Message protection method and system for Internet of Vehicles

Abstract

The invention belongs to the technical field of Internet of Vehicles security, and provides a message protection method and system for Internet of Vehicles, and the method comprises three parts: dynamic encryption algorithm adaptation, distributed trust authentication and abnormal message detection, and achieves the all-around security protection of Internet of Vehicles messages. And the dynamic encryption algorithm adaptation part can adaptively select the most suitable encryption algorithm to ensure the security and confidentiality of the message. The distributed trust authentication part carries out identity verification and trust evaluation on each node in the Internet of Vehicles by constructing a distributed trust system. In the message abnormity detection part, abnormal messages are found and processed in time by utilizing a machine learning algorithm; the message subjected to encryption protection is transmitted to the target node, the whole process of Internet of Vehicles message protection is completed, the method is suitable for a multi-node scene formed by a vehicle-mounted terminal, roadside equipment and a cloud platform, and the technical problems that in Internet of Vehicles message transmission, real-time performance and safety are unbalanced, cross-domain authentication efficiency is low, and abnormal messages are lagged can be solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of Internet of Vehicles security, and particularly relates to a message protection method and system for Internet of Vehicles. BACKGROUND

[0002] The Internet of Vehicles message refers to various types of information generated, propagated and received in the Internet of Vehicles. The Internet of Vehicles realizes data interaction between vehicles, edge facilities, people and the cloud by connecting vehicles with the network. The current Internet of Vehicles message protection technology mainly has the following three core defects: 1. Real-time and security imbalance: The existing technology mainly adopts fixed encryption algorithm, without considering the differentiated needs of Internet of Vehicles messages. For example, emergency messages (such as collision warning) need to be transmitted within milliseconds, while ordinary messages have higher time delay tolerance. Fixed algorithm leads to missed response window for emergency messages due to encryption time consumption, or waste of computing power for ordinary messages due to excessive encryption.

[0003] 2. Low efficiency of cross-domain authentication: The vehicle terminal, roadside device and third-party service platform in the Internet of Vehicles belong to different trust domains. The existing centralized authentication architecture has single point failure risk, and needs multiple interactions when cross-domain authentication is performed, with authentication delay of 200-500 ms, which cannot meet the needs of high mobility scenarios in the Internet of Vehicles.

[0004] 3. Abnormal message lag: The existing scheme mainly detects abnormal messages based on static rules, and cannot identify unknown attacks such as fake vehicle control instructions. Public data shows that the identification rate of unknown attacks by static detection is less than 40%, which easily leads to security incidents such as vehicle control and data leakage.

[0005] In summary, the existing technology cannot meet the three-dimensional needs of low time delay, high security and cross-domain compatibility in the Internet of Vehicles, and there is an urgent need for a message protection scheme with dynamic adaptation, distributed authentication and intelligent detection. SUMMARY

[0006] To solve the above technical problems, the application provides a message protection method and system for Internet of Vehicles to solve the problems in the prior art. The technical scheme adopted by the application is as follows: A message protection method for Internet of Vehicles, comprising the following steps: Step 1, dynamic encryption algorithm adaptation: for the Internet of Vehicles messages generated by multiple nodes of the Internet of Vehicles, based on the message priority and network time delay, an encryption algorithm is adaptively selected, and the encrypted Internet of Vehicles message is used for secure transmission between multiple nodes of the Internet of Vehicles; wherein the multiple nodes of the Internet of Vehicles include a terminal layer, an edge layer and a cloud layer; Step 2, distributed trust authentication: a multi-domain trust authentication model based on a consortium chain is constructed, and when the encrypted Internet of Vehicles message is transmitted across domains, distributed trust authentication based on the consortium chain is performed; Step 3, Abnormal message detection: Multi-dimensional abnormality detection is performed on the vehicle networking message authenticated by distributed trust, and the abnormal message stops transmission, and the normal message is transmitted to the target node.

[0007] Further, in step 1, an encryption algorithm is adaptively selected based on message priority and network delay, including the following steps: Step 1.1, Message priority division: The generated vehicle networking message is divided into at least three levels, and a priority coefficient P is defined: Emergency message, including: collision warning, brake instruction; P = 1.0; its network real-time delay requirement ≤ 50ms; Important message, including: vehicle state, traffic signal; P = 0.6; its network real-time delay requirement ≤ 100ms; Ordinary message, including: entertainment data, navigation update; P = 0.2; its network real-time delay requirement ≤ 500ms; Step 1.2, Encryption algorithm adaptation degree calculation: Introducing the adaptation degree function , the formula is as follows: ; Wherein, is the comprehensive message priority, is the network real-time delay, is the algorithm encryption strength, is the weight coefficient; if The higher the value is, the higher the matching degree of the corresponding encryption algorithm and the current message is; Step 1.3, Dynamic switching: Real-time acquisition of network delay T, calculation of F value of each candidate encryption algorithm, selection of algorithm with maximum F for encryption; if the network delay fluctuation exceeds 20%, repeat steps 2.2 and 2.3 to reselect the encryption algorithm.

[0008] Further, the candidate encryption algorithms include: The lightweight algorithm stored in the terminal layer includes: SM4-128, ChaCha20, AES-128; The standard algorithm stored in the edge layer includes: AES-256, ECC-256; and the edge layer also stores the lightweight algorithm of the terminal layer; The full type algorithm stored in the cloud layer includes: RC4, DES; and the cloud layer also stores the standard algorithm stored in the edge layer, and the lightweight algorithm stored in the terminal layer.

[0009] Further, step 2 includes: Step 2.1, Alliance chain deployment: Each vehicle enterprise, roadside device operator, and cloud platform is used as an alliance chain, and a global trust certificate library is stored respectively; Step 2.2, constructing a multi-domain trust authentication model through node trust value determining trustworthiness, node trust value The formula is as follows: ; Wherein, is the combined historical interaction success rate, is the authentication response time, is the number of abnormal behaviors; when , the node is in a trusted state and passes the authentication; otherwise, secondary verification is triggered; is a dynamic threshold value; Step 2.3, cross-domain authentication process: when the terminal layer initiates a cross-domain request, it submits authentication information to 1-2 high-trust-value nodes of the target domain, and after verification, the Internet of Vehicles message flow is transferred to the target node.

[0010] Further, the dynamic threshold value is determined by the following formula: ; Wherein, is the basic threshold value, which is determined as 0.6 by industry standard; is the node type weight coefficient, which is 1.1 for the terminal layer, 1.0 for the edge layer, and 0.9 for the cloud layer; is the network load coefficient; is the abnormal behavior correction coefficient, if the number of abnormal behaviors in the last 100 interactions 3, =1.1, otherwise =1.0.

[0011] Further, step 3 includes: in step 3, when performing multi-dimensional anomaly detection, a multi-dimensional anomaly detection algorithm based on an improved LSTM model is used, including the following steps: Step 3.1, feature extraction: extracting the core features of the Internet of Vehicles message, including: message length fluctuation value , sending frequency , instruction permission level , node trust value , message format compliance ; Step 3.2, constructing an LSTM model based on the Internet of Vehicles message; Step 3.3, anomaly score calculation: outputting the anomaly probability through the trained LSTM model, and calculating the anomaly score combining the feature weight: ; Wherein, Value 0-1, when ≥0.7 Abnormal message is determined.

[0012] Further, step 3.2 includes the following steps: Step 3.21, input multi-dimensional feature vector : ; Step 3.22, LSTM time series modeling processing: ; In the formula, is the hidden state of the LSTM output, is a long short-term memory network model; Step 3.23, output abnormal probability : ; In the formula, is a sigmoid function, is a linear layer for dimension mapping.

[0013] A message protection system for Internet of Vehicles, adopts the message protection method for Internet of Vehicles, the system comprises: The terminal layer comprises a vehicle-mounted terminal, and the vehicle-mounted terminal is provided with a message generation module, a message classification module, a lightweight encryption module and a local anomaly detection module; wherein the message classification module is used for executing the message priority classification of step 1, the lightweight encryption module is used for executing the adaptive selection of the encryption algorithm of step 1, and the local anomaly detection module is used for executing the multi-dimensional anomaly detection of step 3; The edge layer comprises a roadside device, and the roadside device is provided with a message generation module, a time delay acquisition module, an algorithm adaptation decision module, a distributed authentication module and a local anomaly detection module; wherein the time delay acquisition module is used for executing the network time delay acquisition of step 1, the algorithm adaptation decision module is used for executing the adaptive selection of the encryption algorithm of step 1, the distributed authentication module is used for executing the construction of the multi-domain trust authentication model based on the alliance chain of step 2, and the local anomaly detection module is used for executing the multi-dimensional anomaly detection of step 3; The cloud layer is deployed on an Internet of Vehicles platform, and comprises a message generation module, an alliance chain management module, a global anomaly analysis module and a log audit module; wherein the alliance chain management module is used for executing the construction of the multi-domain trust authentication model based on the alliance chain of step 2, the global anomaly analysis module is used for executing the multi-dimensional anomaly detection of step 3, and the log audit module is used for storing Internet of Vehicles messages to protect the whole-process log and is used for tracing.

[0014] The application has the following beneficial effects: (1) Improved real-time performance: Through dynamic encryption algorithm adaptation, the latency of emergency messages is reduced by more than 50%, meeting the millisecond-level response requirements of the Internet of Vehicles; (2) Enhanced security: The distributed authentication architecture improves the success rate of cross-domain authentication, and the intelligent anomaly detection model has an unknown attack recognition rate of over 92%, which greatly reduces security risks; (3) Compatibility expansion: The consortium blockchain architecture supports the rapid access of new car manufacturers and roadside devices without the need to reconstruct the authentication system, and is adapted to multi-entity collaborative scenarios in the Internet of Vehicles. (4) Computing power optimization: Lightweight encryption algorithm is used for ordinary messages, which reduces the computing power consumption of the vehicle terminal by 30%-40% and extends the battery life of the device. Attached Figure Description

[0015] Figure 1 This is a schematic flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the system structure of the present invention; Figure 3 This is an example diagram of the present invention. Detailed Implementation

[0016] The following will be based on embodiments of the present invention. Figures 1-3 The technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0017] like Figure 1 A message protection method for vehicle-to-everything (V2X) communication includes the following steps: Step 1, Dynamic Encryption Algorithm Adaptation: For vehicle network messages generated by multiple vehicle network nodes, an encryption algorithm is adaptively selected based on message priority and network latency. The encrypted vehicle network messages are used for secure transmission between multiple vehicle network nodes. Among them, the multiple vehicle network nodes include the terminal layer, edge layer, and cloud layer. Step 2, Distributed Trust Authentication: Construct a multi-domain trust authentication model based on consortium blockchain. When encrypted vehicle network messages are transmitted across domains, distributed trust authentication based on consortium blockchain is performed. Step 3, Abnormal Message Detection: Vehicle network messages that have undergone distributed trust authentication undergo multi-dimensional anomaly detection. Abnormal messages are stopped from transmission, while normal messages are transmitted to the target node. Messages encrypted and protected by this invention are transmitted to the target node, completing the entire process of vehicle network message protection. This method is applicable to multi-node vehicle network scenarios consisting of vehicle terminals, roadside equipment, and cloud platforms, and can solve the technical problems of imbalance between real-time performance and security, low efficiency of cross-domain authentication, and delay of abnormal messages in vehicle network message transmission.

[0018] In Step 1, the dynamic encryption algorithm adaptation stage, the system intelligently matches the most suitable encryption algorithm for messages generated by each node in the vehicle network based on a preset message priority evaluation system and real-time network latency data. This process considers not only the sensitivity of the message but also the efficiency of network transmission, ensuring that security is guaranteed without affecting the overall operational efficiency of the vehicle network. Next, in Step 2, the distributed trust authentication stage, the system utilizes consortium blockchain technology to construct a cross-domain trust authentication framework. Within this framework, each node establishes and maintains trust in a distributed manner through the consortium blockchain's consensus mechanism. When encrypted vehicle network messages need to be transmitted across domains, the system automatically triggers the consortium blockchain-based trust authentication process to ensure the authenticity and security of the message during transmission. Finally, in Step 3, the abnormal message detection stage, the system performs multi-dimensional and comprehensive anomaly detection on the vehicle network messages that have undergone distributed trust authentication. Once an abnormal message is detected, the system immediately interrupts its transmission, while normal messages are successfully transmitted to the target node, thus completing the entire vehicle network message protection process and achieving high efficiency and reliability throughout the entire vehicle network message protection process.

[0019] Furthermore, in step 1, the encryption algorithm is adaptively selected based on message priority and network latency, including the following steps: Step 1.1, Message Priority Classification: Divide the generated vehicle network messages into at least 3 levels and define a priority coefficient P: Emergency messages include: collision warnings and braking commands; P=1.0; network real-time latency requirement ≤50ms; Important information includes: vehicle status, traffic signals; P=0.6; its real-time network latency requirement is ≤100ms; Regular messages, including entertainment data and navigation updates; P=0.2; network real-time latency requirement ≤500ms; Step 1.2, Encryption Algorithm Fit Calculation: Introducing a Fit Function The formula is as follows: ; in, To prioritize messages, For network real-time latency, For the encryption strength of the algorithm, These are the weighting coefficients; if The higher the value, the better the matching degree between the corresponding encryption algorithm and the current message; the encryption strength of the algorithm. This is a constant term, predetermined based on empirical methods or expert evaluation, and its value ranges from 0 to 1. For example, it represents the encryption strength of the AES-256 algorithm. The strength is 0.9; the simpler and lighter the encryption algorithm, the stronger the encryption. The lower the value, the lower the encryption strength of the algorithm, such as the lightweight algorithm SM4-128. The value is 0.7. The S-values ​​for other encryption algorithms follow the same pattern and will not be listed further. Extensive testing and verification have confirmed that... The time-adaptation effect is optimal.

[0020] Step 1.3, Dynamic Switching: Real-time collection of network latency T, calculation of F value of each candidate encryption algorithm, and selection of the algorithm with the largest F value for encryption; if network latency fluctuation exceeds 20%, repeat steps 2.2 and 2.3 to reselect the encryption algorithm.

[0021] Furthermore, candidate encryption algorithms include: Lightweight algorithms for terminal layer storage include SM4-128, ChaCha20, and AES-128, all of which are lightweight symmetric encryption algorithms. They feature fast encryption speed and low resource consumption, making them suitable for devices with limited computing power at the terminal layer.

[0022] The standard-level algorithms for edge layer storage include AES-256, ECC-256, SM4, and RC4, all of which are standard-level encryption algorithms with high encryption strength and security.

[0023] The cloud-based storage layer supports a full range of algorithms, including RC4 and DES, all traditional encryption algorithms. It also integrates algorithm libraries from the edge and terminal layers, forming a comprehensive encryption algorithm pool covering all scenarios. This layered storage mechanism not only achieves a dynamic match between computing power and security but also reduces redundant computation in cross-layer communication through algorithm reuse. For example, when a terminal layer initiates a cross-domain request, it can directly call the SM4-128 algorithm stored in the cloud for lightweight encryption, avoiding the repeated transmission of algorithm parameters.

[0024] Furthermore, the cloud layer stores standard-level algorithms stored at the edge layer, as well as lightweight algorithms stored at the terminal layer.

[0025] Furthermore, step 2 includes: Step 2.1, Deployment of the consortium blockchain: Each car manufacturer, roadside equipment operator, and cloud platform will be used as a consortium blockchain, and a global trust certificate library will be stored separately for each. Step 2.2: Construct a multi-domain trust authentication model using node trust values. Trustworthiness is determined by the node's trust value. The formula is as follows: ; in, To incorporate historical interaction success rates, For authentication response time, The number of abnormal behaviors; when If the node is in a trusted state and passes authentication, then secondary verification is triggered. For dynamic thresholds; Step 2.3, Cross-domain authentication process: When the terminal layer initiates a cross-domain request, it submits authentication information to 1-2 high-trust nodes in the target domain. After successful verification, the vehicle network message flow is transferred to the target node.

[0026] Furthermore, dynamic threshold Determined by the following formula: ; in, The base threshold is set at 0.6 according to industry standards. The node type weight coefficient is 1.1 for the terminal layer, 1.0 for the edge layer, and 0.9 for the cloud layer. Network load factor; This is a correction coefficient for abnormal behavior. It is calculated based on the number of abnormal behaviors in the last 100 interactions. 3, then =1.1, otherwise =1.0. Network load factor. , The actual number of data packets per unit time is calculated using tools such as protocol analyzers, which count the number of data packets sent and received per second. The maximum number of data packets allowed by the network is derived from parameters such as communication cycle and data packet length.

[0027] For example, the current network load factor The calculated value is 0.3, which is the abnormal behavior correction coefficient. The value is 1.0; the target node is the edge layer, and the node type weight coefficient is 1.0. Set the value to 1.0; calculate the dynamic threshold. The threshold value is 0.636. In the multi-domain cross-domain authentication scenario of the Internet of Vehicles (IoV), the dynamic threshold setting of this invention comprehensively considers multiple dimensions such as node type, network load, and abnormal behavior to achieve dynamic and accurate judgment of node trustworthiness. It adapts to the functional and security requirements of nodes at different levels and can be flexibly adjusted according to real-time network status and node behavior, ultimately improving the security and reliability of IoV cross-domain authentication.

[0028] Furthermore, step 3 includes: In step 3, when performing multi-dimensional anomaly detection, a multi-dimensional anomaly detection algorithm based on an improved LSTM model is adopted, including the following steps: Step 3.1, Feature Extraction: Extract the core features of the vehicle network message, including: message length fluctuation value. Transmission frequency Command permission level Node trust value Message format compliance ; Step 3.2: Construct an LSTM model based on vehicle network messages; Step 3.3, Anomaly Score Calculation: Output the anomaly probability using the trained LSTM model. Anomaly scores are calculated by combining feature weights. : ; in, Values ​​range from 0 to 1. ≥0.7 When this happens, it is determined to be an abnormal message.

[0029] Furthermore, step 3.2 includes the following steps: Step 3.21, Input multi-dimensional feature vector : ; Step 3.22, LSTM timing modeling processing: ; In the formula, The hidden state output by the LSTM. This is a long short-term memory network model, which is existing technology; Step 3.23, output the anomaly probability. : ; In the formula, For the sigmoid function, It is a linear layer used to perform dimension mapping.

[0030] like Figure 2 , Figure 3 A message protection system for vehicle-to-everything (V2X) networks, employing the aforementioned message protection method for V2X networks, the system comprising: The terminal layer includes an in-vehicle terminal, which is equipped with a message generation module, a message classification module, a lightweight encryption module, and a local anomaly detection module. The message classification module is used to perform message priority classification in step 1, the lightweight encryption module is used to perform adaptive selection encryption algorithm in step 1, and the local anomaly detection module is used to perform multi-dimensional anomaly detection in step 3. Specifically, the in-vehicle terminal is an embedded device. Its message generation module includes vehicle sensors and an in-vehicle MCU. Vehicle sensors include, for example, a GPS module, an IMU (Inertial Measurement Unit), and brake / accelerator sensors. The message classification module is the in-vehicle MCU, which implements priority classification through software logic. The lightweight encryption module is an in-vehicle security chip or an encryption coprocessor built into the MCU. The local anomaly detection module is the in-vehicle MCU or an external small AI acceleration chip.

[0031] The edge layer includes roadside equipment, which is equipped with a message generation module, a latency acquisition module, an algorithm adaptation and decision-making module, a distributed authentication module, and a local anomaly detection module. Among them, the latency acquisition module is used to perform the network latency acquisition in step 1, the algorithm adaptation and decision-making module is used to perform the adaptive selection of encryption algorithm in step 1, the distributed authentication module is used to perform the construction of a multi-domain trust authentication model based on consortium blockchain in step 2, and the local anomaly detection module is used to perform multi-dimensional anomaly detection in step 3. Specifically, roadside equipment hardware can include millimeter-wave radar, high-definition cameras, traffic light controllers, etc., and an edge computing gateway is set up. For example, cameras collect road condition data, and the edge gateway processes the data to generate "road congestion messages." The latency acquisition module combines the edge gateway's network interface chip with a timestamp module. The algorithm adaptation and decision-making module is the edge gateway's CPU or FPGA. The distributed authentication module is the edge gateway's security chip and blockchain node server. The local anomaly detection module is the edge gateway's AI acceleration card and large-capacity memory.

[0032] The cloud layer, deployed on the vehicle-to-everything (V2X) platform, includes a message generation module, a consortium blockchain management module, a global anomaly analysis module, and a log auditing module. The consortium blockchain management module is used to execute step 2, which involves building a multi-domain trust authentication model based on the consortium blockchain. The global anomaly analysis module is used to execute step 3, which involves multi-dimensional anomaly detection. The log auditing module is used to store V2X messages to protect the entire process logs and facilitate traceability.

[0033] Specifically, the cloud-layer hardware is based on the terminal computer of the vehicle-to-everything (V2X) platform, with its message generation module consisting of a cloud server and corresponding network interfaces. The consortium blockchain management module consists of blockchain node servers and corresponding distributed storage. The global anomaly analysis module consists of a cloud GPU cluster and corresponding big data platform. The log auditing module consists of a storage server and corresponding database cluster.

[0034] Specific embodiments of the present invention are as follows: Taking "the vehicle-mounted terminal sending an emergency braking command to the roadside equipment" as an example, the process is as follows: The terminal layer message classification module determines that the instruction is an urgent message and assigns it a certain priority. ; Edge layer latency acquisition module obtains current network latency The algorithm adaptation decision module calculates candidate algorithms, and part of the F-value calculation is as follows: ; ; ; AES-256 has the largest F value, so the AES-256 algorithm is chosen. The terminal layer lightweight encryption module uses AES-256 encryption commands to send to the edge layer; Edge layer distributed authentication module verifies terminal trust value Directly authenticated; The edge layer local anomaly detection module extracts instruction features: ;calculate This was determined to be a normal message. The edge layer forwards instructions to the target vehicle and simultaneously uploads the protection process log to the cloud layer for storage.

[0035] Taking "multi-vehicle collaborative obstacle avoidance on highways" as an example, the process is as follows: On a six-lane highway with a speed limit of 120 km / h, when vehicle C (i.e., vehicle terminal C) is traveling in the left fast lane, it detects a sudden obstacle 150m ahead (such as scattered tires or cargo from a truck) using lidar. It needs to send a cooperative obstacle avoidance message to three related vehicles D, E, and F behind it, as well as the roadside equipment RSU-5.

[0036] 1. Message classification and encryption algorithm adaptation: The message classification module of the vehicle terminal C determines the message type: Emergency deceleration command (vehicle D): P=1.0, latency requirement ≤50ms; Lane change and avoidance suggestion (vehicles E, F): P=0.6, latency requirement ≤100ms.

[0037] The latency acquisition module of the roadside device RSU-5 obtains the network latency in real time: vehicle C → RSU-5: T1=25ms; RSU-5 → vehicle D: T2=22ms; RSU-5 → vehicles E and F: T3=28ms.

[0038] Encryption algorithm compatibility calculation: ; Emergency message (vehicle D): Candidate algorithms AES-256 (S=0.9), SM4-128 (S=0.7); AES-256: ; SM4-128: ; Choose the AES-256 algorithm with the largest F value.

[0039] Important information (vehicles E and F): Candidate algorithms SM4-128 (S=0.7) and RC4 (S=0.5); SM4-128: ; RC4: ; Choose the SM4-128 algorithm (balancing security and computing power).

[0040] 2. Distributed Trust Authentication: The distributed authentication module of the roadside device RSU-5 queries the trust value of each terminal: ; Vehicle terminal C: R=99% (99 out of the last 100 interactions were successful), t=75ms, N=0, Therefore, it has a high trust value and is approved directly; The vehicle terminals D, E, and F have Trust values ​​of 0.85, 0.78, and 0.82 respectively, all ≥0.6, requiring no secondary verification.

[0041] Authentication process: RSU-5 completes authentication of all terminals (including consortium blockchain node synchronization) in just 35ms, meeting the requirement of cross-domain authentication ≤100ms and eliminating the risk of single point of failure.

[0042] 3. Abnormal message detection: The RSU-5's local anomaly detection module extracts message characteristics: Emergency deceleration command (vehicle D): ; Lane change and yielding advice (vehicles E and F): .

[0043] Anomaly scoring calculation ): Emergency deceleration command: (normal); Lane changing and yielding advice: (normal).

[0044] Result: All messages were deemed legitimate and immediately forwarded by RSU-5 without any false positives or interceptions.

[0045] After receiving the emergency deceleration command, vehicle D initiates braking (acceleration -3m / s²) within 30ms and decelerates to 60km / h at a distance of 50m from the obstacle; After receiving the lane change suggestion, vehicles E and F complete the safe lane change within 50ms. The entire cooperative obstacle avoidance process is free of collision risk, and the total message transmission delay is ≤90ms, which meets the real-time requirements of highways.

[0046] Combining the examples of "vehicle-mounted terminal sending emergency braking commands to roadside equipment" and "multi-vehicle cooperative obstacle avoidance on highways," it can be seen that the message protection method and system for vehicle-to-everything (V2X) proposed in this invention demonstrate significant advantages in practical application scenarios. In the scenario of "vehicle-mounted terminal sending emergency braking commands to roadside equipment," corresponding message classification and encryption algorithm adaptation ensure high-priority processing and secure transmission of emergency messages. Simultaneously, the distributed trust authentication mechanism effectively verifies the trustworthiness of the terminal, preventing the access of malicious nodes. The abnormal message detection stage further ensures the authenticity of messages, avoiding the propagation of misoperations or malicious commands.

[0047] In the scenario of "multi-vehicle cooperative obstacle avoidance on highways," obstacle avoidance was successfully achieved through collaborative message transmission and real-time processing between multiple terminals, avoiding collision risks. Furthermore, the latency of the entire process was kept to an extremely low level, meeting the stringent real-time requirements of highways. These two examples fully demonstrate the effectiveness and practicality of this invention in protecting vehicle-to-everything (V2X) messages.

[0048] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, alterations, alterations, or substitutions made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A message protection method for vehicle-to-everything (V2X) networks, characterized in that, Includes the following steps: Step 1, Dynamic Encryption Algorithm Adaptation: For vehicle network messages generated by multiple vehicle network nodes, an encryption algorithm is adaptively selected based on message priority and network latency. The encrypted vehicle network messages are used for secure transmission between multiple vehicle network nodes. Among them, the multiple vehicle network nodes include the terminal layer, edge layer, and cloud layer. Step 2, Distributed Trust Authentication: Construct a multi-domain trust authentication model based on consortium blockchain. When encrypted vehicle network messages are transmitted across domains, distributed trust authentication based on consortium blockchain is performed. Step 3, Abnormal message detection: Vehicle network messages that have undergone distributed trust authentication undergo multi-dimensional anomaly detection. Abnormal messages are stopped from being transmitted, while normal messages are transmitted to the target node.

2. The message protection method for vehicle networking according to claim 1, characterized in that, In step 1, an encryption algorithm is adaptively selected based on message priority and network latency, including the following steps: Step 1.1, Message Priority Classification: Divide the generated vehicle-to-everything (V2X) messages into at least 3 levels and define a priority coefficient P: Emergency messages include: collision warnings and braking commands; P=1.0; network real-time latency requirement ≤50ms; Important information includes: vehicle status, traffic signals; P=0.6; its real-time network latency requirement is ≤100ms; Regular messages, including entertainment data and navigation updates; P=0.2; network real-time latency requirement ≤500ms; Step 1.2, Encryption Algorithm Fit Calculation: Introducing a Fit Function The formula is as follows: ； in, To prioritize messages, For network real-time latency, For the encryption strength of the algorithm, These are the weighting coefficients; if The higher the value, the better the matching degree between the corresponding encryption algorithm and the current message; Step 1.3, Dynamic Switching: Real-time collection of network latency T, calculation of F value of each candidate encryption algorithm, and selection of the algorithm with the largest F value for encryption; if network latency fluctuation exceeds 20%, repeat steps 2.2 and 2.3 to reselect the encryption algorithm.

3. The message protection method for vehicle networking according to claim 2, characterized in that, Candidate encryption algorithms include: Lightweight algorithms for terminal layer storage include: SM4-128, ChaCha20, and AES-128; The edge layer stores standard-level algorithms, including AES-256 and ECC-256; and the edge layer also stores lightweight algorithms from the terminal layer. The cloud layer stores all types of algorithms, including RC4 and DES; and the cloud layer also stores standard-level algorithms for edge layer storage, as well as lightweight algorithms for terminal layer storage.

4. The message protection method for vehicle networking according to claim 1, characterized in that, Step 2 includes: Step 2.1, Deployment of the consortium blockchain: Each car manufacturer, roadside equipment operator, and cloud platform will be used as a consortium blockchain, and a global trust certificate library will be stored separately for each. Step 2.2: Construct a multi-domain trust authentication model using node trust values. Trustworthiness is determined by the node's trust value. The formula is as follows: ； in, To incorporate historical interaction success rates, For authentication response time, The number of abnormal behaviors; when If the node is in a trusted state and passes authentication, then secondary verification is triggered. For dynamic thresholds; Step 2.3, Cross-domain authentication process: When the terminal layer initiates a cross-domain request, it submits authentication information to 1-2 high-trust nodes in the target domain. After successful verification, the vehicle network message flow is transferred to the target node.

5. A message protection method for vehicle networking according to claim 4, characterized in that, Dynamic threshold Determined by the following formula: ； in, The base threshold is set at 0.6 according to industry standards. The node type weight coefficient is 1.1 for the terminal layer, 1.0 for the edge layer, and 0.9 for the cloud layer. Network load factor; This is a correction coefficient for abnormal behavior. It is calculated based on the number of abnormal behaviors in the last 100 interactions. 3, then =1.1, otherwise =1.

0.

6. A message protection method for vehicle networking according to claim 4, characterized in that, Step 3 includes: In step 3, when performing multi-dimensional anomaly detection, a multi-dimensional anomaly detection algorithm based on an improved LSTM model is used, including the following steps: Step 3.1, Feature Extraction: Extract the core features of the vehicle network message, including: message length fluctuation value. Transmission frequency Command permission level Node trust value Message format compliance ; Step 3.2: Construct an LSTM model based on vehicle network messages; Step 3.3, Anomaly Score Calculation: Output the anomaly probability using the trained LSTM model. Anomaly scores are calculated by combining feature weights. : ； in, Values ​​range from 0 to 1. ≥0.7 When this happens, it is determined to be an abnormal message.

7. A message protection method for vehicle networking according to claim 6, characterized in that, Step 3.2 includes the following steps: Step 3.21, Input multi-dimensional feature vector : ； Step 3.22, LSTM timing modeling processing: ； In the formula, The hidden state output by the LSTM. It is a long short-term memory network model; Step 3.23, output the anomaly probability. : ； In the formula, For the sigmoid function, It is a linear layer used to perform dimension mapping.

8. A message protection system for vehicle networking, characterized in that, The message protection method for vehicle networking according to any one of claims 1-7, the system comprising: The terminal layer includes an in-vehicle terminal, which is equipped with a message generation module, a message classification module, a lightweight encryption module, and a local anomaly detection module. The message classification module is used to perform message priority classification in step 1, the lightweight encryption module is used to perform adaptive selection encryption algorithm in step 1, and the local anomaly detection module is used to perform multi-dimensional anomaly detection in step 3. The edge layer includes roadside equipment, which is equipped with a message generation module, a latency acquisition module, an algorithm adaptation and decision-making module, a distributed authentication module, and a local anomaly detection module. Among them, the latency acquisition module is used to perform the network latency acquisition in step 1, the algorithm adaptation and decision-making module is used to perform the adaptive selection of encryption algorithm in step 1, the distributed authentication module is used to perform the construction of a multi-domain trust authentication model based on consortium blockchain in step 2, and the local anomaly detection module is used to perform multi-dimensional anomaly detection in step 3. The cloud layer, deployed on the vehicle-to-everything (V2X) platform, includes a message generation module, a consortium blockchain management module, a global anomaly analysis module, and a log auditing module. The consortium blockchain management module is used to execute step 2, which involves building a multi-domain trust authentication model based on the consortium blockchain. The global anomaly analysis module is used to execute step 3, which involves multi-dimensional anomaly detection. The log auditing module is used to store V2X messages to protect the entire process logs and facilitate traceability.

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