Information security transmission method and system of multiplexing data bus

Abstract

The invention provides an information security transmission method and system for a multiplexing data bus, and relates to the technical field of information security, and the method comprises the steps: deploying a trusted noise node on the multiplexing data bus; a transmission feature vector of the information transmission request is read, the transmission feature vector is input into a generative twinning network for analysis, a twinning transmission feature vector is obtained, and the generative twinning network comprises a predefined similarity item and a corresponding preset similarity threshold value; and sending the information to a trusted noise node for processing to obtain a twin noise signal and a noise transmission protocol, and when an information transmission request is executed, performing interference security transmission on the twin noise signal based on the noise transmission protocol. The technical problem that in the prior art, a multiplexing data bus usually uses a static protection mechanism, the protection means is single, complex attacks are difficult to cope with, and the information transmission safety is insufficient is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information security, and particularly relates to an information security transmission method and system of a multiplex data bus. BACKGROUND

[0002] The multiplex data bus is widely used in key fields such as aviation and aerospace, and is used for information transmission between multiple devices. However, the traditional multiplex data bus has significant problems in the security of information transmission. In the prior art, the multiplex data bus usually uses a static protection mechanism, such as encryption and access control. Although these protection measures can protect information to a certain extent, once the attacker obtains the traffic data of the bus, it is still possible to extract and identify the real data content through traffic analysis, which leads to insufficient security in the information transmission process. SUMMARY

[0003] The present application provides an information security transmission method and system of a multiplex data bus, which aims to solve the technical problem that the multiplex data bus in the prior art usually uses a static protection mechanism, the protection means is single, it is difficult to cope with complex attacks, and the security of information transmission is insufficient.

[0004] The first aspect of the present application provides an information security transmission method of a multiplex data bus, which comprises: deploying a trusted noise node on the multiplex data bus; reading a 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, the generative twin network comprising a predefined similarity term and a preset similarity threshold 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 multiplex data bus executes the information transmission request, interfering with the twin noise signal based on the noise transmission protocol for secure transmission.

[0005] In a second aspect of the present application, a secure information transmission system for a multiplexed data bus is provided. The system is used in the secure information transmission method for a multiplexed data bus. The system comprises: a trusted noise node deployment module configured to deploy a trusted noise node on the multiplexed data bus; a twin transmission feature vector acquisition module configured to read a transmission feature vector of an information transmission request, input the transmission feature vector into a generative twin network for analysis, and acquire a twin transmission feature vector. The generative twin network comprises predefined similarity terms and a preset similarity threshold corresponding to each similarity term; and an interference secure transmission module configured 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. When the multiplexed data bus executes the information transmission request, the noise transmission protocol is used to perform interference secure transmission on the twin noise signal.

[0006] The one or more technical solutions provided in the present application have at least the following beneficial effects: By deploying a trusted noise node on the multiplexed data bus, the security of data transmission is effectively increased. The trusted noise node generates a noise signal to ensure that an attacker cannot accurately identify a real data stream through time correlation analysis and other means. This active interference method changes the system from traditional passive protection to active deception, greatly improving the ability to defend against advanced persistent threats. The generative twin network is used to analyze the transmission feature vector and generate a corresponding twin transmission feature vector, effectively disguising the data transmission process. By carefully analyzing the transmission features and combining them with similarity terms, the generated twin features are similar to but different from the real data, thereby increasing the security of the information and making it difficult for attackers to extract real information from the data stream features. After the twin transmission feature vector is sent to the trusted noise node, the generated twin noise signal is transmitted through the noise transmission protocol for interference secure transmission. The noise signal effectively interferes with the attacker's traffic analysis, making it difficult for the data to be easily identified or recovered during transmission. By precisely controlling the injection timing and strength of the noise, the transmission security and data reliability are ensured.

[0007] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the following detailed description of the embodiments of the present application can be implemented in accordance with the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following detailed description of the embodiments of the present application is provided. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 A secure information transmission method for a multiplexed data bus is provided.

[0009] Figure 2 A schematic diagram of an information security transmission system structure of a multiplex data bus provided by an embodiment of the present application.

[0010] Reference signs: trusted noise node deployment module 10, twin transmission feature vector acquisition module 20, interference secure transmission module 30. DETAILED DESCRIPTION

[0011] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object of the application, the specific embodiments, structures, features and effects according to the present application are described in detail below in combination with the drawings and preferred embodiments.

[0012] Embodiment one, as shown in the present application, a multiplex data bus information security transmission method is provided, the method comprises: Figure 1 Deploying a trusted noise node on a multiplex data bus. The trusted noise node is used to inject noise signals into the bus, which appear random and irregular externally, but they can be managed and controlled through a specific protocol to avoid interfering with effective data transmission, the purpose is to disrupt the correlation between real data and noise, so that it is difficult for attackers to recover real data by analyzing data flow, time characteristics and other information. Deploy multiple trusted noise nodes at different nodes of the multiplex data bus to enhance the randomness of interference, and the deployment position is the edge terminal or intermediate node of data transmission, ensuring that the noise signal covers the critical path of the data stream. The noise signal has randomness and unpredictability, but its generation and injection process is controlled by the trusted noise node, ensuring that the difficulty of analysis by attackers is maximized without damaging the system function.

[0013] The transmission feature vector of the read information transmission request is input into a generative twin network for analysis to obtain a twin transmission feature vector, and the generative twin network includes a predefined similarity term and a preset similarity threshold value corresponding to each similarity term.

[0014] The transmission feature vector of the read information transmission request is input into a generative twin network for analysis to obtain a twin transmission feature vector, and the generative twin network includes a predefined similarity term and a preset similarity threshold value corresponding to each similarity term.

[0015] When receiving an information transmission request, first extract the transmission feature vector of the request, which includes but is not limited to timing information, spectral characteristics, frame length, message interval, etc., which can reflect the transmission law and properties of the data. The generative twin network includes a generator and a discriminator, which uses the generator to generate a twin transmission feature vector similar to the input feature vector but slightly changed, and the discriminator detects the similarity of the candidate vector to ensure that the generated twin transmission feature vector meets the similarity requirement with the original transmission feature vector, that is, it is not distorted but has a deceptive nature. The generative twin network contains multiple similarity terms, such as timing and spectrum, and each similarity term corresponds to a preset similarity threshold. The generator generates a twin transmission feature vector based on the preset similarity threshold, and the discriminator is used for screening to ensure that the generated feature vector is consistent with the original request in these similarity terms. In the process of generating the feature vector, the network adjusts the output of the generator according to the threshold requirement of each feature item to achieve the ideal deception effect. The twin here refers to the high similarity between the transmission feature vectors, making it difficult for attackers to distinguish real data from bus traffic.

[0016] The twin transmission feature vector is sent 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, the twin noise signal is interfered with based on the noise transmission protocol for safe transmission.

[0017] The twin transmission feature vector is transmitted to the trusted noise node, which provides the twin feature vector that has been processed and meets the protocol requirements to the trusted noise node. The trusted noise node generates a twin noise signal based on the input twin transmission feature vector. The twin noise signal is designed to be independent of the original data stream but has an interference effect. The structure of the twin noise signal includes multiple noise segments, each with start and end time, spectral characteristics, power, and other parameters. These parameters work together to ensure that the noise maximizes its impact on bus traffic. To ensure effective interference of the twin noise signal while avoiding affecting the transmission of normal data, the trusted noise node processes and injects the signal according to the noise transmission protocol, which ensures that the twin noise signal does not damage the data stream or the normal operation of the system during transmission. The protocol involves the time, frequency, and location of noise injection; noise intensity and signal morphology; noise periodicity and duty cycle.

[0018] In the execution of the information transmission request, the multiplexed data bus injects noise according to a specific time window and spectral distribution according to the noise transmission protocol, ensuring the security of data transmission. Through these interferences, it is difficult for attackers to extract or recover the effective transmission content through traditional time correlation analysis and other methods. By introducing a trusted noise node and a fraudulent design of twin transmission feature vectors, the system shifts from passive protection to active deception, effectively preventing attacks by advanced persistent threats through traffic analysis.

[0019] Further, the multiplexed data bus includes a security policy manager, which includes a grade-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, and maps the similarity items in the grade-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 value corresponding to each similarity item.

[0020] The security policy manager is a core component for managing and configuring information transmission security. Its role is to identify and evaluate the security requirement level of the information transmission request, and to take appropriate security protection measures according to 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 data. These levels will affect the subsequent transmission feature analysis and the selection of noise interference strategies. The grade-similarity mapping table is an internal data structure of the security policy manager, which contains the mapping relationship between different security requirement levels of the transmission request and the similarity items. The role of the mapping table is to define similarity items such as timing similarity, spectral similarity, and frame length similarity for each transmission request according to different security requirement levels, as well as their corresponding preset similarity thresholds.

[0021] The execution function of the information transmission request can be data transmission, control signal, diagnostic signal, etc. The identification of the execution function of the request is based on the type of request to determine its security requirements. According to the execution function, the corresponding security requirement level is output. The security requirement level is determined by analyzing the following factors: data sensitivity, high sensitivity data requires higher security; system role, certain specific system roles require higher security, such as master nodes and core modules; criticality of execution function, for example, requests involving core data transmission often require high security levels to prevent tampering.

[0022] The transmission feature vector describes parameters such as time sequence, frequency spectrum, frame length, etc. of the data. The transmission feature vector is read to capture the traffic pattern of the request and help subsequent processing to identify the behavior characteristics of the data stream. According to the security requirement level and the transmission feature vector, the similarity item is mapped in the level-similarity mapping table. Different security requirement levels guide the system to focus on different feature items. According to the mapping result, a set of similarity items are output, such as time sequence similarity, frequency spectrum similarity, load fluctuation similarity, etc. Each similarity item corresponds to a preset similarity threshold. These thresholds represent the tolerance range of each similarity item. The generated twin network will generate twin transmission feature vectors according to these thresholds to ensure that their similarity with the original data is within the allowed range.

[0023] Further, the method for constructing the level-similarity mapping table comprises: Defining a set of similarity items, including time sequence similarity, frequency spectrum similarity, frame length similarity, message interval distribution similarity, load fluctuation similarity, and protocol semantic similarity; obtaining historical information transmission request samples and security requirement level label samples of the historical information transmission request samples; collecting transmission feature vector samples of the historical information transmission request samples; optimizing learning the set of similarity items according to the security requirement level label samples and the transmission feature vector samples, and constructing the level-similarity mapping table.

[0024] The time sequence similarity describes the transmission pattern of the data packet on the time axis. By analyzing the time interval and time sequence relationship of the data packet, the similarity of the transmission feature is judged. For example, if the transmission time difference of two data packets is small, it means that they belong to similar transmission streams. The frequency spectrum similarity describes the distribution of the transmission data in the frequency spectrum. The frequency spectrum characteristic is an important dimension for evaluating the similarity of the transmission content. Transmission with high similarity often shows similar frequency spectrum distribution. The frame length similarity describes the size or length of the transmission data packet. If the lengths of two data packets are very similar, it can be considered that they are similar in structure. The message interval distribution similarity is used to analyze whether the interval between data packets has regularity or similarity. Attackers may infer the data transmission content by analyzing the distribution of the data packet interval. Maintaining the similarity of the interval can enhance the confidentiality of the transmission. The load fluctuation similarity describes the volatility of the transmission load within a period of time. Normal data streams often show regular fluctuations, while malicious data streams have abnormal fluctuation patterns. The protocol semantic similarity is used to analyze whether the semantic level of the transmission protocol is consistent. The semantic difference of different protocols will affect the transmission characteristics of the data.

[0025] Collect historical information transmission request samples from actual systems or simulated environments, which include various types of data transmission, involving different application scenarios, transmission modes, etc. Each historical information transmission request sample has a corresponding security requirement level label, which indicates the security requirement level of the request, which can be different levels such as low, medium, and high. The determination of the label is based on the nature, sensitivity, and functions performed by the request. The labeled security requirement level label can be determined by an expert system, manual rules, or analysis based on historical events.

[0026] Transmission feature vector samples are obtained through packet analysis, which specifically includes 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.

[0027] Based on the collected transmission feature vector samples and security requirement level label samples, the importance of each similarity term under different security requirement levels is learned. Algorithms such as support vector machines, neural networks, and decision trees can be used to gradually adjust the weight of each similarity term in the mapping table through learning from historical data. Through training, the parameters of the model are optimized to give a reasonable evaluation of each similarity term under each security requirement level. During the learning process, the security contribution of each similarity term is analyzed to evaluate its role in improving system security, which 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 corresponding similarity terms and similarity thresholds for each security requirement level.

[0028] Further, it includes: According to the security requirement level label sample and the transmission feature vector sample, the security contribution of the similarity term set is analyzed to obtain a security contribution set; the security contribution set is analyzed to obtain the corresponding identified similarity term under each security requirement level label, wherein the identified similarity term is a similarity term with a security contribution greater than a preset contribution threshold; the initial similarity threshold of the identified similarity term is initialized; the objective function is optimized according to the initial similarity threshold of the identified similarity term to obtain the similarity threshold optimal solution of the identified similarity term; and the level-similarity mapping table is constructed according to the mapping relationship between the security requirement level label sample and the similarity threshold optimal solution of the identified similarity term.

[0029] For each transmission request sample, combined with its transmission feature vector and security requirement level label, how the similarity term affects the security of the system can be evaluated, and a model can be trained to quantify the contribution of each similarity term, such as using regression analysis, decision tree, etc. to analyze the influence of each similarity term on the security requirement level. The security contribution can be calculated based on the weighted importance of the similarity term, for example, the timing similarity, the spectral similarity, the frame length similarity, etc. have different weights for different levels of security requirements, and the model obtained by training outputs the security contribution of each similarity term to the transmission request. By analyzing all the transmission request samples, a complete security contribution set is obtained, which records the contribution of each similarity term under different security requirement levels.

[0030] For each security requirement level label, the contribution values of each similarity term in the security contribution set are analyzed, and the security contribution of each similarity term is compared with a preset contribution threshold. If the contribution of a certain similarity term is greater than the threshold, it is determined to be the identified similarity term under that security requirement level. For each security requirement level, these identified similarity terms play a core role in subsequent twin transmission feature generation and security interference transmission.

[0031] For each identified similarity term, an initial similarity threshold is set to determine whether the transmission features are similar enough to meet the security requirement standard. If the timing difference between two transmission features is greater than the threshold, they are considered not similar. The initial similarity threshold can be analyzed from historical transmission data or estimated through the optimization process of the model. Generally, a relatively conservative threshold can be set first, and then adjusted through continuous optimization to adapt to different types of transmission requests.

[0032] The objective function combines multiple factors such as security, transmission quality, system efficiency, etc. The optimization goal is to find a balance point that can provide sufficient security without excessive interference with normal data transmission. In the optimization process, if the objective function is continuous and derivable, gradient descent can be used to minimize the loss while maximizing security; if the objective function is complex and has no explicit derivative, genetic algorithm is used to simulate the biological evolution process to find the optimal solution. In each optimization process, the similarity threshold is continuously adjusted until the similarity threshold that maximizes the objective function value is found. In this process, the optimization goal is to minimize the impact on normal transmission while ensuring system security. Finally, through the optimization algorithm, the similarity threshold optimal solution of the identified similarity term is obtained, which represents the most suitable value of different similarity terms under different security requirement levels.

[0033] The mapping table is constructed by analyzing the relationship between the security requirement level label samples obtained during the training process and the optimal solution of the similarity threshold, that is, for each security requirement level, the optimal similarity threshold solution is selected and bound with the corresponding similarity term, and the mapping relationship ensures that the similarity standard can be dynamically adjusted according to the security requirement during information transmission.

[0034] Further, the transmission feature vector is input into the generative twin network for analysis to obtain a twin transmission feature vector, including: The generative twin network includes a generator and a discriminator, and 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 candidate transmission feature vector set corresponding to the similarity term; the discriminator is used to calculate the similarity between each candidate transmission feature vector in the candidate transmission feature vector set and the transmission feature vector to obtain a candidate transmission feature vector satisfying a preset similarity threshold corresponding to each similarity term as a twin transmission feature vector.

[0035] The generative twin network is a network structure composed of a generator and a discriminator, which is different from the standard generative adversarial network. The generative twin network focuses on generating a transmission feature vector similar to the input feature but subjected to interference processing to increase the security of transmission. The function of the generator is to generate a set of candidate transmission feature vectors according to the input transmission feature vector. This generation process is conditional, that is, according to the specific value of the transmission feature vector and the similarity term, the corresponding twin feature vector is generated. The generator adopts a structure based on a variational encoder. The variational encoder is a generative model that compresses the input transmission feature vector into the distribution of the latent space through the encoder, and then generates a transmission feature vector similar to it through the decoder. The advantage of the variational encoder is that it can generate smoother and more diverse feature vectors, avoiding the pattern collapse problem that may occur in traditional generative models.

[0036] The generator generates a set of candidate transmission feature vectors based on the input transmission feature vector, each with a different perturbation, thereby enhancing the privacy and security of the data. The discriminator performs similarity calculations on each candidate feature vector in the set of candidate transmission feature vectors, based on specific similarity terms such as temporal similarity, spectral similarity, frame length similarity, etc., which measure the degree of similarity between the candidate transmission feature vector and the original feature vector in different dimensions. Each similarity term has a pre-set similarity threshold, and if the similarity between the candidate feature vector and the original transmission feature vector exceeds the threshold, the candidate feature vector is considered an effective twin feature vector, otherwise the discriminator excludes it. During the discriminator screening process, only those candidate transmission feature vectors that meet the pre-set similarity threshold of all similarity terms can be selected as the final twin transmission feature vector, which is used in the subsequent secure jamming transmission phase.

[0037] Further, 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, each noise segment including an injection start and end time window, an injection node list, an injection power, an injected spectral shape, and a duty cycle parameter.

[0038] When the twin transmission feature vector is generated, it is transmitted to the trusted noise node deployed in the bus. The trusted noise node performs noise processing on the twin transmission feature vector, including injecting noise into the transmission signal to make it more difficult for attackers to restore 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. Each noise segment introduces appropriate perturbations during transmission to make the data stream appear more chaotic, thereby making it difficult for attackers to easily distinguish between real data and noise.

[0039] The injection start and end time window is used to specify the duration of noise injection, by controlling the injection timing of noise, the data stream can be more flexible to interfere, so as to avoid being identified by the attacker through time characteristic analysis to avoid normal transmission mode; The injection node list specifies which nodes the noise will be injected into, such as intermediate equipment, terminal equipment in the data transmission link, or edge nodes on the data bus, etc., to cover different links on the transmission path and enhance the interference effect; The injection power is a parameter that determines the interference strength, the power setting should be strong enough to achieve the interference purpose, but not too strong to affect the normal transmission of data or produce abnormal network behavior; The injected frequency spectrum shape determines the distribution of noise in the frequency domain, the frequency spectrum shape can be designed as wideband noise or narrowband noise according to the need, and can be adjusted according to the transmitted data characteristics to ensure the effectiveness of the interference; The duty cycle parameter defines the switching period of the noise signal, that is, the time ratio of the high level and 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.

[0040] Further, when the multiplexed data bus is a 1553B data bus, the set of similarity terms further includes a bus cycle time synchronization similarity, an RT sub-address behavior similarity, and a mode code semantic similarity.

[0041] The 1553B data bus is a standard data bus widely used in the field of aerospace, especially for communication of aircraft, on which all data transmission relies on a fixed cycle time synchronization mechanism, and different communication nodes, such as remote terminals, management terminals, etc., synchronously send and receive information at specific time intervals at the same time.

[0042] The bus cycle time synchronization similarity measures the similarity of the time synchronization characteristics between different data transmission requests. Due to the synchronization of the 1553B bus, the time period characteristics of the transmission have certain regularity, and the attacker can infer the data content by analyzing these regularities. By introducing the bus cycle time synchronization similarity, it can be detected whether the time synchronization conforms to the predetermined mode to identify and resist potential attacks.

[0043] The RT sub-address is 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. The RT sub-address behavior similarity measures the similarity of the behavior of different information transmission requests when using the RT sub-address. By analyzing the RT sub-address behavior of the request, the security of the transmission request can be judged.

[0044] Mode codes are used in the 1553B data bus protocol to identify specific communication operations or message types, each mode code represents a different type of operation, such as data transmission, diagnostic request, response, etc., attackers may guess the content of the transmission on the bus by analyzing the frequency or combination of mode codes. Mode code semantic similarity is to compare the mode codes in the data request and judge whether they meet the expected logical relationship, for example, certain mode codes only appear in certain situations, if a request contains an unusual or semantically incorrect mode code, it is identified as an abnormal request.

[0045] Further, when the multiplexed data bus is a 1553B data bus, the trusted noise node is deployed at the edge terminal including a specific RT address.

[0046] In the specific application of 1553B data bus, the trusted noise node is deployed at the edge terminal including a specific RT address, the edge terminal is located at both ends of the data transmission link and is the key node for sending and receiving data, these nodes have important communication tasks and sensitive data flow, the specific RT address refers to a specific remote terminal address on the 1553B bus, representing a specific device or system, when deploying the trusted noise node, the specific RT address is selected as the interference source to accurately target the key data flow and reduce the possibility of data analysis by attackers through precise interference. For example, if the communication at one end is at high risk, such as transmitting critical commands or sensitive data, noise can be injected to this specific RT address to increase the difficulty of decoding the traffic through traffic analysis by attackers.

[0047] Further, based on the noise transmission protocol, the twin noise signals are subjected to interference-safe transmission, which further includes: The transmission quality conflict detection module is used to detect the transmission quality conflict before and after the twin noise signals are subjected to interference-safe transmission, and obtain the transmission quality index difference, including the transmission error rate difference, the end-to-end delay difference and the packet loss rate difference; when the transmission quality index difference is greater than the preset threshold, the twin noise signals are subjected to conflict optimization and then subjected to interference-safe transmission.

[0048] The conflict detection module is used to detect the transmission quality before and after the interference transmission, evaluate the potential problems introduced by the interference, and obtain the following transmission quality index difference: the transmission error rate difference indicates the error rate increment introduced by the interference signal, the transmission error rate is used to measure the error rate in the transmission process, the higher the 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, the interference signal can cause the delay to increase, affecting the real-time performance; the packet loss rate difference indicates the change of packet loss after the interference, the packet loss rate is used to measure the number of lost packets in the transmission process, and the packet loss will cause the incompleteness or loss of data.

[0049] When the transmission quality index difference exceeds the set preset threshold value, the conflict optimization process is started, the goal is to reduce the quality loss caused by the interference, and to ensure that the data transmission still meets the requirements of real-time performance and integrity. Specifically, according to the transmission quality index difference, the parameters such as the spectral shape, power, and duty cycle of the noise are optimized to reduce the interference of the noise on the normal data stream, for example, the strength of the noise is reduced or the injection time window of the noise is adjusted to reduce the impact on the data. After completing the conflict optimization, the interference-safe transmission is restarted, and in this process, the optimized noise signal will be re-injected into the data stream to ensure that the transmission quality and safety are effectively guaranteed. This process is a dynamic adjustment process, and the conflict detection module continuously monitors the transmission quality index and continuously optimizes the noise parameters as needed to cope with different network conditions and security requirements.

[0050] Embodiment two, based on the same inventive concept as the information security transmission method of the multiplexed data bus in the preceding embodiments, as Figure 2 shown, the embodiments of the present application provide a multiplexed data bus information security transmission system, the system comprises: A trusted noise node deployment module 10 is used to deploy a trusted noise node on a multiplexed data bus; a twin transmission feature vector acquisition module 20 is used to read a transmission feature vector of an information transmission request, input the transmission feature vector into a generative twin network for analysis, and acquire a twin transmission feature vector, the generative twin network includes a predefined similarity term and a preset similarity threshold value corresponding to each similarity term; an interference-safe 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, when the multiplexed data bus executes the information transmission request, the noise transmission protocol is used to perform interference-safe transmission on the twin noise signal.

[0051] Further, the multiplexing data bus comprises a security policy manager, the security policy manager comprises a grade-similarity mapping table; an execution function of the information transmission request is identified, a security requirement grade of the information transmission request is output according to the execution function; a transmission feature vector of the information transmission request is read, and similarity item mapping is performed in the grade-similarity mapping table according to the security requirement grade of the information transmission request and the transmission feature vector, and a corresponding similarity item and a preset similarity threshold value corresponding to each similarity item are output.

[0052] Further, the trusted noise node deployment module 10 is used to perform the following operation steps: A similarity item set is defined, the similarity item set comprises a time sequence similarity, a frequency spectrum similarity, a frame length similarity, a message interval distribution similarity, a load fluctuation similarity and a protocol semantic similarity; a historical information transmission request sample and a security requirement grade label sample of the historical information transmission request sample are obtained; a transmission feature vector sample of the historical information transmission request sample is collected; the grade-similarity mapping table is constructed by performing optimization learning on the similarity item set according to the security requirement grade label sample and the transmission feature vector sample.

[0053] Further, the trusted noise node deployment module 10 is used to perform the following operation steps: The security contribution degree analysis is performed on the similarity item set according to the security requirement grade label sample and the transmission feature vector sample, and a security contribution degree set is obtained; the security contribution degree set is analyzed, and a corresponding identification similarity item under each security requirement grade label is obtained, wherein the identification similarity item is a similarity item with a security contribution degree greater than a preset contribution degree threshold value; an initial similarity threshold value of the identification similarity item is initialized; the target function optimization is performed according to the initial similarity threshold value of the identification similarity item, and a similarity threshold value optimal solution of the identification similarity item is obtained; and the grade-similarity mapping table is constructed according to the mapping relationship between the security requirement grade label sample and the similarity threshold value optimal solution of the identification similarity item.

[0054] Further, the twin transmission feature vector acquisition module 20 is used to perform the following operation steps: The generative twin network comprises 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, a candidate transmission feature vector set corresponding to the similarity item is obtained, the discriminator is used to perform similarity calculation on each candidate transmission feature vector in the candidate transmission feature vector set and the transmission feature vector, and a candidate transmission feature vector satisfying a preset similarity threshold value corresponding to each similarity item is obtained as a twin transmission feature vector.

[0055] Further, 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 a plurality of noise segments, and each noise segment includes an injection start-stop time window, an injection node list, an injection power, an injected spectral shape, and a duty cycle parameter.

[0056] Further, when the multiplexed data bus is a 1553B data bus, the set of similarity terms further includes a bus cycle time synchronization similarity, an RT subaddress behavior similarity, and a mode code semantic similarity.

[0057] Further, when the multiplexed data bus is a 1553B data bus, the trusted noise node is deployed at an edge terminal including a specific RT address.

[0058] Further, the interference-safe transmission module 30 is configured to perform the following operation steps: The transmission quality conflict detection module is configured to perform transmission quality conflict detection on the twin noise signal before and after the interference-safe transmission process, to obtain a transmission quality index difference value, including a transmission bit error rate difference value, an end-to-end delay difference value, and a packet loss rate difference value; and when the transmission quality index difference value is greater than a preset threshold value, the twin noise signal is subjected to conflict optimization before the interference-safe transmission.

[0059] The foregoing detailed description of a multiplexed data bus information security transmission method enables those skilled in the art to clearly understand a multiplexed data bus information security transmission system in the present embodiment. Since the system corresponds to the disclosed method, it is described simply, and the relevant parts are described in the method section.

[0060] The above description is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as the above preferred embodiment, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, without departing from the technical solution of the present application. Any modification, equivalent change, and modification of the above embodiments based on the technical essence of the present application are still within the scope of the technical solution of the present application.

Claims

1. A method for secure transmission of information on a multiplexed data bus, characterized in that, The method comprises: Deploying a trusted noise node on a multiplex data bus; Reading a 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 comprises a predefined similarity term and a preset similarity threshold 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 performing interference secure transmission on the twin noise signal based on the noise transmission protocol when the multiplex data bus executes the information transmission request.

2. The method for secure transmission of information on a multiplexed data bus of claim 1, wherein, The multiplex data bus comprises a security policy manager, and the security policy manager comprises a grade-similarity mapping table; Identifying an execution function of the information transmission request, and outputting a security requirement grade of the information transmission request according to the execution function; Reading a transmission feature vector of an information transmission request, and mapping a similarity term in the grade-similarity mapping table according to the security requirement grade of the information transmission request and the transmission feature vector, and outputting the corresponding similarity term and the preset similarity threshold corresponding to each similarity term.

3. The method for secure transmission of information on a multiplexed data bus of claim 2, wherein, The method for constructing the grade-similarity mapping table comprises: Defining a similarity term set, wherein the similarity term set comprises a time sequence similarity, a frequency spectrum similarity, a frame length similarity, a message interval distribution similarity, a load fluctuation similarity, and a protocol semantic similarity; Obtaining historical information transmission request samples and security requirement grade label samples of the historical information transmission request samples; Collecting transmission feature vector samples of the historical information transmission request samples; Optimizing learning the similarity term set according to the security requirement grade label samples and the transmission feature vector samples, and constructing the grade-similarity mapping table.

4. The method for secure transmission of information on a multiplexed data bus of claim 3, wherein, Comprise: Performing security contribution degree analysis on the similarity term set according to the security requirement grade label samples and the transmission feature vector samples, and obtaining a security contribution degree set; Analyzing the security contribution degree set to obtain a corresponding identification similarity term under each security requirement grade label, wherein the identification similarity term is a similarity term with a security contribution degree greater than a preset contribution degree threshold; Initializing an initial similarity threshold of the identification similarity term; Performing target function optimization according to the initial similarity threshold of the identification similarity term to obtain a similarity threshold optimal solution of the identification similarity term; Constructing the grade-similarity mapping table according to a mapping relationship between the security requirement grade label samples and the similarity threshold optimal solution of the identification similarity term.

5. The method for secure transmission of information in a multiplexed data bus as claimed in claim 1, wherein, Inputting the transmission feature vector into a generative twin network for analysis to obtain a twin transmission feature vector, comprising: The generative twin network comprises a generator and a discriminator, and the generator is a conditional generator based on a variational encoder; The transmission feature vector is input into a generator in a generative twin network to obtain a candidate transmission feature vector set corresponding to a similarity term, the discriminator is configured to perform similarity calculation on each candidate transmission feature vector in the candidate transmission feature vector set and the transmission feature vector to obtain a candidate transmission feature vector satisfying a preset similarity threshold corresponding to each similarity term as a twin transmission feature vector.

6. The method for secure transmission of information in a multiplexed data bus of claim 1, wherein, The twin transmission feature vector is sent to the trusted noise node for processing to obtain a twin noise signal. The twin noise signal includes a plurality of noise segments, and each noise segment includes an injection start and end time window, an injection node list, an injection power, an injected frequency spectrum shape, and a duty cycle parameter.

7. The method for secure transmission of information in a multiplexed data bus as claimed in claim 3 wherein, When the multiplexed data bus is a 1553B data bus, the set of similarity terms further includes a bus cycle time synchronization similarity, an RT subaddress behavior similarity, and a mode code semantic similarity.

8. The method for secure transmission of information of a multiplexed data bus according to claim 7, characterized in that, When the multiplexed data bus is a 1553B data bus, the trusted noise node is deployed at an edge terminal including a specific RT address.

9. The method for secure transmission of information in a multiplexed data bus of claim 1, wherein, The twin noise signal is subjected to interference-safe transmission based on the noise transmission protocol, and further includes: The process before and after the interference-safe transmission of the twin noise signal is subjected to transmission quality conflict detection by a conflict detection module to obtain a transmission quality index difference, and the transmission quality index difference includes a transmission error rate difference, an end-to-end delay difference, and a packet loss rate difference. When the transmission quality index difference is greater than a preset threshold, the twin noise signal is subjected to conflict optimization before being subjected to interference-safe transmission.

10. A multiplexed data bus information security transmission system, characterized by, An information security transmission method for a multiplexed data bus according to any one of claims 1-9, the system comprising: A trusted noise node deployment module for deploying a trusted noise node on a multiplexed data bus; A twin transmission feature vector acquisition module for reading a 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, the generative twin network including predefined similarity terms and a preset similarity threshold corresponding to each similarity term; An interference-safe 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, and when the multiplexed data bus executes the information transmission request, the twin noise signal is subjected to interference-safe transmission based on the noise transmission protocol.

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