A blockchain-based information security data defense method and system

Abstract

This invention discloses a blockchain-based information security data defense method and system. In the field of information security defense technology, this invention addresses the technical problem that single-protection errors can reduce data transmission security and negatively impact data transmission. Through reasonable data capacity segmentation, sensitive data identification, and data packet classification, this invention not only improves data transmission security but also adopts corresponding transmission strategies based on the characteristics of different data packets. This ensures both data security and transmission efficiency. Furthermore, it selects different transmission methods based on the characteristics of the data itself and rationally allocates network resources according to actual conditions to ensure secure and efficient data transmission, thereby improving data transmission security. Simultaneously, by replacing existing duplicate data, it further conceals the authenticity of the data content, thereby enhancing data confidentiality and integrity.

Description

Technical Field

[0001] The present invention relates to the field of information security defense technology, and specifically to a blockchain-based information security data defense method and system. Background Art

[0002] Big data, or massive data, refers to information so large that it cannot be captured, managed, processed, and organized within a reasonable timeframe using mainstream software tools. This information is crucial for businesses to make more proactive business decisions. When smart devices are in operation, information security is crucial, necessitating the use of information security defense systems.

[0003] According to a big data-based information security defense system with Chinese application number CN202210048178.8, the system includes a client, a security system platform and a background management terminal. The client is connected to the security system platform, and the security system platform is connected to the background management terminal. The security system platform includes a device connection module, a security verification module, an operation monitoring module, a status memory module, a status marking module, a status storage module, a status extraction module, a query module, a security inspection module, a data transmission module, a status comparison module, a comparison and judgment module, an early warning module, an early warning type analysis, an early warning type sorting module, a security defense module, a status tracking module and a feedback module. The present invention can monitor the operating status of the equipment and improve the safety of the equipment operation.

[0004] When some existing information security data transmission defenses are used, due to the different types and contents of data, when protecting special data from transmission, they cannot provide targeted protection based on the particularity of the data. Unified protection measures will reduce the security of data transmission and further cause data transmission security problems. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a blockchain-based information security data defense method and system, which solves the problem that a single protection error will reduce the security of data transmission and cause data transmission impact.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a blockchain-based information security data defense method, comprising:

[0007] Step 1: Collect information security data, calculate the transmission risk based on the information parameters corresponding to the information security data, and make defense judgments based on the transmission risk. The specific defense judgment method is as follows:

[0008] Acquire information security data and record it as an analysis target, then acquire information parameters of the analysis target object, where the information parameters include: information format, data capacity, and network fluctuation, and calculate the format value of the analysis target based on the acquired information format;

[0009] The information format of the target to be analyzed is recorded as the target format. At the same time, the number of transmissions C, the number of exceptions C1, and the mean value R of the format capacity corresponding to the target format in the time period t are obtained. Here, the number of exceptions (C1) specifically refers to the frequency of transmission exceptions that occur during the transmission process. In order to reveal the stability of the format, the obtained target format parameters are substituted into the formula Calculate the format value Q of the target format;

[0010] Substitute the format value Q, data capacity L, and network fluctuation B into the formula The transmission risk value W of the analysis target is calculated, and a1 and θ are both preset values, and a1 = 0.417, θ = 1.47. Then the calculated transmission risk value W is compared with the risk monitoring value Wy; specifically, through this formula, the transmission risk value (W) of the analysis target is calculated, which represents the potential risk level of data transmission through the target format under the current network environment.

[0011] When the transmission risk value W is greater than the risk monitoring value Wy, it means that there is a risk in the transmission of the analysis target, and defense processing is required, and a defense processing signal is generated. Conversely, when the transmission risk value W is less than the risk monitoring value Wy, it means that there is no risk in the transmission of the analysis target, and a transmission monitoring signal is generated.

[0012] Step 2: The generated transmission monitoring signal is monitored by monitoring network traffic and user activities, and the analysis target corresponding to the transmission monitoring signal is analyzed to generate monitoring information. The specific method of generating the monitoring information is as follows:

[0013] Leverage network security tools and technologies, such as intrusion detection systems (IDS), intrusion prevention systems (IPS), and security information and event management (SIEM) systems, to collect, analyze, and visualize network traffic and user activity data;

[0014] Based on predetermined security policies and rules, an automated alarm system is configured. Through user behavior analysis (UBA) technology, behaviors that deviate from normal patterns can be identified and corresponding monitoring information can be generated.

[0015] Step 3: Analyze the generated defense processing signal. The specific analysis method is as follows:

[0016] The transmission channel of the transmission analysis target is labeled as i, where i = 1, 2, ..., j. At the same time, the packet loss rate Ki and delay time Ti corresponding to the transmission channel are obtained. The specific delay time, also known as latency, refers to the time required for data to be transmitted from the sender to the receiver. This time includes the time it takes for the data to propagate on the physical transmission medium, the time it takes for the data to be processed in the network device, and the time it waits in the queue. The packet loss rate refers to the ratio of data packets lost during data transmission to the total number of data packets sent. Data packet loss is usually caused by network congestion, signal attenuation, or system failure, and the obtained packet loss rate Ki and delay time Ti are substituted into the formula The weight ratio Pi of transmission channel i is calculated, where α is a preset value, and α=2.17, and the transmission channels i are sorted from large to small according to the weight ratio Pi, and the weight ratio Pi is calculated to obtain a simplified ratio, and the transmission amount corresponding to the transmission channel i is obtained and recorded as Mi; and the ratio calculation of the weight ratio Pi here is specifically expressed as simplifying the ratio of all weight ratios, for example, the final ratio is 4:6:7:2:5.

[0017] The analysis target and its corresponding data capacity L are obtained, and the data capacity L of the analysis target is divided into n data packets according to the simplification ratio of the transmission channel i. Specifically, the number of data packets obtained here is equal to the sum of the simplification ratio values. At the same time, sensitive data is identified for data packet n. The data packet corresponding to the presence of sensitive data is recorded as a heavy-sense data packet n1, and the data packet without sensitive data is classified as a light-sense data packet n2. The sensitive data here includes personal identity information, intellectual property rights, trade secrets, etc.

[0018] Step 4: Then obtain all the heavy-sensing data packets n1 and perform defense analysis based on the different data in the heavy-sensing data packets. The specific analysis method is as follows:

[0019] All heavy-sensing data packets n1 are obtained, and the capacities corresponding to the sensitive data and non-sensitive data in the heavy-sensing data packet n1 are obtained, and the capacities of the two are compared. When the capacity of sensitive data is greater than that of non-sensitive data, the heavy-sensing data packet n1 is converted into a binary format as a whole to obtain a converted heavy-sensing data packet. Then, the duplicate data in the heavy-sensing data packet n1 is identified and extracted, and the duplicate data is converted into a binary format. The obtained binary duplicate data is reversed and then reorganized to obtain a replacement template. Then, the replacement template is replaced and combined with the duplicate data in the heavy-sensing data packet to obtain a combined heavy-sensing data packet n1.

[0020] When the sensitive data capacity is smaller than the non-sensitive data capacity, all non-sensitive data are obtained, and the duplicate data in the non-sensitive data are extracted. At the same time, a replacement template is generated, and the replacement template is processed in reverse order. Then, the replacement template is replaced with the duplicate data to generate a re-sensitive data packet n1.

[0021] Step 5: Obtain all light-sensing data packets n2 and perform defense analysis on the light-sensing data packets. The specific processing method is as follows:

[0022] Determine whether there is duplicate data in the light-sensing data packet. If there is duplicate data, generate a replacement template according to the duplicate data, and replace the replacement template with the duplicate data to generate a light-sensing data packet. If there is no duplicate data, determine the data integrity of the light-sensing data packet. If the light-sensing data packet is complete, perform binary conversion on the light-sensing data packet and reverse process it, and then perform binary inverse conversion to obtain a light-sensing data packet. If the light-sensing data packet is incomplete, fill the light-sensing data packet with data to obtain a light-sensing data packet. Specifically, the non-sensitive data obtained after processing here is different from the previous non-sensitive data in terms of data content.

[0023] Step 6: Obtain the heavy-sense data packet n1 and the light-sense data packet n2, and perform different transmission prevention methods on the two. The specific transmission prevention methods are as follows:

[0024] Obtain all transmission channels i and the corresponding transmission volume M i. Calculate the mean weight ratio P1 for transmission channel i. Classify transmission channels with a weight ratio Pi less than P1 as pre-selected channels, and those with a weight ratio Pi greater than P1 as candidate channels. Specifically, a larger weight ratio indicates a more unstable transmission channel. Therefore, all transmission channels are classified by calculating the overall mean, and different data is subsequently transmitted based on the classified transmission channels.

[0025] Then calculate the total transmission volume corresponding to all pre-selected channels and record it as M1, and obtain the total data capacity L1 of all heavy-sensing data packets n1, and compare the total transmission volume M1 with the total data capacity L1. When the total transmission volume M1 is greater than the total data capacity L1, all heavy-sensing data packets are transmitted at one time, and when transmitting, they are transmitted in order from small to large according to the data capacity of the heavy-sensing data packets and from large to small according to the weight ratio corresponding to the pre-selected channels. Conversely, when the total transmission volume M1 is less than the total data capacity L1, the heavy-sensing data packets are transmitted in batches, and when transmitting, the heavy-sensing data packets are preferentially transmitted in order from large to small according to the data capacity of the heavy-sensing data packets and the transmission information is generated.

[0026] When transmitting light-sensing data packets, select a candidate channel for transmission, and sort the candidate channels from large to small according to the weight ratio. The light-sensing data packets are matched and transmitted with the candidate channels sorted from large to small in order of data capacity, and transmission information is generated.

[0027] The present invention provides a blockchain-based information security data defense method and system. Compared with the existing technology, it has the following advantages:

[0028] The present invention can not only improve the security of data transmission through reasonable data capacity segmentation, identification of sensitive data and classification processing of data packets, but also adopt corresponding transmission strategies according to the characteristics of different data packets, thereby ensuring data security while also ensuring transmission efficiency. Secondly, different transmission methods are selected according to the characteristics of the data itself, and network resources are reasonably allocated according to actual conditions to ensure the security and efficient transmission of data, thereby improving the security of data transmission. At the same time, the authenticity of the data content is further hidden by replacing the original duplicate data, thereby improving the confidentiality and integrity of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Flow chart of the method of the present invention;

[0030] Figure 2 This is a block diagram of the system principle of the present invention;

[0031] In the figure: 101, information collection unit; 102, data classification unit; 103, transmission analysis unit; 104, information output unit. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] For example 1, please refer to Figure 1 , this application provides an information security data defense method based on blockchain, and the method specifically includes the following steps:

[0034] Step 1: Collect information security data, calculate the transmission risk based on the information parameters corresponding to the information security data, and make defense judgments based on the transmission risk. The specific defense judgment method is as follows:

[0035] Acquire information security data and record it as an analysis target, then acquire information parameters of the analysis target object, where the information parameters include: information format, data capacity, and network fluctuation, and calculate the format value of the analysis target based on the acquired information format;

[0036] The information format of the target to be analyzed is recorded as the target format. At the same time, the number of transmissions C, the number of exceptions C1, and the mean value R of the format capacity corresponding to the target format in the time period t are obtained. Here, the number of exceptions (C1) specifically refers to the frequency of transmission exceptions that occur during the transmission process. In order to reveal the stability of the format, the obtained target format parameters are substituted into the formula Calculate the format value Q of the target format;

[0037] Substitute the format value Q, data capacity L, and network fluctuation B into the formula The transmission risk value W of the analysis target is calculated, and a1 and θ are both preset values, and a1 = 0.417, θ = 1.47. Then the calculated transmission risk value W is compared with the risk monitoring value Wy; specifically, through this formula, the transmission risk value (W) of the analysis target is calculated, which represents the potential risk level of data transmission through the target format under the current network environment.

[0038] When the transmission risk value W is greater than the risk monitoring value Wy, it means that there is a risk in the transmission of the analysis target, and defense processing is required, and a defense processing signal is generated. Conversely, when the transmission risk value W is less than the risk monitoring value Wy, it means that there is no risk in the transmission of the analysis target, and a transmission monitoring signal is generated.

[0039] Step 2: The generated transmission monitoring signal is monitored by monitoring network traffic and user activities, and the analysis target corresponding to the transmission monitoring signal is analyzed to generate monitoring information. The specific method of generating the monitoring information is as follows:

[0040] Leverage network security tools and technologies, such as intrusion detection systems (IDS), intrusion prevention systems (IPS), and security information and event management (SIEM) systems, to collect, analyze, and visualize network traffic and user activity data;

[0041] Based on predetermined security policies and rules, an automated alarm system is configured. Through user behavior analysis (UBA) technology, behaviors that deviate from normal patterns can be identified and corresponding monitoring information can be generated.

[0042] Step 3: Analyze the generated defense processing signal. The specific analysis method is as follows:

[0043] The transmission channel of the transmission analysis target is labeled as i, where i = 1, 2, ..., j. At the same time, the packet loss rate Ki and delay time Ti corresponding to the transmission channel are obtained. The specific delay time, also known as latency, refers to the time required for data to be transmitted from the sender to the receiver. This time includes the time it takes for the data to propagate on the physical transmission medium, the time it takes for the data to be processed in the network device, and the time it waits in the queue. The packet loss rate refers to the ratio of data packets lost during data transmission to the total number of data packets sent. Data packet loss is usually caused by network congestion, signal attenuation, or system failure, and the obtained packet loss rate Ki and delay time Ti are substituted into the formula The weight ratio Pi of transmission channel i is calculated, where α is a preset value, and α=2.17, and the transmission channels i are sorted from large to small according to the weight ratio Pi, and the weight ratio Pi is calculated to obtain a simplified ratio, and the transmission amount corresponding to the transmission channel i is obtained and recorded as Mi; and the ratio calculation of the weight ratio Pi here is specifically expressed as simplifying the ratio of all weight ratios, for example, the final ratio is 4:6:7:2:5.

[0044] The analysis target and its corresponding data capacity L are obtained, and the data capacity L of the analysis target is divided into n data packets according to the simplification ratio of the transmission channel i. Specifically, the number of data packets obtained here is equal to the sum of the simplification ratio values. At the same time, sensitive data is identified for data packet n. The data packet corresponding to the presence of sensitive data is recorded as a heavy-sense data packet n1, and the data packet without sensitive data is classified as a light-sense data packet n2. The sensitive data here includes personal identity information, intellectual property rights, trade secrets, etc.

[0045] Specifically, suppose a company needs to transmit a report containing a large amount of business data, and the data capacity of this report is L. In order to efficiently transmit this report, the entire file must first be split into multiple smaller data packets. This process is similar to dividing a large shipment into multiple small packages for transportation. Based on the characteristics of the transmission channel i, such as bandwidth limitations, stability and other factors, we decided to use the simplification ratio to split the data, thereby obtaining n data packets. The number of these data packets is equal to the sum of the simplification ratio values. While splitting the data packets, sensitive data must also be identified for each data packet. This step is crucial because it involves the protection of important information such as personal identity information, intellectual property rights, and trade secrets. Using advanced data recognition technologies, such as data pattern recognition and machine learning, data packets containing sensitive information can be effectively identified.

[0046] Step 4: Then obtain all the heavy-sensing data packets n1 and perform defense analysis based on the different data in the heavy-sensing data packets. The specific analysis method is as follows:

[0047] All heavy-sensing data packets n1 are obtained, and the capacities corresponding to the sensitive data and non-sensitive data in the heavy-sensing data packet n1 are obtained, and the capacities of the two are compared. When the capacity of sensitive data is greater than that of non-sensitive data, the heavy-sensing data packet n1 is converted into a binary format as a whole to obtain a converted heavy-sensing data packet. Then, the duplicate data in the heavy-sensing data packet n1 is identified and extracted, and the duplicate data is converted into a binary format. The obtained binary duplicate data is reversed and then reorganized to obtain a replacement template. Then, the replacement template is replaced and combined with the duplicate data in the heavy-sensing data packet to obtain a combined heavy-sensing data packet n1.

[0048] Suppose a large enterprise data center needs to process a report containing numerous employee personal information, company financial data, and customer transaction records. This report is divided into multiple data packets for transmission, some of which are identified as heavy-sensing data packets n1 because they contain sensitive information such as personal identification information and trade secrets. First, the capacity of sensitive and non-sensitive data within these heavy-sensing data packets n1 is measured and compared. If the capacity of sensitive data in heavy-sensing data packet n1 is greater than that of non-sensitive data, this means that sensitive information occupies the majority of the data packet. In this case, to improve data security, the entire heavy-sensing data packet n1 needs to be converted to binary format.

[0049] After binary conversion, the next step is to identify duplicate data in re-sense packet n1. Duplicate data may be caused by format redundancy or multiple entries of the same information. After identifying these duplicates, they are converted to binary form and the resulting binary data is reassembled in reverse order to form a replacement template. The purpose of this step is to further conceal the authenticity of the data content by replacing the original duplicate data, thereby improving data confidentiality and integrity. The replacement template generated in the previous step is used to replace and combine the duplicate data in re-sense packet n1, resulting in the combined re-sense packet n1.

[0050] When the sensitive data capacity is smaller than the non-sensitive data capacity, all non-sensitive data are obtained, and the duplicate data in the non-sensitive data are extracted. At the same time, a replacement template is generated, and the replacement template is processed in reverse order. Then, the replacement template is replaced with the duplicate data to generate a re-sensitive data packet n1.

[0051] Step 5: Obtain all light-sensing data packets n2 and perform defense analysis on the light-sensing data packets. The specific processing method is as follows:

[0052] Determine whether there is duplicate data in the light-sensing data packet. If there is duplicate data, generate a replacement template according to the duplicate data, and replace the replacement template with the duplicate data to generate a light-sensing data packet. If there is no duplicate data, determine the data integrity of the light-sensing data packet. If the light-sensing data packet is complete, perform binary conversion on the light-sensing data packet and reverse process it, and then perform binary inverse conversion to obtain a light-sensing data packet. If the light-sensing data packet is incomplete, fill the light-sensing data packet with data to obtain a light-sensing data packet. Specifically, the non-sensitive data obtained after processing here is different from the previous non-sensitive data in terms of data content.

[0053] Step 6: Obtain the heavy-sense data packet n1 and the light-sense data packet n2, and perform different transmission prevention methods on the two. The specific transmission prevention methods are as follows:

[0054] Obtain all transmission channels i and the corresponding transmission volume M i. Calculate the mean weight ratio P1 for transmission channel i. Classify transmission channels with a weight ratio Pi less than P1 as pre-selected channels, and those with a weight ratio Pi greater than P1 as candidate channels. Specifically, a larger weight ratio indicates a more unstable transmission channel. Therefore, all transmission channels are classified by calculating the overall mean, and different data is subsequently transmitted based on the classified transmission channels.

[0055] Then calculate the total transmission volume corresponding to all pre-selected channels and record it as M1, and obtain the total data capacity L1 of all heavy-sensing data packets n1, and compare the total transmission volume M1 with the total data capacity L1. When the total transmission volume M1 is greater than the total data capacity L1, all heavy-sensing data packets are transmitted at one time, and when transmitting, they are transmitted in order from small to large according to the data capacity of the heavy-sensing data packets and from large to small according to the weight ratio corresponding to the pre-selected channels. Conversely, when the total transmission volume M1 is less than the total data capacity L1, the heavy-sensing data packets are transmitted in batches, and when transmitting, the heavy-sensing data packets are preferentially transmitted in order from large to small according to the data capacity of the heavy-sensing data packets and the transmission information is generated.

[0056] In practical terms, during a one-time transmission, packets with high data capacity should be prioritized based on their data capacity and the weight ratio of the preselected channels. Specifically, packets with smaller data capacity should be transmitted first. The weight ratio of the preselected channels should also be considered, with the channels with higher weight ratios being prioritized. During multiple transmissions, packets with high data capacity, n1, should be prioritized. Because large packets contain more information and are likely to be more important and sensitive, prioritizing their transmission ensures that the most important data is processed and reaches its destination as quickly as possible.

[0057] When transmitting light-sensing data packets, select a candidate channel for transmission, and sort the candidate channels from large to small according to the weight ratio. The light-sensing data packets are matched and transmitted with the candidate channels sorted from large to small in order of data capacity, and transmission information is generated.

[0058] For example 2, please refer to Figure 2 , an information security data defense system based on blockchain, including: an information collection unit, a data classification unit, a transmission analysis unit and an information output unit.

[0059] The information collection unit 101 is used to collect information security data and information parameters thereof, where the information parameters include information format, data capacity and network fluctuation, and transmit the collected information parameters to the data classification unit 102 .

[0060] The data classification unit 102 is used to calculate the transmission risk value of the information security data based on the acquired information parameters, and compare the transmission risk value with the risk monitoring value to determine whether the information security data requires defense processing, and generate a defense processing signal and a transmission monitoring signal, and then transmit the two to the transmission analysis unit 103.

[0061] The transmission analysis unit 103 is used to analyze the acquired defense processing signal and transmission monitoring signal, generate monitoring information after processing the transmission monitoring signal, perform transmission analysis on the information security data corresponding to the defense processing signal, and classify different transmission channels according to the weight ratio of the transmission channel, and segment the information security data according to the weight ratio, and then transmit the segmented information security data according to the weight ratio and generate transmission information, and transmit the transmission information to the information output unit 104.

[0062] The information output unit 104 is configured to store the acquired transmission information.

[0063] On the other hand, the present invention also proposes a computer storage medium, on which one or more programs are stored, which, when executed by a processor, implement the above-mentioned blockchain-based information security data defense method.

[0064] On the other hand, the present invention also proposes a computer device, including a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned blockchain-based information security data defense method.

[0065] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0066] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0067] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A blockchain-based information security data defense method, characterized in that: The following steps are involved: Step 1: Collect information security data, calculate the transmission risk according to the information parameters corresponding to the information security data, make defense judgments based on the transmission risk, generate defense processing signals and transmission monitoring signals, and calculate the transmission risk value of information security data and generate different signals in the following specific ways: The information format of the target to be analyzed is recorded as the target format. At the same time, the number of transmissions C, the number of abnormalities C1, and the mean value R of the format capacity corresponding to the target format in the time period t are obtained, and the obtained target format parameters are substituted into the formula Calculate the format value Q of the target format; Substitute the format value Q, data capacity L, and network fluctuation B into the formula The transmission risk value W of the analysis target is calculated, and a1 and θ are both preset values, and a1 = 0.417, θ = 1.47, and then the calculated transmission risk value W is compared with the risk monitoring value Wy; When the transmission risk value W is greater than the risk monitoring value Wy, a defense processing signal is generated, and conversely, when the transmission risk value W is less than the risk monitoring value Wy, a transmission monitoring signal is generated; Step 2: The generated transmission monitoring signal is monitored by monitoring network traffic and user activities, and the analysis target corresponding to the transmission monitoring signal is analyzed to generate monitoring information; Step 3: For the generated defense processing signal, the weight ratio of the transmission channel is calculated and the simplification ratio is calculated, and the information security data is segmented according to the simplification ratio to obtain data packets, and the data packets are classified into heavy-sense data packets and light-sense data packets. The specific method of calculating the weight ratio of the transmission channel and the simplification ratio to obtain the simplification ratio and segmenting the information security data according to the simplification ratio is as follows: The transmission channel is labeled as i, where i = 1, 2, ..., j, and the packet loss rate Ki and delay time Ti corresponding to the transmission channel are obtained. The obtained packet loss rate Ki and delay time Ti are substituted into the formula Calculate the weight ratio Pi of transmission channel i, where α is a preset value, and α=2.17, and sort the transmission channels i from large to small according to the weight ratio Pi, and calculate the ratio of the weight ratio Pi to obtain a simplified ratio, and at the same time obtain the transmission amount corresponding to the transmission channel i and record it as Mi; Obtain the data capacity L corresponding to the analysis target, and divide the data capacity L of the analysis target into n data packets according to the simplification ratio of the transmission channel i, and perform sensitive data identification on the data packet n, and record the data packet corresponding to the sensitive data as the heavy-sense data packet n1, and classify the data packet without sensitive data as the light-sense data packet n2; Step 4: Classify the data in the heavy-sense data packet to obtain sensitive data and non-sensitive data, and generate a replacement template based on the duplicate data corresponding to the two, and use the replacement template to replace the duplicate data to obtain the heavy-sense data packet. The specific method of analyzing the heavy-sense data packet is as follows: The capacity corresponding to the sensitive data and the non-sensitive data in the heavy-sensing data packet n1 is obtained. When the capacity of the sensitive data is greater than the capacity of the non-sensitive data, the heavy-sensing data packet n1 is converted into a binary format to obtain a converted heavy-sensing data packet. Then, the repeated data in the heavy-sensing data packet n1 is identified and extracted, and the repeated data is converted into a binary format. The obtained binary repeated data is reversed and then reassembled to obtain a replacement template. Then, the replacement template is replaced and combined with the repeated data in the heavy-sensing data packet to obtain a combined heavy-sensing data packet n1. When the capacity of sensitive data is smaller than that of non-sensitive data, all non-sensitive data are obtained, and duplicate data in the non-sensitive data is extracted, and a replacement template is generated at the same time, and the replacement template is processed in reverse order, and then the replacement template is replaced with the duplicate data to generate a re-sensitive data packet n1; Step 5: Determine the duplicate data in the light-sensing data packet, combine the data integrity of the light-sensing data packet to obtain the light-sensing data, and analyze and process the light-sensing data in the following specific ways: Determine whether there is duplicate data in the light-sensing data packet. If there is duplicate data, generate a replacement template according to the duplicate data, and replace the replacement template with the duplicate data to generate a light-sensing data packet. If there is no duplicate data, determine the data integrity of the light-sensing data packet. If the light-sensing data packet is complete, perform binary conversion on the light-sensing data packet and process it in reverse order, and then perform binary inverse conversion to obtain the light-sensing data packet. If the light-sensing data packet is incomplete, fill the light-sensing data packet with data to obtain the light-sensing data packet. Step 6: Classify the transmission channels according to the weight ratio, and select different transmission channels for transmission according to the data capacity of the heavy-sense data packet and the light-sense data packet to generate transmission information.

2. According to claim 1, a blockchain-based information security data defense method is characterized in that: The specific method of analyzing the transmission monitoring signal and generating monitoring information in step 2 is: Using network security tools and technologies, network traffic and user activity data can be collected, analyzed and visualized. Through user behavior analysis technology, behaviors that deviate from normal patterns can be identified and corresponding monitoring information can be generated.

3. According to claim 1, a blockchain-based information security data defense method is characterized in that: The specific method of classifying the transmission channels in step 6 is: Obtain all transmission channels i, and obtain the transmission amount Mi corresponding to the transmission channel i, and calculate the mean weight ratio P1 of the transmission channel i, and classify the transmission channels whose weight ratio Pi is less than the mean weight ratio P1 as pre-selected channels, and classify the transmission channels whose weight ratio Pi is greater than the mean weight ratio P1 as candidate channels.

4. According to the information security data defense method based on blockchain in claim 1, it is characterized in that: The specific method of performing transmission analysis on the heavy-sense data packet and the light-sense data packet in step 6 is: Calculate the total transmission volume corresponding to all pre-selected channels as M1, and obtain the total data capacity L1 of all heavy-sensing data packets n1, and compare the total transmission volume M1 with the total data capacity L1. When the total transmission volume M1 is greater than the total data capacity L1, all heavy-sensing data packets are transmitted at one time, and when transmitting, they are transmitted in accordance with the data capacity of the heavy-sensing data packets from small to large and the weight ratio corresponding to the pre-selected channels from large to small. On the contrary, when the total transmission volume M1 is less than the total data capacity L1, the heavy-sensing data packets are transmitted in batches, and when transmitting, the data capacity of the heavy-sensing data packets is preferentially transmitted from large to small and the transmission information is generated. When transmitting a light-sensing data packet, a candidate channel is selected for transmission, and the candidate channels are sorted from large to small according to the weight ratio, and the light-sensing data packet is matched and transmitted with the candidate channels sorted from large to small in order of data capacity from small to large, and transmission information is generated.

5. A blockchain-based information security data defense system is applied to a blockchain-based information security data defense method as described in any one of claims 1 to 4, characterized in that: include: Information collection unit, data classification unit, transmission analysis unit and information output unit; The information collection unit is used to collect information security data and its information parameters, and the information parameters include: information format, data capacity and network fluctuation, and transmit the collected information parameters to the data classification unit; A data classification unit, used to calculate the transmission risk value of the information security data according to the acquired information parameters, and compare the transmission risk value with the risk monitoring value to determine whether the information security data needs defense processing, and generate a defense processing signal and a transmission monitoring signal, and then transmit the two to the transmission analysis unit; A transmission analysis unit, used to analyze the acquired defense processing signal and transmission monitoring signal, generate monitoring information after processing the transmission monitoring signal, perform transmission analysis on the information security data corresponding to the defense processing signal, and classify different transmission channels according to the weight ratio of the transmission channel, and segment the information security data according to the weight ratio, and then transmit the segmented information security data according to the weight ratio and generate transmission information, and transmit the transmission information to the information output unit; The information output unit is used to store the acquired transmission information.

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