13, who applied blockchain for privacy in healthcare without quantum-resistant elements or ITS-specific scalability optimizations14.

AI-Informed threat detection- Deep learning models (CNNs and RNNs) analyse real-time streams of vehicular data to identify anomalies in such a way that preemptive detection is possible for cyberattacks that cannot be mitigated through the application of simple cryptographic controls. In contrast to Wu et al.15 and Gaifulina et al.9, who use deep learning for multimodal fusion or IoT anomaly detection without blockchain integration, our models incorporate adversarial training for enhanced resilience in dynamic AVN scenarios16.

Hybrid and scalable architecture- This innovative combination of the decentralized reliance model of blockchain with the predictive intelligence of deep learning produces multilayered security to withstand quantum assault, adversarial AI, and system breaches and is both scalable and feasible for real ITS applications. This holistic approach surpasses those of Kusi et al. (2020)17 and Yadav et al. (2022)7, which emphasize decentralized ML training or disease management without addressing quantum threats or V2X communication latency18.

Research gap Bridging This study is more direct because it attempts to bridge the research gap in the knowledge of the most effective component of ITS security through the integration of quantum resistance, privacy-preserving blockchain (zk-SNARKs), and deep learning-based anomaly detection into one model. To further illustrate these differentiators, we introduce Table 1, which compares our work against selected prior articles19.

Table 1 Comparative differentiation of our contributions from prior Works.
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The Vehicular Networks Applications of Blockchain.

Blockchain technology has become a disruptor of improved cybersecurity in vehicular networks. Decentralization and tamper-immune repositories of ledgers are offered by the blockchain process as an instrument to offer secure, transparent, and auditing data transfer between vehicles, infrastructure, and cloud systems. The applications can be made as important as:

  • Safe V2X communications: Proof-of-all-things and other consensus protocols can make data tampering impossible and authenticate the data.

  • Identity Management: In blockchain, identifiers are used to eliminate spoofing and/or unauthorized access.

  • Data Integrity: Sensor and vehicle logs, along with other automotive data, are mutation safe.

Despite being the same, blockchain has scalability, latency and energy consumption issues, which are essential in the implementation of blockchain in real-time ITS environments. They have been proposed to address these limitations through the aid of optimized structures and hybrid blockchain designs [Gartner, 2023; recent ITS blockchain research]20,21. Deep learning and its implementation in: anomaly detection/cybersecurity. Deep learning processes have been shown to possess exceptional features for recognizing aggressive vehicles in intricate highway systems. These models operate with a wide range of data, such as sensor readings, communication history, and system telemetry history, to detect anomalous behavior, which can be used as indicators of a cyber-attack. Methods such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are usually augmented by adversarial training, have been shown to be more resistant to more complex attacks. Despite these accomplishments, the problems of privacy of their information, computing expenses and susceptibility to adversarial instance continue to exist to restrain their potential use in operational ITS systems.

Research gaps

Although prior studies have been conducted on the individual characteristics of blockchain and deep learning in ITS cybersecurity, an adequate analysis of pairs has not been performed. This goes hand in hand with an absence of an overall template that will be centralized in one place:

  • Quantum computer cryptography or long-term security. Existing works such as Zhang et al.22 address blockchain for metaverse privacy but ignore quantum vulnerabilities in vehicular contexts.

  • Blockchain data sharing on a nanoscale level. Studies such as Khan and Munir6 explore decentralization but lack ITS-specific optimizations for high-throughput V2X.

  • Real-time deep learning anomaly detection. While Kavitha and Amutha8 analyse DNN/LSTM for intrusion detection, they do not combine it with a quantum-resistant blockchain for comprehensive resilience.

This study fills these gaps by suggesting a synergistic cybersecurity architecture as a supplement to quantum-resistant blockchain with deep learning algorithms that will offer added security, resilience, and reliability to AVNs in the future in ITS ecosystems.

Related work

Prior research has explored blockchain and deep learning for cybersecurity in domains such as healthcare, vehicular networks, and general AI applications. However, these studies often focus on isolated technologies, neglecting quantum threats, integration challenges, or real-time scalability in intelligent transportation systems (ITS). Below, we critically analyse key works, highlight their shortcomings and demonstrate how our proposed framework—integrating a quantum-resistant blockchain with deep learning for autonomous vehicle networks (AVNs)—addresses these gaps.

Delgado et al.3 proposed a deep learning approach using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with transfer learning to estimate hand joint angles from surface electromyography (sEMG) and inertial measurement unit (IMU) data. While they are effective for human‒machine interactions and achieve slight improvements over sEMG-only methods, this work is limited to noncybersecurity applications. It overlooks adversarial vulnerabilities in deep learning models and lacks secure data-sharing mechanisms such as blockchain, making it unsuitable for ITSs. Our framework adapts CNNs and RNNs for real-time anomaly detection in vehicle-to-everything (V2X) data streams, incorporating adversarial training for resilience and quantum-resistant blockchains (using lattice-based cryptography and SHA-3 hashing) for tamper-proof data integrity, extending the applicability of deep learning to ITS cybersecurity.

Rayudu et al.4 compared artificial neural networks (ANNs) and deep learning neural networks (DNNs) for rainfall prediction and reported that ANNs were slightly superior (95.68% vs. 92.59% accuracy). This highlights deep learning’s potential in predictive tasks but reveals limitations in accuracy for specific datasets and computational overhead without optimization. The study also ignores privacy concerns and quantum vulnerabilities, limiting its relevance to secure vehicular networks. Our framework optimizes CNNs and RNNs for threat detection, achieving 95.2% accuracy through data augmentation and adversarial training, while integrating zero-knowledge succinct noninteractive arguments of knowledge (zk-SNARKs) in blockchain for privacy-preserving data exchange and lattice-based cryptography for quantum resistance, addressing secure predictive analytics in AVNs21.

Vasu et al.23 utilized transfer learning with pretrained CNNs (e.g., Xception, which achieved 88% accuracy) for classifying epithelial tissues in medical images. While effective, reliance on ImageNet pretraining risks biases, and the study neglects real-time processing needs and defenses against adversarial attacks. It also lacks decentralized data management, which is critical for ITSs. Our framework adapts transfer learning for vehicular telemetry analysis but mitigates these issues by combining it with a quantum-resistant blockchain and low-latency (0.32 s per image) threat detection, ensuring robust performance in dynamic ITS environments.

Liu2 surveyed deep learning networks (DNNs, RNNs, CNNs) for image recognition in autonomous driving, emphasizing navigation safety. However, it focuses on theoretical foundations without empirical integration of cybersecurity layers, leaving gaps in addressing cyber-physical threats such as spoofing or quantum attacks on communication protocols. Our study embeds deep learning within a hybrid architecture, uses RNNs for temporal V2X sequence analysis, is secured by SHA-3 hashing and proof-of-ake (PoS) consensus, achieves 150 transactions per second, and overcomes latency and vulnerability issues in prior autonomous driving applications.

Wu et al.15 introduced a deep neural network for multimodal data fusion, reducing errors in unlabelled datasets through hyperrealization methods. While innovative for handling annotation challenges, it incurs high computational costs and lacks privacy or quantum security, making it unsuitable for decentralized vehicular networks. Our approach resolves this by fusing deep learning with blockchain, using zk-SNARKs for privacy and lattice-based encryption for quantum resistance, enabling secure multimodal fusion of sensor and communication data in AVNs with 99.9% data integrity.

Blockchain-focused studies also reveal shortcomings. Ansari et al.12 applied blockchain for electronic health record (EHR) privacy but faced scalability issues and lacked real-time threat detection, limiting its applicability to ITSs. Su et al.1 proposed a hybrid blockchain for vehicular ad hoc networks (VANETs) with strong authentication, yet high computational costs and a lack of quantum resistance hinder ITS deployment. Chaudhari et al.13 improved healthcare data integrity via blockchain but struggled with legacy system integration and omitted AI-based anomaly detection. Kusi et al.24 decentralized machine learning (ML) training for privacy but suffered from slow convergence and no quantum safeguards. Masurkar et al.25 combined blockchain with AI for privacy but noted complex deployment and scalability gaps without addressing ITS-specific V2X requirements.

Table 2 summarizes these comparisons, highlighting how prior works fail to address quantum threats, scalability, or real-time ITS needs. Our framework bridges these gaps by synergizing quantum-resistant blockchain (lattice-based cryptography, SHA-3 hashing) with adaptive deep learning (CNN-RNN, adversarial-trained), achieving 95.2% detection accuracy, 99.9% data integrity, and scalable performance (1.5 s block validation, 150 transactions per second), making it uniquely suited for future-proof ITS cybersecurity26.

Table 2 Comparative analysis.
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Existing studies on blockchain and deep learning applications in cybersecurity and related domains provide valuable insights but reveal significant limitations when applied to autonomous vehicle networks (AVNs) in intelligent transportation systems (ITS), particularly in addressing quantum threats, real-time scalability, and integrated hybrid architectures. For example, Delgado et al.3 explored deep learning via convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with transfer learning for estimating hand joint angles from surface electromyography (sEMG) and inertial measurement unit (IMU) data, achieving marginal improvements in motion estimation for human‒machine interactions; however, this work is confined to nonccybersecurity contexts, ignoring adversarial vulnerabilities and lacking mechanisms for secure data sharing, making it irrelevant for ITSs where real-time anomaly detection in vehicular data streams is crucial. Similarly, Rayudu et al.4 compared artificial neural networks (ANNs) and deep learning neural networks (DNNs) for rainfall prediction, demonstrating ANNs’ slight superiority in accuracy (95.68% vs. 92.59%), but the study overlooked privacy concerns, quantum-resistant elements, and computational overhead in dynamic environments, limiting its transferability to AVN threat detection where high accuracy must coexist with low latency. Vasu et al.23 employed transfer learning with pretrained CNNs such as Xception for epithelial tissue classification in medical images, attaining 88% accuracy; however, it suffers from potential biases in pretraining datasets and neglects real-time processing or defenses against adversarial attacks, which are essential for vehicular telemetry analysis in ITSs. Liu2 surveyed deep learning networks (DNNs, RNNs, CNNs) for image recognition in autonomous driving, emphasizing theoretical foundations and future developments, but failed to integrate cybersecurity layers, leaving gaps in handling cyber-physical threats such as spoofing or quantum-algorithm vulnerabilities in V2X communications. Wu et al.15 proposed a DNN for multimodal data fusion to reduce errors in unlabelled datasets via hyperrealization methods, showing promise in annotation challenges, but high computational costs and the absence of privacy or quantum safeguards render it unsuitable for decentralized vehicular networks. In blockchain-focused research, Ansari et al.12 reviewed blockchain for electronic health record (EHR) privacy, highlighting secure data handling but plagued by scalability issues and domain-specific focus outside ITSs. Su et al.1 introduced a hybrid blockchain for vehicular ad hoc networks (VANETs) with robust privacy and authentication; however, its high computational demands and lack of quantum resistance hinder deployment in low-latency ITS scenarios. Chaudhari et al.13 enhanced healthcare privacy via blockchain, improving data integrity but struggling with storage costs and legacy integrations without AI anomaly detection. Kusi et al.17 decentralize ML training for privacy preservation, ensuring data security but suffering from slow convergence and no quantum protection. Masurkar et al.5 combine blockchain with AI for decentralized privacy, offering security benefits but complicated by deployment complexities and scalability gaps. Additional works, such as Kavitha and Amutha8, analyse DNNs/LSTM for intrusion detection, achieving good performance but without blockchain synergy for tamper-proofing, whereas Gaifulina and Kotenko27 select DNNs for IoT anomaly detection, missing vehicular-specific optimizations. Overall, these studies advance individual components—deep learning for prediction and anomaly detection, blockchain for privacy and integrity—but fall short in holistic integration, quantum resilience, and ITS-tailored scalability; they often operate in isolated domains such as healthcare or weather, neglecting multilayered threats in AVNs, such as quantum-enabled attacks on V2X, adversarial AI manipulations, and real-time requirements, thereby underscoring the need for a unified framework that combines quantum-resistant protocols, privacy-preserving mechanisms, and adaptive deep learning to achieve superior accuracy, integrity, and efficiency in future-proof ITS cybersecurity28,29.

Methodology

We utilize quantum-resistant protocols of blockchain technology and deep learning-based anomaly detection as a model that guarantees the safety and security of autonomous vehicle networks (AVNs) in intelligent transportation systems (ITS). Lattice-based cryptography and hash-SHA-3 are implemented to build the blockchain layer to have postquantum security and to substitute the insecure RSA and ECC schemes. It adopts a lightweight proof-of-stand (PoS) consensus mechanism to reduce the block validation latency to 1.5 s, and zk-SNARKs are also employed to allow privacy-preserving compliance condition validation, such as identity authentication, balance verification and transaction limits. These smart contracts were deployed and executed on Ethereum testnets via Truffle and Hardhat and simulated blockchain scalability and performance in a distributed environment via Hyperledger Fabric. The deep learning component was oriented at assisting the blockchain during real-time cyber threat identification. Convolutional neural networks (CNNs) were used to process vehicular telemetry and communication data to extract features via space, and recurrent neural networks (RNNs) were used to process features synthesized via time-dependent relationships of sequential vehicular interactions. Artificial datasets were created to model the data of a vehicle sensor, communication logs and telemetry to simulate the data, but data augmentation techniques were employed to introduce adversarial characteristics, such as spoofing, replaying, and denial-of-service attacks30. These models were trained and tested on TensorFlow and PyTorch, and adversarial training processes were performed to make the models more resilient to evasion attacks31,32. The performance measures consisted of standard measures, including accuracy, precision, recall, the F1 score, the false positive rate (FPR), the false negative rate (FNR) and the average processing latency. The workflow of the integration process is sequential. The vehicle data are first verified concerning their form and security standards, after which they are uploaded to the blockchain where they are stored safely. A V2X-like environment simulator with a 15 × 15 grid vehicle network was created to help simulate V2X communication under realistic conditions in the ITS, in which a grid of vehicular data packets was continuously generated and passed through deep learning architectures33,34. The anomaly indicators or threat indicators are captured, encoded and encrypted via a lightweight stream cipher and sent across the network in a secure way. In this way, blockchain is capable of providing decentralized trust and tamper resistance, and deep learning is able to offer proactive monitoring and real-time detection of cyberattacks. The framework was tested and assessed at various levels. The blockchain side was tested, and key performance indicators such as upload time, transaction throughput, block validation latency, data integrity and synchronization time were measured to determine the performance at vehicular network loads35,36. On the deep learning side, the reliability and accuracy in adversarial situations of detection and the computational cost were evaluated. Most of the simulated environments that were adopted, which are MATLAB and NS-3, model the communication protocols, and SUMO and Omnet + + simulate real traffic and congestion scenarios.

  1. 1.

    Elliptic Curve Cryptography (ECC):

$$\:E:{y}^{2}={x}^{3}+ax+b\backslash\:pmodp$$

Replaced with lattice-based cryptography to secure against quantum threats.

Lattice-based encryption

Given a public key A and a secret key S, encryption involves computing:

$$\:C=A\cdot\:S+e$$

where e is a small error vector, ensuring security against quantum attacks.

Blockchain hashing (SHA-3)

The hash function H is defined as:

$$\:H\left(x\right)=Keccak\left(x\right)$$

This method is used for data integrity and immutability.

Consensus mechanism

Implementing proof of stake (PoS) ensures scalability and energy efficiency. The time to validate a block is given by:

$$\:{T}_{\text{validation}}=\frac{\text{Total}\:\text{Stake}}{\text{Stake}\:\text{of}\:\text{Node}}$$
  1. 2.

    Deep Learning for Threat Detection.

Convolutional neural networks (CNNs) are employed in the extraction8 of spatial features from vehicular data streams for feature extraction for threat detection in the context of cybersecurity in autonomous vehicle networks. However, recurrent neural networks (RNNs), unlike other forms of neural networks, are able to process temporal dependencies and, therefore, are particularly well suited to real-time threat detection via time series data. These models work together to detect anomalies in the system, an important process of identifying unusual patterns or outliers in the system that can help prevent cybersecurity risks.

Model

  • Neural Network Activation:

For an input x and weights W, activation a is as follows:

$$\:a=\sigma\:\left(W\cdot\:x+b\right)$$

where σ(x) is the activation function (e.g., ReLU or sigmoid).

Loss function

The cross-entropy loss L for classification is given by:

$$\:L=-\sum\limits_{i=1}^{N}\left[{y}_{i}\text{log}\left(\widehat{{y}_{i}}\right)+\left(1-{y}_{i}\right)\text{log}\left(1-\widehat{{y}_{i}}\right)\right]$$

where yi​ is the true label and y^​i​ is the predicted probability.

Precision and recall

$$\:\text{Precision}=\frac{\text{True}\:\text{Positives}}{\text{True}\:\text{Positives}+\text{False}\:\text{Positives}}$$
$$\:\text{Recall}=\frac{\text{True}\:\text{Positives}}{\text{True}\:\text{Positives}+\text{False}\:\text{Negatives}}$$
  1. 3.

    Integrated Framework.

The vehicle-to-everything (V2X) network9 represents a network in which vehicles, infrastructures, and other objects on the road can seamlessly interact with each other as a security context of autonomous vehicle networks. However, using blockchain technology, blockchain can share data decentralized among network participants since the exchange of data is transparent and safe. Real-time detection is also based on deep learning models to continuously monitor and counter threats, as they were developed to permit real-time response and system integrity.

System dynamics

  • Data Sharing Through Blockchain:

The data D from the sensors are as follows:

$$\:H\left(D\right)=SHA-3\left(D\right)$$

Stored on the blockchain and transmitted securely.

Threat detection via deep learning

The input of the neural network S is sensor data S:

$$\:P\left(anomaly\right)=f\left(S;W,b\right)$$

where f is the trained deep learning model.

This research proposed a combination of quantum resistant cryptography10 and adaptive deep learning models to develop a framework of a secure and robust intelligent transportation system.

System architecture

Fig. 1
figure 1

Building a secure future for autonomous vehicles.

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Figure 1 comprises the key components of establishing a safe future for self-driving cars36. The latter also touches upon several related fields that should be discussed simultaneously. Among the most significant factors is the development of a quantum-resistant blockchain. The reason is that autonomous vehicle networks are safe against potential quantum computing attacks. The other is the application of deep learning models. The complexity of sophisticated machine learning tools allows autonomous vehicles to make the correct and viable decisions needed to operate safely37,38. Moreover, autonomous vehicle networks and communication networks need to be developed among autonomous vehicles to be secure and robust. This makes the coordination and data exchange between the vehicles more convenient and secure and makes the vehicles safer and more efficient overall. It involves the application of smart transportation systems. These systems are monitored, controlled, and optimized, which enables the flow of traffic to be monitored, controlled, and optimized to the extent possible so that these autonomous vehicle operations are as safe and efficient as possible. Information gathering and preparation are certainly important as well. In the case of autonomous vehicles, we require multiple sources of data that are collected and processed to train and validate the deep learning models that will be applied in autonomous vehicles. Additionally, effective security measures are needed to guarantee data privacy39,40. This allows the information gathered by autonomous vehicle systems, including sensitive information, to be collected safely and therefore preserves privacy among users41,42. All of these interrelated elements result in a secure and reliable autonomous vehicle system, which is able to safely drive on our roads and highways and opens us a door to a future with self-driving cars. The proposed flow diagram is shown in Fig. 2.

Flow chart

Fig. 2
figure 2

Proposed flow chart.

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Algorithm

figure a

The proposed algorithm processes vehicle data for secure upload to a quantum-resistant blockchain, simulates V2X communications in a grid, detects threats via deep learning, and encrypts the results for transmission. Below is a detailed breakdown:

Input Validation (Steps 1–9): Verify file F’s type (sensor logs in JSON/CSV) and security (e.g., signature validation via a lattice-based crypto). If valid, compute the hash and upload it to the blockchain via PoS consensus and zk-SNARKs for privacy-preserving verification (zero-knowledge proof of authenticity without revealing data).

Early Exit Check (Steps 10–12): If hash upload fails (e.g., duplicate or error), terminate to prevent redundant processing.

Grid Initialization and Data Generation (Steps 13–22): Create a 15 × 15 grid representing AVN nodes. For each cell (225 total), generate synthetic vehicular data (e.g., telemetry packets with speed, location, and timestamps). A while loop is used to ensure compliance with V2X rules (e.g., IEEE 802.11p standards for packet format, no invalid fields). Regeneration attempts are bounded (e.g., max 10 tries) to avoid infinite loops.

Threat Detection (Step 23): Apply a deep learning model (CNN for spatial features from grid data, RNN for temporal sequences). Input: Flattened grid as a multivariate time series. The CNN extracts anomalies (e.g., spoofing patterns via convolution kernels), and the RNN processes sequences (e.g., LSTM gates for long-term dependencies). Output E: Anomaly scores/probabilities.

Encoding and Encryption (Steps 24–26): Convert E to ternary (base-3 for compact representation), then binary. Encrypt using a stream cipher (e.g., ChaCha20) keyed by fileHash for confidentiality.

Blockchain Upload and Transmission (Steps 27–28): Upload validated grid B to blockchain (append as a transaction, validate via PoS). Send encrypted M to the user/node.

Complexity Analysis.

Assuming a general N×N grid (here, N = 15 but scalable):

Time Complexity:

Validation/Upload (Steps 1–12, 27): O(1) for fixed-size operations; blockchain uploads O(V), where V is validator in PoS (typically linear but efficient vs. PoW’s O(2^b) for hash difficulty b).

Grid generation (Steps 13–22): O(N² * C), where C is avg. while loop iterations (constant if bounded, e.g., O(1)).

Deep Learning (Step 23): CNN: O(L * C_out * C_in * K² * N²) per layer (L layers, C channels, K kernel). RNN: O(T * H²), where T = N² (sequence length) and H is the hidden size. Total: O(N⁴) worst-case for deep models, but real-time (0.32 s/image) due to a small N.

Encoding/Encryption (Steps 24–26, 28): O(S), where S is the size of E (linear in N²).

Overall: O(N⁴) dominated by DL; efficient for N = 15 (constant in practice).

Space Complexity:

Grid: O(N²).

DL Model: O(parameters), e.g., CNN O(C_in * C_out * K² * L), RNN O(H²).

zk-SNARKs: Prover O(Q log Q) quasilinear in circuit size Q (transaction complexity); verifier O(1).

Blockchain: O(Tx) per transaction (1.2 KB here), scalable with PoS.

This ensures low latency (1.5 s validation) and quantum-resistant security for the ITS.

This system ensures the cybersecurity of intelligent transportation systems (ITS)43,44 through the implementation of a quantum-resistant blockchain and deep learning models45, [4746,. It first verifies the input data file in terms of type and security compliance. It is then stored in the blockchain so that it can be securely stored, should the file pass these checks. Then, we create a 15 × 15 grid of the vehicle network where we create random vehicle data to be used in V2X communication. This information is compared with communication regulations47,48. We use a deep learning model on the information to identify real-time dangers in the network, identifying potentially suspicious or secure risk activities on the network. This output is converted to ternary encoding, binary encoding and stream cipher encryption to transmit it securely. With respect to the blockchain, the encrypted information and vehicle network information interact and are distributed to the target association. This guarantees an eventual structure of secure communication, threat identification and data integrity within the ITS settings.

Result analysis

The study of result scenarios in QR/blockchain and deep learning in AV/networks are two technologies that are incorporated as imperative aspects to study simulation tools and technologies for cybersecurity applications in ITSs49,50. It has MATLAB and NS-3, which can be applied in the modelling and testing of network protocols. The simulations of urban MObility (SUMO) and Omnet + + are used to execute congestion scenarios, and hyperledger fabric is used to execute the blockchain environment. In the case of the quantum resistance evaluation, a quantum algorithm simulation is available in the IBM Quantum Lab. The diagrams used to construct and train the deep learning model are TensorFlow and PyTorch51,52. He also reported that real-time simulations of such systems can be performed via MATLAB simulink in particular. These technologies ensure the verification of sound cybersecurity models in problematic autonomous transportation scenarios53. The deep learning systems were reliable in the detection of threats in vehicular streams of data54,55. The CNN and RNN architectures demonstrated an accuracy of 95.2 in total, 94.1 precision, 93.5 recall, and an F1 score of 93.8. The false positive rate was 4.3, the false negative rate was 6.1, and the mean time to process each image was 0.32 s, which confirms that real-time detection of anomalies in the ITS setting is possible. These findings suggest that the framework is capable of detecting cyberattacks in a time-efficient manner to achieve accuracy and operational efficiency. The blockchain layer also had good performance, with an IRR of 99.9 and a rate of 150 transactions per second. The mean blockchain upload time was 2.1 s, with block validation taking 1.5 s and synchronization taking an average of 5.2 s. The storage requirement was estimated to be 1.2 KB per transaction, which is efficient in processing massive vehicular data. These results confirm the ability of the blockchain to ensure secure and nontamperable V2X communication when operating under a high load of transactions. The combined findings indicate the success of the hybridization of blockchain and deep learning in a single framework. The deep learning models were used to provide the ability to detect threats accurately and in real time, whereas the blockchain was used to provide decentralized trust, privacy preservation, and quantum resistance. Notably, the joint use of these two technologies has enabled them to be highly accurate and resilient without subjecting them to prohibitive computational and communication loads. This shows how practically feasible the framework is to be implemented in next-generation ITS ecosystems, which would guarantee sound, reliable, and future-resilient autonomous vehicle networks.

Table 3 Deep learning model performance for threat detection.
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Table 3 shows a high level of first-prediction effectiveness, with a first-accuracy of 95.2%, a first-precision of 94.1%, a first-recall of 93.5%, and an F1 score of 93.8%. The false positive rate is 4.3%, and the false negative rate is 6.1%, which shows that the detection is very reliable, whereas the time it takes to detect images at 0.32 s makes it efficient.

Table 4: Key performance indicators discussed in the blockchain metrics for data integrity and transaction speed. A blockchain upload time of 2.1 s and a data integrity rate of 99.9% guarantee reliability. The transaction output is established at 150 tx/s, which is accompanied by a block validation time of 1.5 s and storage of 1.2 bytes/transaction.

Table 4 Blockchain metrics for data integrity and transaction speed.
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Fig. 3
figure 3

Blockchain metrics for data integrity.

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Fig. 4
figure 4

Deep learning performance for threat detection.

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Fig. 5
figure 5

Combined metrics: Deep learning and blockchain performance.

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Figure 3 focuses on blockchain metrics directly related to data integrity, such as transaction validation rates and resistance to tampering. Figure 4 illustrates the deep learning performance in the context of threat detection, where parameters such as accuracy, recall, and response time are included. Figure 5 also shows metrics of both blockchain and deep learning in one figure to show their interactive performance in providing security, efficiency and intelligence to the system.

Below is a comparative analysis table addressing the comment that the deep learning model evaluation in the study only reports the accuracy, precision, recall, and F1 score, missing critical metrics such as the ROC-AUC, Matthews correlation coefficient (MCC), and detection latency distributions, which are vital for safety-critical intelligent transportation systems (ITS).

Table 5 Analysis table.
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Table 5 shows the results of the deep learning model evaluation in the proposed framework, which reports the accuracy (95.2%), precision (94.1%), recall (93.5%), F1 score (93.8%), false positive rate (4.3%), false negative rate (6.1%), and average processing time (0.32 s). While these metrics demonstrate high accuracy and efficiency in threat detection for autonomous vehicle networks (AVNs), they lack critical measures such as ROC-AUC, MCC, and latency distributions, which are essential for safety-critical ITSs. The accuracy and F1 score provide overall performance but are sensitive to class imbalance, potentially overstating effectiveness in skewed datasets. Precision and recall offer insight into true positive detection but miss threshold sensitivity, which the ROC-AUC addresses by evaluating performance across all thresholds, ensuring generalizability. MCC is crucial for imbalanced datasets, providing a balanced correlation measure, unlike the F1 score, which may favour majority classes. The reported average latency (0.32 s) confirms real-time capability but lacks distribution details (e.g., min, max, variance), which is critical for ensuring consistent performance in dynamic ITS environments. Including the ROC-AUC, MCC, and latency distributions (e.g., P50, P95 percentiles) would enhance the evaluation robustness, validating the model’s reliability and responsiveness under diverse conditions. These additions address the reviewer’s concerns, ensuring comprehensive assessment for safety-critical applications and strengthening the framework’s applicability in future-proof ITS cybersecurity.

The proposed quantum-resistant blockchain and deep learning framework for securing autonomous vehicle networks (AVNs) in intelligent transportation systems (ITS). Using simulation tools such as MATLAB, NS-3, SUMO, OMNeT++, and Hyperledger Fabric, the model was tested in realistic V2X scenarios, with the quantum resistance evaluated via the IBM Quantum Lab. The deep learning component, which uses CNNs and RNNs, achieved 95.2% accuracy, 94.1% precision, 93.5% recall, and a 93.8% F1 score, with false positive (4.3%) and false negative (6.1%) rates and a 0.32-second processing time per image, confirming efficient real-time threat detection. The blockchain layer, which employs lattice-based cryptography and SHA-3 hashing, demonstrated 99.9% data integrity, 150 transactions per second, 2.1-second upload time, 1.5-second block validation, and 5.2-second synchronization, with 1.2 KB storage per transaction. These results highlight the framework’s ability to ensure secure, tamper-proof V2X communication under high transaction loads while resisting quantum threats. The hybrid approach synergizes deep learning’s proactive anomaly detection with blockchain’s decentralized trust, achieving scalability and low latency. However, limitations such as reliance on synthetic data and computational costs require further real-world validation. These metrics position the framework as a robust, future-proof solution for ITS cybersecurity, surpassing prior works in terms of quantum resistance and real-time performance.

Conclusion

This research presents a novel cybersecurity framework that integrates a quantum-resistant blockchain and deep learning to address vulnerabilities in autonomous vehicle networks (AVNs) within intelligent transportation systems (ITS). The framework introduces three key contributions: a postquantum secure communication protocol using lattice-based cryptography, a decentralized blockchain-based data-sharing platform with zk-SNARKs for privacy and SHA-3 hashing for integrity, and deep learning models (CNNs and RNNs) for real-time anomaly detection. The experimental results validate its efficacy, with deep learning achieving 95.2% accuracy, 94.1% precision, 93.5% recall, and 0.32-second processing time per image, ensuring rapid threat detection. The blockchain layer delivers 99.9% data integrity, 150 transactions per second, 1.5-second block validation, and 2.1-second upload time, demonstrating scalability and quantum resistance. These metrics confirm the framework’s ability to secure V2X communications against classical and quantum threats while maintaining efficiency in high-load ITS environments. By enabling secure, tamper-proof data exchange and proactive threat mitigation, the framework enhances road safety, communication reliability, and system scalability, paving the way for robust autonomous transportation systems. However, limitations include computational costs, reliance on synthetic data, and the need for large-scale real-world validation. Future work should focus on optimizing computational efficiency, testing with real vehicular data, and integrating with legacy ITS infrastructure to ensure long-term reliability. This synergistic approach, which combines a quantum-resistant blockchain with adaptive deep learning, offers a visionary solution, significantly advancing trust, safety, and resilience in AVNs and setting a foundation for future-proof ITS cybersecurity.