Information security transmission method and system of multiplex data bus

Abstract

The application provides a kind of multiplex data bus information security transmission method and system, it is related to information security technical field, method includes: in multiplex data bus deployment trusted noise node;Read the transmission characteristic vector of information transmission request, input generation twin network and carry out analysis, obtain twin transmission characteristic vector, generation twin network includes predefined similarity term and corresponding preset similarity threshold value;Send to trusted noise node for processing, obtain twin noise signal and noise transmission protocol, when executing information transmission request, twin noise signal is interfered with based on noise transmission protocol and safely transmitted.The application solves the technical problem that the multiplex data bus in the prior art usually uses static protection mechanism, the protection means is single, it is difficult to deal with complex attack, leading to insufficient information transmission security.

Description

Technical Field

[0001] This invention relates to the field of information security technology, specifically to an information security transmission method and system for a multiplexed data bus. Background Technology

[0002] Multiplexed data buses are widely used in critical fields such as aviation and aerospace for information transmission between multiple devices. However, traditional multiplexed data buses have significant security issues in information transmission. Existing technologies typically use static protection mechanisms for multiplexed data buses, such as encryption and access control. While these measures can protect information to some extent, attackers, once they obtain the bus traffic data, can still extract and identify the actual data content through traffic analysis, leading to insufficient security during information transmission. Summary of the Invention

[0003] This application provides a method and system for secure information transmission using a multiplexed data bus, aiming to solve the technical problem that existing multiplexed data buses typically use static protection mechanisms, which are limited in scope and unable to cope with complex attacks, resulting in insufficient information transmission security.

[0004] The first aspect disclosed in this application provides a method for secure information transmission on a multiplexed data bus. The method includes: deploying a trusted noise node on the multiplexed data bus; reading the transmission feature vector of an information transmission request; inputting the transmission feature vector into a generative twin network for analysis to obtain a twin transmission feature vector, wherein the generative twin network includes predefined similarity terms and preset similarity thresholds corresponding to each similarity term; sending the twin transmission feature vector to the trusted noise node for processing to obtain a twin noise signal and a noise transmission protocol; and when the multiplexed data bus executes the information transmission request, interfering with the twin noise signal for secure transmission based on the noise transmission protocol.

[0005] The second aspect of this application discloses an information security transmission system for a multiplexed data bus. The system is used in the aforementioned information security transmission method for a multiplexed data bus. The system includes: a trusted noise node deployment module for deploying trusted noise nodes on the multiplexed data bus; a twin transmission feature vector acquisition module for reading the transmission feature vector of an information transmission request, inputting the transmission feature vector into a generative twin network for analysis, and obtaining a twin transmission feature vector, wherein the generative twin network includes predefined similarity terms and preset similarity thresholds corresponding to each similarity term; and an interference-secure transmission module for sending the twin transmission feature vector to the trusted noise node for processing to obtain a twin noise signal and a noise transmission protocol. When the multiplexed data bus executes the information transmission request, it performs interference-secure transmission of the twin noise signal based on the noise transmission protocol.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects:

[0007] By deploying trusted noise nodes on a multiplexed data bus, data transmission security is effectively enhanced. These nodes generate noise signals, ensuring attackers cannot accurately identify the real data stream through time correlation analysis. This proactive interference method shifts the system from traditional passive protection to proactive deception, significantly improving its ability to defend against advanced persistent threats. Generative twin networks are used to analyze transmission feature vectors, generating corresponding twin transmission feature vectors. This effectively disguises the data transmission process. Detailed analysis of transmission features, combined with similarity terms, ensures that the generated twin features are similar to but different from the real data, thus increasing information security and making it difficult for attackers to extract real information from the data stream's features. After sending the twin transmission feature vectors to the trusted noise nodes, the generated twin noise signals are used to interfere with secure transmission via a noise transmission protocol. The noise signals effectively interfere with attackers' traffic analysis, preventing data from being easily identified or recovered during transmission. By precisely controlling the timing and intensity of noise injection, transmission security and data reliability are ensured.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] Figure 1 This is a schematic flowchart of an information security transmission method for a multiplexed data bus provided in an embodiment of this application.

[0010] Figure 2 This is a schematic diagram of an information security transmission system structure for a multiplexed data bus, provided as an embodiment of this application.

[0011] Figure labeling: Trusted noise node deployment module 10, twin transmission feature vector acquisition module 20, interference secure transmission module 30. Detailed Implementation

[0012] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0013] Example 1, as Figure 1 As shown in the figure, this application provides an information security transmission method for a multiplexed data bus, the method comprising:

[0014] Deploy trusted noise nodes on a multiplexed data bus.

[0015] Trusted noise nodes are used to inject noise signals into the bus. These noise signals appear random and irregular externally, but they can be managed and controlled through specific protocols to avoid interfering with effective data transmission. The goal is to disrupt the correlation between real data and noise, making it difficult for attackers to recover the real data by analyzing data flow, time characteristics, and other information. Multiple trusted noise nodes are deployed at different nodes of the multiplexed data bus to enhance the randomness of the interference. These nodes are deployed at the edge terminals or intermediate nodes of data transmission, ensuring that the noise signals cover the critical path of the data flow. While the noise signals are random and unpredictable, their generation and injection process is controlled by trusted noise nodes, ensuring that the difficulty of analysis for attackers is maximized without compromising system functionality.

[0016] The transmission feature vector of the information transmission request is read, and the transmission feature vector is input into the generative twin network for analysis to obtain the twin transmission feature vector. The generative twin network includes predefined similarity terms and preset similarity thresholds corresponding to each similarity term.

[0017] When a data transmission request is received, the transmission feature vector of the request is first extracted. These features include, but are not limited to, timing information, spectral characteristics, frame length, and message interval. These features reflect the transmission patterns and attributes of the data. A generative twin network consists of a generator and a discriminator. The generator produces twin transmission feature vectors that are similar to the input feature vector but slightly different. The discriminator performs similarity checks on candidate vectors to ensure that the generated twin transmission feature vectors meet the similarity requirements of the original transmission feature vectors—that is, they are not distorted but are deceptive. The generative twin network includes multiple similarity terms, such as timing and spectral characteristics. Each similarity term corresponds to a preset similarity threshold. The generator generates twin transmission feature vectors based on the preset similarity thresholds, and the discriminator filters them to ensure that the generated feature vectors are consistent with the original request in these similarity terms. During the generation of feature vectors, the network adjusts the generator's output according to the threshold requirements of each feature term to achieve the desired deception effect. Here, "twin" refers to the high similarity between transmission feature vectors, making it difficult for attackers to distinguish the real data from the bus traffic.

[0018] The twin transmission feature vector is sent to the trusted noise node for processing to obtain the twin noise signal and the noise transmission protocol. When the multiplexed data bus executes the information transmission request, the twin noise signal is subjected to interference-secure transmission based on the noise transmission protocol.

[0019] The twin transport feature vector is transmitted to the trusted noise node, which provides the node with a processed twin feature vector that conforms to the protocol requirements. The trusted noise node generates a twin noise signal based on the input twin transport feature vector. This twin noise signal is designed to be independent of the original data stream but still possess interference effects. The twin noise signal structure includes multiple noise segments, each with start and end times, spectral characteristics, power, and other parameters. These parameters work together to maximize the noise's impact on bus traffic. To ensure effective interference from the twin noise signal while avoiding interference with normal data transmission, the trusted noise node processes and injects the signal according to a noise transmission protocol. This protocol ensures that the twin noise signal will not disrupt the data stream or normal system operation during transmission. The protocol covers the noise injection time, frequency, and location; noise intensity and signal shape; and noise periodicity and duty cycle.

[0020] When executing information transmission requests, the multiplexed data bus injects noise according to a noise transmission protocol, following a specific time window and spectral distribution, to ensure data transmission security. Through this interference, attackers find it difficult to extract or recover valid transmission content using traditional methods such as time correlation analysis. By introducing a deceptive design using trusted noise nodes and twin transmission feature vectors, the system shifts from passive protection to active deception, effectively preventing attacks through traffic analysis by advanced persistent threats.

[0021] Furthermore, the multiplexed data bus includes a security policy manager, which includes a level-similarity mapping table; identifies the execution function of the information transmission request, outputs the security requirement level of the information transmission request according to the execution function; reads the transmission feature vector of the information transmission request, maps similarity items in the level-similarity mapping table according to the security requirement level of the information transmission request and the transmission feature vector, and outputs the corresponding similarity items and the preset similarity threshold corresponding to each similarity item.

[0022] The Security Policy Manager is a core component for managing and configuring the security of information transmission. Its role is to identify and assess the security requirement levels of information transmission requests and take appropriate security measures based on the specific characteristics of the transmission. The Security Policy Manager assigns different security requirement levels to each transmission request based on various factors, such as the type of transmission and the sensitivity of the data. These levels influence subsequent transmission characteristic analysis and the selection of noise interference strategies. The level-similarity mapping table is a data structure within the Security Policy Manager that contains the mapping relationship between different security requirement levels of transmission requests and similarity items. The purpose of the mapping table is to define similarity items for each transmission request for different security requirement levels, such as temporal similarity, spectral similarity, frame length similarity, etc., and their corresponding preset similarity thresholds.

[0023] The execution function of an information transmission request can be data transmission, control signals, diagnostic signals, etc. Identifying the execution function of a request involves determining its security requirements based on the request type. Based on the execution function, the corresponding security requirement level is output. The security requirement level is determined by analyzing the following factors: data sensitivity (highly sensitive data requires higher security); system role (certain specific system roles require higher security, such as master control nodes and core modules); and the criticality of the execution function (for example, requests involving core data transmission often require a high level of security to prevent tampering).

[0024] Transmission feature vectors describe parameters such as data timing, spectrum, and frame length. Reading these vectors helps capture the traffic patterns of the request, aiding subsequent processing in identifying the behavioral characteristics of the data stream. Based on the security requirement level and the transmission feature vector, a lookup is performed in a level-similarity mapping table to map similarity items. Different security requirement levels guide the system to focus on different feature items. Based on the mapping results, a set of similarity items is output, such as timing similarity, spectrum similarity, and load fluctuation similarity, along with a preset similarity threshold for each item. These thresholds represent the tolerable range for each similarity item. The generative Siamese network generates a Siamese transmission feature vector based on these thresholds, ensuring its similarity to the original data remains within acceptable limits.

[0025] Furthermore, methods for constructing a rank-similarity mapping table include:

[0026] Define a set of similarity items, including temporal similarity, spectral similarity, frame length similarity, message interval distribution similarity, load fluctuation similarity, and protocol semantic similarity; obtain historical information transmission request samples and security requirement level label samples of the historical information transmission request samples; collect transmission feature vector samples of the historical information transmission request samples; optimize the set of similarity items based on the security requirement level label samples and the transmission feature vector samples to construct the level-similarity mapping table.

[0027] Temporal similarity describes the transmission pattern of data packets along the time axis. By analyzing the time intervals and temporal relationships of data packets, it judges the similarity of transmission characteristics. For example, if the transmission times of two data packets are small, it indicates that they belong to similar transmission streams. Spectral similarity describes the distribution of transmitted data in the spectrum. Spectral characteristics are an important dimension for assessing whether the transmitted content is similar. Transmissions with high similarity often exhibit similar spectral distributions. Frame length similarity describes the size or length of transmitted data packets. If the lengths of two data packets are very similar, they can be considered structurally similar. Message interval distribution similarity is used to analyze whether the intervals between data packets are regular or similar. Attackers may infer the data transmission content by analyzing the distribution of data packet intervals. Maintaining similarity in intervals can enhance the confidentiality of transmission. Load fluctuation similarity describes the fluctuation of transmission load over a period of time. Normal data streams often exhibit regular fluctuations, while malicious data streams have abnormal fluctuation patterns. Protocol semantic similarity is used to analyze whether the semantic level of the transmission protocol is consistent. Semantic differences between different protocols will affect the transmission characteristics of data.

[0028] Historical information transmission request samples are collected from real-world systems or simulated environments. These samples encompass various types of data transmission, involving different application scenarios and transmission modes. Each historical information transmission request sample has a corresponding security requirement level label. This label indicates the security requirement level of the request, which can be low, medium, or high. The label is determined based on the nature, sensitivity, and function of the request. The security requirement level label can be determined through expert systems, manual rules, or analysis based on historical events.

[0029] Transmission feature vector samples are obtained through packet analysis, specifically by measuring various attributes of each packet, including timing characteristics, spectral characteristics, frame characteristics, and traffic patterns. These transmission feature vector samples serve as input data for subsequent learning algorithms.

[0030] Based on collected transmission feature vector samples and security requirement level label samples, the importance of each similarity item under different security requirement levels is learned. Algorithms such as support vector machines, neural networks, and decision trees can be used. By learning from historical data, the weight of each similarity item in the mapping table is gradually adjusted. Through training, the model parameters are optimized to provide a reasonable evaluation of the similarity item under each security requirement level. During the learning process, the security contribution of each similarity item is analyzed to evaluate its role in improving system security. This can be achieved by calculating the contribution value of each feature to the security requirement level judgment. Finally, after optimization learning, the constructed level-similarity mapping table contains the similarity items and similarity thresholds corresponding to each security requirement level.

[0031] Furthermore, this includes:

[0032] Based on the security requirement level label samples and the transmission feature vector samples, a security contribution analysis is performed on the similarity item set to obtain a security contribution set; the security contribution set is analyzed to obtain the corresponding identifier similarity item under each security requirement level label, wherein the identifier similarity item is a similarity item whose security contribution is greater than a preset contribution threshold; the initial similarity threshold of the identifier similarity item is initialized; the objective function is optimized according to the initial similarity threshold of the identifier similarity item to obtain the optimal solution of the similarity threshold of the identifier similarity item; based on the mapping relationship between the security requirement level label samples and the optimal solution of the similarity threshold of the identifier similarity item, the level-similarity mapping table is constructed.

[0033] For each transmission request sample, combining its transmission feature vector and security requirement level label, the impact of similarity items on system security can be evaluated. A model can be trained to quantify the contribution of each similarity item, for example, using regression analysis, decision trees, etc., to analyze the influence of each similarity item on the security requirement level. Security contribution can be calculated based on the weighted importance of similarity items; for example, temporal similarity, spectral similarity, and frame length similarity have different weights for different security requirement levels. The trained model outputs the security contribution of each similarity item to the transmission request. By analyzing all transmission request samples, a complete set of security contributions is obtained, recording the contribution of each similarity item at different security requirement levels.

[0034] For each security requirement level label, the contribution value of each similarity item in the security contribution set is analyzed. The security contribution of each similarity item is compared with a preset contribution threshold. If the contribution of a certain similarity item is greater than the threshold, it is determined to be an identifier similarity item under that security requirement level. For each security requirement level, these identifier similarity items play a core role in the subsequent twin transmission feature generation and secure interference transmission.

[0035] For each identified similarity item, an initial similarity threshold is set to determine whether the transmission features are sufficiently similar to meet security requirements. If the temporal difference between two transmission features exceeds this threshold, they are considered dissimilar. The initial similarity threshold can be determined by analyzing historical transmission data or estimated through model optimization. Generally, a conservative threshold can be set initially, and then continuously optimized and adjusted to adapt to different types of transmission requests.

[0036] The objective function is established by considering multiple factors, such as security, transmission quality, and system efficiency. The optimization goal is to find a balance that provides sufficient security without excessively interfering with normal data transmission. During the optimization process, if the objective function is continuous and differentiable, gradient descent can be used to minimize the loss while maximizing security. If the objective function is complex and lacks a clear derivative, a genetic algorithm is used to simulate biological evolution to find the optimal solution. In each optimization process, the similarity threshold is continuously adjusted until the threshold that maximizes the objective function is found. In this process, the optimization goal is to ensure that interference with secure transmission minimizes the impact on normal transmission while maintaining system security. Finally, the optimization algorithm yields optimal similarity thresholds that identify similarity items. These thresholds represent the most suitable values ​​for different similarity items under different security requirement levels.

[0037] By utilizing the security requirement level label samples and similarity threshold optimization solutions obtained during training, a mapping table is constructed by analyzing the relationship between them. That is, for each security requirement level, the optimal similarity threshold optimization solution is selected and bound to the corresponding similarity item. This mapping relationship ensures that the similarity standard can be dynamically adjusted according to security requirements during information transmission.

[0038] Furthermore, the transmission feature vector is input into a generative Siamese network for analysis to obtain a Siamese transmission feature vector, including:

[0039] The generative twin network includes a generator and a discriminator. The generator is a conditional generator based on a variational encoder. The transmission feature vector is input into the generator in the generative twin network to obtain a set of candidate transmission feature vectors based on similarity terms. The discriminator is used to calculate the similarity between each candidate transmission feature vector in the set of candidate transmission feature vectors and the transmission feature vector to obtain candidate transmission feature vectors that satisfy a preset similarity threshold for each similarity term as twin transmission feature vectors.

[0040] Generative Siamese Networks (GSiamese Networks) are network structures composed of a generator and a discriminator. Unlike standard Generative Adversarial Networks (GANs), GSiamese Networks focus on generating transport feature vectors that are similar to the input features but have undergone perturbation to increase transport security. The generator's function is to generate a set of candidate transport feature vectors based on the input transport feature vector. This generation process is conditional; that is, it generates corresponding Siamese feature vectors based on the specific values ​​of the transport feature vectors and a similarity term. The generator employs a variational encoder-based structure. A variational encoder is a generative model that compresses the input transport feature vector into a distribution in the latent space through an encoder, and then generates similar transport feature vectors through a decoder. The advantage of variational encoders is that they can generate smoother and more diverse feature vectors, avoiding the pattern collapse problem that may occur in traditional generative models.

[0041] The generator produces a set of candidate transmission feature vectors based on the input transmission feature vector. Each candidate transmission feature vector has a different perturbation, thereby enhancing data privacy and security. The discriminator calculates the similarity of each candidate feature vector in the set. The similarity calculation is based on specific similarity terms, such as temporal similarity, spectral similarity, and frame length similarity. These similarity terms measure the degree of similarity between the candidate transmission feature vector and the original feature vector in different dimensions. Each similarity term has a preset similarity threshold. If the similarity between the candidate feature vector and the original transmission feature vector exceeds the threshold, the candidate feature vector is considered a valid twin feature vector; otherwise, the discriminator will exclude the candidate vector. During the discriminator's selection process, only those candidate transmission feature vectors that meet the preset similarity thresholds for all similarity terms are selected as the final twin transmission feature vectors for use in the subsequent secure interference transmission stage.

[0042] Furthermore, the twin transmission feature vector is sent to the trusted noise node for processing to obtain a twin noise signal; wherein the twin noise signal includes multiple noise segments, and each noise segment includes an injection start and end time window, an injection node list, an injection power, and injection spectral shape and duty cycle parameters.

[0043] Once the twin transport feature vector is generated, it is transmitted to a trusted noise node deployed on the bus. The trusted noise node performs noise processing on the twin transport feature vector, including injecting noise into the transport signal to make it more difficult for attackers to reconstruct the real data through traffic analysis. The result of the processing is a signal containing noise interference, called a twin noise signal, which is composed of multiple noise segments. The role of each noise segment in the transmission process is to introduce appropriate perturbations to make the data stream look more chaotic, thus making it difficult for attackers to distinguish between real data and noise.

[0044] The injection start and end time window specifies the duration of noise injection. By controlling the timing of noise injection, the data stream can be interfered with more flexibly, thus preventing attackers from identifying normal transmission patterns through time feature analysis. The injection node list specifies which nodes the noise will be injected into, such as intermediate devices, terminal devices in the data transmission link, or edge nodes on the data bus, to cover different links in the transmission path and enhance the interference effect. The injection power is a parameter that determines the interference intensity. The power setting must be strong enough to achieve the interference purpose, but not so strong that it affects the normal transmission of data or causes abnormal network behavior. The injected spectrum shape determines the distribution of noise in the frequency domain. The spectrum shape can be designed as broadband or narrowband noise as needed, and can be adjusted according to the characteristics of the transmitted data to ensure the effectiveness of the interference. The duty cycle parameter defines the switching period of the noise signal, that is, the ratio of the high level to the low level of the noise signal. By adjusting the duty cycle, the duration and frequency of the noise signal can be controlled, thereby affecting the interference effect on data transmission.

[0045] Furthermore, when the multiplexed data bus is a 1553B data bus, the similarity item set also includes bus cycle time synchronization similarity, RT sub-address behavior similarity, and pattern code semantic similarity.

[0046] The 1553B data bus is a standard data bus widely used in the aerospace field, especially for aircraft communication. On this data bus, all data transmission relies on a fixed periodic time synchronization mechanism. Different communication nodes, such as remote terminals and management terminals, send and receive information synchronously at specific time intervals at the same time.

[0047] The purpose of bus cycle time synchronization similarity is to measure the similarity of time synchronization characteristics between different data transmission requests. Due to the synchronicity of the 1553B bus, the transmission time cycle characteristics exhibit certain regularities, which attackers can analyze to infer data content. By introducing bus cycle time synchronization similarity, it is possible to detect whether time synchronization conforms to a predetermined pattern, thereby identifying and countering potential attacks.

[0048] RT sub-addresses are a key component of the 1553B data bus, used to distinguish and identify specific data transmission channels on different remote terminals. Each RT device has multiple sub-addresses, each corresponding to a specific function or data channel. RT sub-address behavior similarity measures the similarity of behavior among different information transmission requests when using RT sub-addresses. By analyzing the RT sub-address behavior of requests, the security of transmission requests can be determined.

[0049] Pattern codes in the 1553B data bus protocol are used to identify specific communication operations or message types. Each pattern code represents a different type of operation, such as data transmission, diagnostic requests, and responses. Attackers may try to guess the content transmitted on the bus by analyzing the frequency or combination of pattern codes. Semantic similarity of pattern codes is determined by comparing pattern codes in data requests to see if they conform to expected logical relationships. For example, some pattern codes only appear under specific circumstances; if a request contains uncommon or semantically inconsistent pattern codes, an abnormal request is identified.

[0050] Furthermore, when the multiplexed data bus is a 1553B data bus, the trusted noise node is deployed on an edge terminal including a specific RT address.

[0051] In specific applications of the 1553B data bus, trusted noise nodes are deployed at edge terminals with specific RT addresses. These edge terminals, located at both ends of the data transmission link, are critical nodes for sending and receiving data. They have important communication tasks and handle sensitive data flows. A specific RT address refers to a specific remote terminal address on the 1553B bus, representing a particular device or system. When deploying trusted noise nodes, selecting a specific RT address as the interference source is to precisely target critical data flows and reduce the likelihood of attackers analyzing the data through precise interference. For example, if communication at one end is high-risk, such as transmitting critical commands or sensitive data, noise injection can be concentrated on that specific RT address, increasing the difficulty for attackers to decode the traffic through traffic analysis.

[0052] Furthermore, the method for securely transmitting the twin noise signal through interference based on the noise transmission protocol also includes:

[0053] The collision detection module performs transmission quality collision detection on the twin noise signal before and after the interference secure transmission process, and obtains the transmission quality index difference, which includes the transmission error rate difference, end-to-end delay difference, and message loss rate difference. When the transmission quality index difference is greater than a preset threshold, the twin noise signal is collision optimized before interference secure transmission.

[0054] The collision detection module is used to detect the transmission quality before and after interference, assess the potential problems introduced by the interference, and obtain the following transmission quality index differences: the transmission bit error rate difference indicates the increase in bit error rate introduced by the interference signal. The transmission bit error rate is used to measure the error rate during transmission. The higher the bit error rate, the worse the transmission quality. The end-to-end delay difference indicates whether the transmission delay caused by noise interference has increased. The end-to-end delay is the time delay from the data sender to the receiver. Interference signals may cause the delay to increase, affecting real-time performance. The message loss rate difference indicates the change in message loss after interference. The message loss rate is used to measure the number of messages lost during transmission. Message loss will lead to incomplete or lost data.

[0055] When the transmission quality index difference exceeds a preset threshold, a conflict optimization process is initiated. The goal is to reduce quality loss caused by interference while ensuring data transmission still meets real-time and integrity requirements. Specifically, based on the transmission quality index difference, parameters such as the noise's spectral shape, power, and duty cycle are optimized to reduce noise interference with the normal data stream. For example, the noise intensity is reduced or the noise injection time window is adjusted to minimize its impact on the data. After conflict optimization is complete, interference-secure transmission resumes. During this process, the optimized noise signal is re-injected into the data stream, ensuring effective protection of transmission quality and security. This process is dynamic; the conflict detection module continuously monitors the transmission quality index and optimizes noise parameters as needed to address different network conditions and security requirements.

[0056] Example 2, based on the same inventive concept as the information security transmission method of a multiplexed data bus in the foregoing examples, such as... Figure 2 As shown in the figure, this application provides an information security transmission system for a multiplexed data bus, the system comprising:

[0057] A trusted noise node deployment module 10 is used to deploy trusted noise nodes on a multiplexed data bus; a twin transmission feature vector acquisition module 20 is used to read the transmission feature vector of an information transmission request, input the transmission feature vector into a generative twin network for analysis, and obtain a twin transmission feature vector, wherein the generative twin network includes predefined similarity items and preset similarity thresholds corresponding to each similarity item; an interference-secure transmission module 30 is used to send the twin transmission feature vector to the trusted noise node for processing to obtain a twin noise signal and a noise transmission protocol, and when the multiplexed data bus executes the information transmission request, it performs interference-secure transmission of the twin noise signal based on the noise transmission protocol.

[0058] Furthermore, the multiplexed data bus includes a security policy manager, which includes a level-similarity mapping table; identifies the execution function of the information transmission request, outputs the security requirement level of the information transmission request according to the execution function; reads the transmission feature vector of the information transmission request, maps similarity items in the level-similarity mapping table according to the security requirement level of the information transmission request and the transmission feature vector, and outputs the corresponding similarity items and the preset similarity threshold corresponding to each similarity item.

[0059] Furthermore, the trusted noise node deployment module 10 is used to perform the following operation steps:

[0060] Define a set of similarity items, including temporal similarity, spectral similarity, frame length similarity, message interval distribution similarity, load fluctuation similarity, and protocol semantic similarity; obtain historical information transmission request samples and security requirement level label samples of the historical information transmission request samples; collect transmission feature vector samples of the historical information transmission request samples; optimize the set of similarity items based on the security requirement level label samples and the transmission feature vector samples to construct the level-similarity mapping table.

[0061] Furthermore, the trusted noise node deployment module 10 is used to perform the following operation steps:

[0062] Based on the security requirement level label samples and the transmission feature vector samples, a security contribution analysis is performed on the similarity item set to obtain a security contribution set; the security contribution set is analyzed to obtain the corresponding identifier similarity item under each security requirement level label, wherein the identifier similarity item is a similarity item whose security contribution is greater than a preset contribution threshold; the initial similarity threshold of the identifier similarity item is initialized; the objective function is optimized according to the initial similarity threshold of the identifier similarity item to obtain the optimal solution of the similarity threshold of the identifier similarity item; based on the mapping relationship between the security requirement level label samples and the optimal solution of the similarity threshold of the identifier similarity item, the level-similarity mapping table is constructed.

[0063] Furthermore, the twin transmission feature vector acquisition module 20 is used to perform the following operation steps:

[0064] The generative twin network includes a generator and a discriminator. The generator is a conditional generator based on a variational encoder. The transmission feature vector is input into the generator in the generative twin network to obtain a set of candidate transmission feature vectors based on similarity terms. The discriminator is used to calculate the similarity between each candidate transmission feature vector in the set of candidate transmission feature vectors and the transmission feature vector to obtain candidate transmission feature vectors that satisfy a preset similarity threshold for each similarity term as twin transmission feature vectors.

[0065] Furthermore, the twin transmission feature vector is sent to the trusted noise node for processing to obtain a twin noise signal; wherein the twin noise signal includes multiple noise segments, and each noise segment includes an injection start and end time window, an injection node list, an injection power, and injection spectral shape and duty cycle parameters.

[0066] Furthermore, when the multiplexed data bus is a 1553B data bus, the similarity item set also includes bus cycle time synchronization similarity, RT sub-address behavior similarity, and pattern code semantic similarity.

[0067] Furthermore, when the multiplexed data bus is a 1553B data bus, the trusted noise node is deployed on an edge terminal including a specific RT address.

[0068] Furthermore, the interference secure transmission module 30 is used to perform the following operational steps:

[0069] The collision detection module performs transmission quality collision detection on the twin noise signal before and after the interference secure transmission process, and obtains the transmission quality index difference, which includes the transmission error rate difference, end-to-end delay difference, and message loss rate difference. When the transmission quality index difference is greater than a preset threshold, the twin noise signal is collision optimized before interference secure transmission.

[0070] Through the foregoing detailed description of an information security transmission method for a multiplexed data bus, those skilled in the art can clearly understand that this embodiment provides an information security transmission system for a multiplexed data bus. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant details can be found in the method section.

[0071] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for secure information transmission using a multiplexed data bus, characterized in that, The method includes: Deploy trusted noise nodes on a multiplexed data bus; Read the transmission feature vector of the information transmission request, input the transmission feature vector into the generative twin network for analysis, and obtain the twin transmission feature vector. The generative twin network includes a predefined similarity item and a preset similarity threshold corresponding to each similarity item. The twin transmission feature vector is sent to the trusted noise node for processing to obtain the twin noise signal and the noise transmission protocol. When the multiplexed data bus executes the information transmission request, the twin noise signal is subjected to interference-secure transmission based on the noise transmission protocol. The step of inputting the transmission feature vector into a generative Siamese network for analysis to obtain the Siamese transmission feature vector includes: The generative twin network includes a generator and a discriminator, wherein the generator is a conditional generator based on a variational encoder. The transmission feature vector is input into the generator in the generative Siamese network to obtain a set of candidate transmission feature vectors based on similarity terms. The discriminator is used to calculate the similarity between each candidate transmission feature vector in the set of candidate transmission feature vectors and the transmission feature vector to obtain candidate transmission feature vectors that satisfy the preset similarity threshold corresponding to each similarity term as Siamese transmission feature vectors.

2. The information security transmission method for a multiplexed data bus as described in claim 1, characterized in that, The multiplexed data bus includes a security policy manager, which includes a level-similarity mapping table. Identify the execution function of the information transmission request, and output the security requirement level of the information transmission request according to the execution function; Read the transmission feature vector of the information transmission request, map the similarity items in the level-similarity mapping table according to the security requirement level of the information transmission request and the transmission feature vector, and output the corresponding similarity items and the preset similarity threshold for each similarity item.

3. The information security transmission method for a multiplexed data bus as described in claim 2, characterized in that, Methods for constructing a rank-similarity mapping table include: Define a set of similarity items, which includes temporal similarity, spectral similarity, frame length similarity, message interval distribution similarity, load fluctuation similarity, and protocol semantic similarity; Obtain historical information transmission request samples and security requirement level label samples of the historical information transmission request samples; Collect transmission feature vector samples of the historical information transmission request samples; The similarity item set is optimized and learned based on the security requirement level label samples and the transmission feature vector samples to construct the level-similarity mapping table.

4. The information security transmission method for a multiplexed data bus as described in claim 3, characterized in that, include: Based on the security requirement level label samples and the transmission feature vector samples, a security contribution analysis is performed on the similarity item set to obtain a security contribution set; The security contribution set is analyzed to obtain the corresponding identification similarity item under each security requirement level label, wherein the identification similarity item is a similarity item whose security contribution is greater than a preset contribution threshold; Initialize the initial similarity threshold of the identifier similarity item; The objective function is optimized according to the initial similarity threshold of the identifier similarity item to obtain the optimal solution of the similarity threshold of the identifier similarity item; Based on the mapping relationship between the security requirement level label samples and the similarity threshold optimal solution of the identifier similarity item, the level-similarity mapping table is constructed.

5. The information security transmission method for a multiplexed data bus as described in claim 1, characterized in that, The twin transmission feature vector is sent to the trusted noise node for processing to obtain the twin noise signal; The twin noise signal includes multiple noise segments, each of which includes an injection start and end time window, an injection node list, an injection power, and injection spectral shape and duty cycle parameters.

6. The information security transmission method for a multiplexed data bus as described in claim 3, characterized in that, When the multiplexed data bus is a 1553B data bus, the similarity item set also includes bus cycle time synchronization similarity, RT sub-address behavior similarity, and pattern code semantic similarity.

7. The information security transmission method for a multiplexed data bus as described in claim 6, characterized in that, When the multiplexed data bus is a 1553B data bus, the trusted noise node is deployed on an edge terminal that includes a specific RT address.

8. The information security transmission method for a multiplexed data bus as described in claim 1, characterized in that, The method for securely transmitting the twin noise signal through interference based on the aforementioned noise transmission protocol also includes: The collision detection module performs transmission quality collision detection on the twin noise signal before and after the interference secure transmission process, and obtains the transmission quality index difference, which includes the transmission error rate difference, end-to-end delay difference, and message loss rate difference. When the difference in the transmission quality index is greater than a preset threshold, the twin noise signal is subjected to conflict optimization before interference-secure transmission.

9. An information security transmission system for a multiplexed data bus, characterized in that, A method for secure information transmission via a multiplexed data bus according to any one of claims 1-8, the system comprising: A trusted noise node deployment module is used to deploy trusted noise nodes on a multiplexed data bus. The twin transmission feature vector acquisition module is used to read the transmission feature vector of the information transmission request, input the transmission feature vector into the generative twin network for analysis, and obtain the twin transmission feature vector. The generative twin network includes predefined similarity items and preset similarity thresholds corresponding to each similarity item. The interference secure transmission module is used to send the twin transmission feature vector to the trusted noise node for processing to obtain the twin noise signal and the noise transmission protocol. When the multiplexed data bus executes the information transmission request, it performs interference secure transmission of the twin noise signal based on the noise transmission protocol.

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