As the demand for secure image transmission continues to rise, encryption techniques based on chaotic systems have emerged as a vital component in the field of information security. To meet this demand and enhance transmission security, we propose a novel encryption scheme that significantly improves the security of images during transmission. In this work, a novel Sinusoidal-Quadratic Map Coupled Map Lattices (SQMCML) model is proposed, incorporating a non-adjacent coupling mechanism guided by cellular automata (CA). Besides, we presented the Sinusoidal-Quadratic Map (SQM) to enhance the system’s dynamics behavior. The relevant comprehensive dynamics analysis indicates that all lattices of SQMCML are in a chaotic state. Based on the SQMCML system, a new plaintext-related image encryption scheme is proposed, featuring a random-trajectory Josephus permutation method. Firstly, in order to improve the scheme’s capability to withstand differential attacks, a plaintext-related method for generating the chaotic initial values of SQMCML is proposed, which significantly enhances the plaintext sensitivity of the image cryptosystem. Subsequently, we presented the random-trajectory Josephus permutation method, in which chaotic sequences are utilized to dynamically determine the starting points and step sizes of each traversal round, enhancing both the randomness and security of the permutation process. Additionally, the diffusion operation is carried out by leveraging the iterative dynamics of 2D-CA model. The results of comprehensive simulations confirm the high security strength of the proposed approach, demonstrating its applicability to the secure transmission of image data. Comprehensive simulations validate the high security of the proposed scheme: chi-square test values significantly exceed the 0.01 threshold, NPCR and UACI meet theoretical expectations, and global Shannon entropy exceeds 7.999 while local Shannon entropy conforms to standard benchmarks—confirming its suitability for secure image transmission.

Internet of Things (IoT) systems face escalating security threats as the number of connected devices surpassed 18.5 billion in 2024 and is projected to reach 39 billion by 2030. The dynamic and heterogeneous characteristics of IoT networks create vulnerabilities to sophisticated attacks. Existing trust management approaches struggle with these threats due to static graph construction, feature redundancy between direct and indirect trust, and inflexible fusion strategies. This paper proposes a Dual-Trust graph attention network-based malicious device detection method for IoT environments, named DTEM. The proposed scheme constructs dynamic temporal graphs based on real interaction history. Then it designs a completely decoupled dual-trust learning architecture. Finally it introduces a hybrid fusion mechanism. Overall, the improvements significantly enhance the detection accuracy of malicious devices in complex environments and adaptability to heterogeneous scenarios. Experimental results on the UNSW-NB15 dataset demonstrate that DTEM achieves an average F1-Score of 0.973 and ROC-AUC of 0.994 across three threat scenarios, outperforming traditional methods.

Image encryption technology based on chaotic systems is an important guarantee for the secure transmission of information. In this paper, a novel cellular automata controlled coupled map lattices (CACCML) is proposed, in which the coupling structure is dynamically controlled by cellular automata to enhance structural complexity, and two enhanced one-dimensional maps are presented to improve the dynamics performance of the system. The dynamics analysis reveals that all lattices of the system exhibit chaotic behavior within the given parameter range, demonstrating its strong potential for cryptographic applications. Based on the proposed CACCML system and two-dimensional cellular automata, an novel image encryption scheme is presented. First, the plain image information and the initial security keys are jointly computed to generate chaotic initial values, ensuring strong sensitivity to plaintext image. Next, a dual-state transformation permutation method is introduced, which leverages the iterative evolution of the two-dimensional Von Neumann neighborhood structure cellular automata (2D-VNCA) to indirectly scramble pixel positions. Subsequently, an iterative HL-Bitplane diffusion method is proposed, cleverly integrating the separated high-four-bit and low-four-bit planes into the iterative process of the two-dimensional reversible-like cellular automata (2D-RLCA), thereby achieving an efficient diffusion operation. The results from comprehensive security analyses and comparative evaluations demonstrate that our proposed image encryption scheme possesses exceptional security strength and robustness.

Studying the collaborative effects among different functional brain regions helps reveal the working mechanisms of electrical activity in the nervous system. However, existing studies on memristive Hopfield neural networks mainly focus on single-network structures or general coupling forms, while the information exchange mechanisms between different subnetworks and their effects on controllable multiscroll dynamics remain insufficiently explored. Therefore, this paper proposes a new multi-segment linear memristor to simulate a neural synapse and constructs a dual neural network heterogeneous coupled complex multiscroll system (DNNHCMS) by connecting two asymmetric sub-neural networks through synaptic coupling. Numerical results reveal that the coupling strength between the subnetworks determines the dynamical characteristics of the DNNHCMS. Within specific parameter ranges, the system can generate multiple double-scroll attractors, the number of which can be controlled by adjusting the segment parameter of the memristor. Meanwhile, the system exhibits significant extreme multistability and flexible amplitude control, with circuit simulations and hardware implementations further validating the reliability of the numerical analysis. Finally, based on the controllable multiscroll and extreme multistability characteristics of the DNNHCMS, this paper constructs 10 plaintext-related dynamic S-boxes using the highly complex chaotic sequences generated by the system, and proposes a new image encryption scheme. In each round of permutation and diffusion, chaotic sequences are used to dynamically select S-boxes for substitution operations, thereby reducing redundant computations and significantly improving the security and efficiency of the encryption process. Security analysis demonstrates that the proposed scheme possesses robust security performance.

A novel sine-cosine-optimized coupled map lattices (SCOCML) is proposed, which integrates the cellular automata (CA)-guided non-adjacent coupling method and is constructed upon the sine-cosine-optimized (SCO) map. Within the given parameter range, dynamics analysis shows that every lattice point in the proposed SCOCML system sustains a chaotic state, thereby exhibiting enhanced chaotic features. Based on the SCOCML system, a novel image encryption scheme is presented. It starts with generating a plaintext-related initial key, ensuring high sensitivity to plain images. Additionally, a pseudo-random number generator (PRNG) based on the dynamic gas cellular automata (DGCA) model is proposed to further improve the randomness of chaotic sequences, enabling the image cryptosystem to achieve robust security performance. Subsequently, a CA-controlled synchronous permutation–diffusion method is proposed, which utilizes the evolving states of cellular automata to dynamically perform row and column encryption, achieving an efficient and coordinated encryption effect in merely three rounds. Extensive experimental results and security analyses confirm that the proposed image cryptosystem offers excellent resistance against various attacks, achieving information entropy > 7.999, NPCR > 99.60, and UACI ≈ 33.45, making it suitable for practical secure image communication applications.

Image encryption using chaotic maps is an important approach for securing digital images in the era of widespread Internet communication. We propose a novel discrete chaotic system, the two-dimensional Arcsin-Cubic-Sine-Exp System (2D-ACSES), which exhibits hyperchaotic dynamics, making it suitable for cryptographic applications. Based on the 2D-ACSES system, we introduce a new encryption scheme using bit-level folding permutation, improving the permutation efficiency. The process begins with an internal key generation method, transforming both the plaintext image and the initial key into internal keys, which initialize the system. The image matrix is reshaped into a bit matrix, which undergoes several transformation stages, including folding, row shifting, and refolding to increase confusion. Finally, the modified bit matrix is restored to the image matrix, which is further encrypted through row diffusion and pixel-level permutation. These steps enhance the diffusion of information, ensuring that the ciphertext is highly sensitive to both the plaintext and the initial keys, thus providing robust security. Simulation results and analysis demonstrate that the proposed encryption scheme provides strong security performance.

In neural networks, complex topological structures often lead to diverse dynamic behaviors. Therefore, a non-volatile locally active memristor is proposed, and a four-neuron Memristive Recurrent topology Hopfield Neural Network with Dual Self-Loops (MRHNN-DSL) is constructed by simulating the external electromagnetic radiation effect through the memristor. Numerical simulations reveal that the MRHNN-DSL exhibits a variety of symmetric coexisting attractors–such as chaotic, quasi-periodic, periodic, and fixed-point behaviors–within the parameter space. Further analysis indicates that when the memristive parameter falls within the locally active region, the MRHNN-DSL tends to converge, whereas in the non-locally active region, it predominantly exhibits divergent dynamics. In addition, the introduction of multi-level logic pulse current can significantly change the dynamic behavior of MRHNN-DSL, not only inducing multi-scroll attractors, but also the number of scrolls has an exponential relationship with the pulse level. The correctness and effectiveness of the theoretical model are validated through circuit experiments. Furthermore, based on the multistability of MRHNN-DSL, we propose a novel medical image encryption scheme that utilizes coordinate pair indices composed of self-orthogonal Latin squares and their transposed matrices to achieve simultaneous permutation and diffusion within the Galois field. This scheme reduces redundant operations and effectively enhances encryption speed. Meanwhile, the encryption scheme introduces an internal initial value mapping method that adaptively selects chaotic symmetric attractors from the MRHNN-DSL, substantially improving the randomness of the generated chaotic sequences. Security analysis shows that the medical image encryption scheme performs excellently in multiple key security performance indicators.

Chaos-based image encryption systems are essential for securing sensitive digital content. To address existing limitations and enhance security, we first present a new discrete model called the 2D Sinusoidal-Logarithmic-Exponential Modulation System (2D-SLEMS). Analysis of its dynamics reveals that for values of θ∈ (0, 1) , the system demonstrates a hyperchaotic characteristic, where the phase-plane trajectories are highly irregular and densely fill the space. This dynamic behavior makes the system a promising candidate for cryptographic purposes. Next, we propose a novel color image encryption scheme based on the 2D-SLEMS, incorporating a novel non-equilibrium dynamic S-box substitution method. In this scheme, we initially propose a method that links the plaintext to generate the system’s parameters and initial conditions for the 2D-SLEMS, ensuring that the encryption process exhibits a high level of sensitivity to the plaintext through the iterative control of the system. The scheme constructs four types of S-boxes of varying sizes: 6-bit, 8-bit, 10-bit, and 12-bit. The plaintext image is transformed into a bit matrix corresponding to the red, green, and blue (RGB) channels, where each row contains a 24-bit value. A column permutation is applied to the bit matrix, followed by the division of each 24-bit row into 6-bit, 8-bit, and 10-bit groups, with each group being substituted using its corresponding S-box. Subsequently, the 24-bit data is grouped into two 12-bit blocks and substituted using a 12-bit S-box. Finally, a pixel-level permutation and diffusion process is applied to generate the encrypted ciphertext image. The proposed image encryption scheme offers strong security. With a key space of 2^499 , the system shows excellent resistance to statistical analysis, as indicated by near-zero correlation coefficients. The NPCR and UACI values of 99.6006

In the digital age, chaotic map-based image encryption techniques play a crucial role in safeguarding multimedia content during transmission. To address performance and security challenges, we propose a new discrete chaotic system called the two-dimensional exponential-Tangent-Cosine System (2D-ETCS). Comprehensive dynamic analysis reveals that the system exhibits hyperchaos within the parameter range θ∈ (0, 1) , enabling its trajectories to cover the entire phase plane, making it an excellent candidate for cryptographic applications. Building on the 2D-ETCS system, we present a novel color image encryption scheme using an innovation cross permutation method. The process begins with a plaintext-related method that maps the parameters and initial values of the 2D-ETCS system, effectively increasing the system’s sensitivity to the plaintext. We then propose a cross-permutation method, which extracts pixels from the three color components under the control of chaotic sequences and reshuffles them to disrupt the pixel arrangement. After six rounds of cross-permutation, rotation, and mask operations, the final ciphertext image is generated. Security analysis and comparison confirm that the proposed encryption scheme offers robust security, making it well-suited for protecting image data in open-network communication tasks.

Internet of Vehicles (IoV) has become the key technology to improve road safety and traffic efficiency. However, with the explosion of the number of vehicles and more frequent authentication, computing and communication costs increase. Nevertheless, most of the traditional IoV authentication protocols lack scalability and are vulnerable to physical attacks and internal attacks, so they are difficult to meet the needs of modern IoV environment. To address these issues, this article proposes a hierarchical mutual authentication protocol for the IoV based on physical unclonable function (PUF) and fog computing. In this protocol, we design a three-layer architecture for IoV supported by fog computing, where the fog node (FN) acts as an intermediate authentication layer, managing a group of roadside units (RSUs) and sharing the computational tasks of the trusted authority (TA) to alleviate its burden. Moreover, PUFs are embedded in entity devices to encrypt sensitive parameters, preventing internal data leakage. Based on this architecture, our protocol implements both vehicle-to-infrastructure (V2I) authentication and group authentication. In the V2I authentication, the FN distributes session keys in bulk to a group of RSUs and multiple vehicles, significantly reducing the repetitive authentication overhead between vehicles and RSUs. In the group authentication, the RSU authenticates vehicles and distributes group key, thereby avoiding the need for frequent authentication. We have also implemented fast updates of session keys and group keys, independently completed by the FN and RSU, reducing reliance on the TA and enhancing key security. We have conducted both formal and informal security analyzes of the proposed protocol and used the ProVerif tool to verify its security. The results demonstrate that the protocol meets the security requirements needed for IoV. The evaluation results shows that the proposed protocol can significantly reduce the computation and communication overhead, and improve the overall performance of the system.

In today's digital era, protecting multimedia content during transmission is crucial, and chaotic map-based image encryption methods play a vital role. To overcome challenges related to both performance and security, we introduce an innovative discrete chaotic system, the two-dimensional sinusoidal-quadratic infinite collapse system (2D-SQICS). This system demonstrates strong hyperchaotic behavior for theta is an element of (0, 1), with its phase plane trajectories fully covering the space, showcasing its unique potential for image encryption. Building on the 2D-SQICS system, we propose a novel image encryption scheme that incorporates an innovative permutation method, referred to as the random area selected permutation. This method employs a plaintext-related internal key generation strategy, ensuring high sensitivity to variations in the plaintext image. Furthermore, we introduce a random non-overlapping region marking algorithm to select and rearrange pixel values, followed by a diffusion process to generate the ciphertext image. Experimental results and comprehensive security analyses highlight the security and effectiveness of our cryptosystem. Specifically, our scheme demonstrates a large key space, high sensitivity to both plaintext and keys, as well as strong resistance to statistical attacks. It also yields favorable results in information entropy and robustness analyses. The comparison results show that our scheme outperforms existing methods in terms of both security and performance, establishing it as an excellent solution for safeguarding image data in open-network communications.

The study of brain-like network dynamics contributes to understanding biological neural mechanisms and brain functions. In this paper, a multi-asymptotically stable state locally active memristor and a single-parameter regulated locally active region memristor are proposed. A dual-memristor Hopfield neural network (DMHNN) is then constructed by employing these two types of locally active memristors to simulate electromagnetic radiation and memristive autapse weights between neurons, respectively. Numerical analyses demonstrate that DMHNN possesses an infinite number of equilibrium points and is capable of generating atypical dynamic behaviors with hidden attractors. The effects of electromagnetic radiation intensity and autapse coupling strength on the dynamical behaviors of the DMHNN are analyzed, demonstrating that the DMHNN can exhibit multiple complex dynamic characteristics including periodic, quasi-periodic, and chaotic states across parameter space. Furthermore, it is found that the DMHNN has multistability, that is, the coexistence of multiple combinations of attractors (only limit cycles, limit cycles with hidden chaotic attractors, or only hidden chaotic attractors), obtained by changing only the initial conditions. Meanwhile, to verify the theoretical model, an analog circuit is designed using Multisim, and the circuit simulation results closely match the numerical analyses. Finally, an internal initial value mapping scheme is designed, which exploits the multistability of the DMHNN to enable adaptive stochastic selection between two coexisting chaotic attractors, thereby enhancing the unpredictability of chaotic sequences. Based on this scheme, a pseudorandom number generator is subsequently developed, with the NIST SP800-22 test revealing that the generated sequence exhibits excellent randomness and demonstrates significant potential for engineering applications in information encryption.

In brain-like research, investigating the impact of heterogeneous neurons on specific neural network structures is of significant importance. In this paper, a heterogeneous memristive synapse-coupled system (HMSCS) is proposed, which is coupled by Hindmarsh-Rose (HR) and unidirectional cyclic Hopfield neural network (HNN), and is established by presenting a new locally active memristor with multi-stable behavior to simulate the synaptic connection between HR neuron and HNN. The dynamic analysis results show that the locally active region of the memristor can significantly affect the chaos range of HMSCS, and the initial value of the memristor can drive a shift in the phase of the attractor, resulting in extreme multistability. In addition, the numerical analyses are validated by circuit design and simulation. Furthermore, a novel medical image encryption scheme is proposed based on the HMSCS chaotic system, in which an improved Hilbert curve permutation method is given, where the traversal direction and starting point of the Hilbert curve are dynamically selected in each round, thereby effectively enhance permutation performance. Additionally, a region-constrained initial value mapping method is proposed to prevent multistability systems from entering periodic orbits due to improper initial value selection, which is a common problem in existing multistability chaotic systems applied to image encryption. Some common security analyses results show that our proposed encryption scheme has excellent security performance for medical image application scenarios.

By simulating the unique properties of different types of neurons and their interconnecting patterns, heterogeneous neural networks can more accurately reflect the structural and functional characteristics of biological neural systems. Therefore, a novel hyperbolic nonvolatile locally active memristor is proposed, enabling the construction of a heterogeneous Hindmarsh-Rose neuron memristive synapse-coupled Hopfield neural network (HR-M-HNN) via synaptic characteristic emulation. Dynamic analysis reveals that the HR-M-HNN exhibits hidden attractor characteristics due to the absence of equilibrium points and can generate complex dynamics behaviors, including hyperchaos, chaos, periodic and quasi-periodic, and multistability. Further research reveals a strong correlation between the chaotic state of HR-M-HNN and the locally active control parameter of the memristor. To verify the accuracy of the dynamic analysis, the corresponding analog circuit is designed. Furthermore, based on the HR-M-HNN hyperchaotic system, a novel image encryption scheme is proposed, which utilizes the position index of the Latin square to perform synchronous permutation and diffusion on the plain image, reducing redundant operations and effectively enhancing the encryption speed. Security analyses demonstrate that the proposed image encryption scheme offers excellent performance and robustness.

In this paper, a new 2D discrete chaotic system is proposed, which is called two-dimensional closed-loop modulation map with infinite collapse (2D-CLMIC). The dynamic analyses show that the 2D-CLMIC system has a large parameter space for hyper chaos, when the parameter θ =0.01 , the trajectory can occupy all phase plane which is beneficial for application in cryptography. Based on the 2D-CLMIC system, a novel encryption scheme is proposed. In this scheme, we first generate the initial values of 2D-CLMIC by global plaintext related method to ensure the image cryptosystem has enough plaintext sensitivity. Then, a pixel level permutation is done by a control matrix which is generated by the iteration sequence of 2D-CLMIC. After that, random templates which are used for overlapping diffusion are generated by the iteration sequence of 2D-CLMIC. In addition, a templates select control sequences, x-coordinate and y-coordinate select control sequences are generated by two other 2D-CLMIC systems. Finally, do the random overlapping diffusion and output the cipher image. Furthermore, simulation and analysis are given to verify the proposed image encryption system has an acceptable security performance.

Remote sensing images, due to their substantial data volumes and stringent confidentiality requirements, present a significant challenge in the field of image encryption. In this paper, we propose a novel 2D discrete hyperchaotic system named the 2D-Hyperbolic Tangent-Cosecant Coupled Map (2D-HTCCM).ournal instruction requires a city and country for affiliations; however, these are missing in affiliation [1]. Please verify if the provided city and country are correct and amend if necessary. Our nonlinear dynamic analysis demonstrates that 2D-HTCCM possesses exceptional dynamic characteristics, making it highly suitable for cryptosystem design. Building upon this, we developed a new image encryption and compression scheme that leverages the 2D-HTCCM along with a plaintext-related random S-box generation method. The proposed scheme begins by splitting the plain image into 8× 8 blocks, followed by a Discrete Cosine Transform (DCT) on each block. A compression rate-controlled selection is then performed on the DCT coefficients along a zigzag path. Subsequently, we introduce a plaintext-related internal key generation method, which produces 10 sets of system parameters and initial values for the 2D-HTCCMs. These are used to perform permutation and quantization of the compressed DCT coefficients under a control sequence generated by 2D-HTCCM. The subsequent substitution and diffusion stages use randomly generated S-boxes and mask matrices to produce the final cipher image. Comprehensive security analysis and comparisons with existing state-of-the-art techniques reveal that our proposed cryptosystem offers superior security and compression performance, making it particularly well-suited for remote sensing images.

In this paper, we propose an efficient image encryption and compression scheme based on a novel coupled map lattice (CML) system called the improved sine-tangent coupled map lattices (ISTCML) model, along with the Fisher–Yates permutation–diffusion combination algorithm. Nonlinear analysis shows that the ISTCML system has more chaotic lattices than the traditional CML system, making it more suitable for image encryption. Furthermore, based on the ISTCML chaotic system, we propose an image encryption algorithm that incorporates both plaintext relations and the Fisher–Yates permutation–diffusion combinatorial algorithm. This algorithm achieves simultaneous permutation and diffusion, enhancing both the plaintext sensitivity and the efficiency of image encryption. The encryption and compression process involves several steps. Initially, the plain image is correlated with the initial keys to generate internal keys that control the whole process of encryption and compression. Then, the plain image is transformed by discrete wavelet transform (DWT) to create a coefficient matrix. This matrix is then scrambled using the index control sequence generated by the ISTCML system. Subsequently, the coefficient matrix is compressed using the measurement matrix generated by the ISTCML system. Finally, the Fisher–Yates permutation–diffusion combination algorithm is employed to simultaneously achieve permutation and diffusion. Under some common security analyses, our proposed image cryptosystem has strong security and excellent compression performance.

In this paper, we propose a new Sine-Logistic Map Coupled Map Lattices (SLMCML) model, which exhibits enhanced chaotic characteristics and more suitable for image encryption compared with the classical coupled map lattices. Based on the SLMCML system, we propose an image encryption and compression method. To improve the plaintext sensitivity of image cryptosystem, we propose a novel plaintext-related internal keys generation method, which can obviously improve the plaintext sensitivity of initial values of SLMCML system, thus improve the plaintext sensitivity of whole process of compression and encryption. Our proposed image encryption scheme contains several steps. Initially, the discrete wavelet transform (DWT) is utilized to convert original image into coefficient matrix. Then a plaintext relation method is constructed, which generate internal keys as initial values of SLMCML system. Next the coefficient matrix is permutated by permutation sequences generated by SLMCML system to cyclic shift for making the energy evenly distributed. Next the coefficient matrix is done sparse processing. The compressed sensing is employed to compress coefficient matrix. Subsequently, the compressive image is permutated with spiral traversal and twice zigzag transform. Finally, the permutated image is diffused with column diffusion to generate cipher image. Through some common security analyses, our proposed image encryption scheme has good security performance and excellent image recovery quality.

In this paper, a new logistic–sine coupled map lattices (LSSCML) is proposed, which has a large parameter space and all lattices are in stable chaotic state. The characteristics of LSSCML system better satisfy the cryptography compared with the traditional coupled map lattices (CML) model. Moreover, we propose a dynamic compressed sensing method, which significantly improves compression performance and efficiency. Based on the LSSCML system and dynamic compressed sensing, an image compression and encryption scheme is proposed. It contains several steps. Firstly, the plain information is associated with external keys to generate initial values of LSSCML system which used to generate a group of control sequence for overall process of image encryption and compression. Then plain image matrix is converted to coefficient matrix by discrete wavelet transform. Next, a group of measurement matrix for dynamic compressed sensing which structured according to different compression ratio are generated by LSSCML system. Subsequently, the coefficient matrix is compressed by dynamic compressed sensing, and the compressed matrix is grouping quantized to generate quantized matrix. Finally, we proposed a Fisher–Yates simultaneous permutation–diffusion algorithm to do diffusion and permutation of quantized matrix. The test results show that our proposed image cryptosystem has excellent compression and security performance.

Internet of Vehicles (IoV) is a critical component of the transportation field, which can greatly facilitate the current transportation system. Meanwhile, more and more vehicles connect to the IoV and the security and privacy need to be guaranteed. Traditional authentication protocols based on bilinear pairs are computatively heavy and difficult to protect user identity and privacy in IoV environment. In addition, most existing protocols only consider the authentication between vehicles and infrastructure, but not consider between vehicles and vehicles, as well as single point of failure in the traditional single trusted authority (TA). To address these issues, this article proposes two lightweight mutual authentication protocols (MAPs) based on physical unclonable function (PUF) and multi-TA. The first protocol named V2I-MAP and is applied to vehicle-to-infrastructure (V2I) communication. The second is named V2V-MAP and is applied to vehicle-to-vehicle (V2V) communication. The protocols solve the interference of noise on PUF signals by fuzzy extractor, reduce the communication overhead and computation overhead of vehicles by utilizing PUF's lightweight computation characteristics, deal with the problems of impersonation attack with the help of the unclonable characteristics of PUF, and work out single TA single point of failure problems with the multi-TA model. Finally, the security analysis and informal security analysis of the proposed protocols are demonstrates that the proposed protocols meet the security requirements of the IoV system. ProVerif is used to verify the security of the protocols. Performance analysis shows that the protocols can reduce the communication and computation overhead than the comparable protocols.