Data security analysis system based on data mining

Abstract

The invention discloses a data security analysis system based on data mining, particularly relates to the technical field of data mining, and comprises a log file acquisition module, a preprocessing module, a data analysis module and a man-machine interaction module. The method comprises the following steps: acquiring a first safety range and a second safety range of target log file detection through a log file acquisition module; performing preprocessing operation through the first safety range and the second safety range through a preprocessing module to respectively obtain a first safety keyword group and a second safety keyword group; the data analysis module obtains a first safety degree text corresponding to the first safety range through the first safety keyword group according to the data mining intelligent model, and constructs a third safety keyword group through the optimized keyword; obtaining a second safety degree text corresponding to the second safety range through the third safety keyword group according to the data mining intelligent model, and feeding back the second safety degree text to the management personnel; and when the same security problem is deeply detected, data analysis is more accurate and rapid.

Description

Technical Field

[0001] The present invention relates to the technical field of data mining, and more specifically, to a data security analysis system based on data mining. Background Art

[0002] Data mining technology was born in the context of explosive growth in data in the information age. With the rapid development of information technology, various fields such as business, scientific research, medical care, and finance continue to generate massive amounts of data. It integrates multidisciplinary knowledge such as statistics, machine learning, and database technology. By automatically analyzing data, it discovers association rules and trend predictions in the data, providing strong support for business optimization and scientific research, helping people gain insight into laws, seize opportunities, and solve problems from complex data.

[0003] In the current data security analysis system, the security of data is tested by analyzing security issues and then obtaining methods from a pre-prepared database. However, it only analyzes the current security issues to test their security, and cannot perform in-depth security tests on the same security issue, which leads to reduced data security performance.

[0004] There is an urgent need for a data security analysis system based on data mining to solve the problem. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a data security analysis system based on data mining, which solves the problems raised in the above-mentioned background technology by using the field of ray recognition technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a data security analysis system based on data mining, comprising: a log file acquisition module, a preprocessing module, a data analysis module and a human-computer interaction module.

[0007] Log file acquisition module: in response to the data security operation of data mining, acquires a first security range for target log file detection for the target enterprise; acquires a second security range for log file detection, the second security range being a security range for log file detection after the first security range; Preprocessing module: performing a preprocessing operation on the first safety range, the preprocessing operation is used to obtain a first safety keyword group corresponding to the first safety range; performing a preprocessing operation on the second safety range, the preprocessing operation is used to obtain a second safety keyword group corresponding to the second safety range; Data analysis module: obtain a data mining intelligent model, and obtain a first safety degree text corresponding to a first safety range through a first safety keyword group according to the data mining intelligent model; obtain optional keywords in the first safety keyword group and the second safety keyword group respectively, and construct a third safety keyword group by using the same optional keywords in the first safety keyword group and the second safety keyword group as preferred keywords; obtain a second safety degree text corresponding to a second safety range through the third safety keyword group according to the data mining intelligent model; Human-computer interaction module: Send the second safety degree text corresponding to the second safety range to the management personnel of the target enterprise according to the preset analysis method.

[0008] Preferably, obtaining a data mining intelligent model specifically includes: According to the first security keyword group, a security detection classification operation is performed on the first security scope to obtain security points corresponding to the first security scope. The security points corresponding to the first security scope are target security areas corresponding to the first security scope. The target security areas include customer relationship management, supply chain management, and financial management. The data mining intelligent model corresponding to the security key points is obtained in a preset security database, and the preset security database is used to store the corresponding relationship between the security key points and the data mining intelligent model.

[0009] Preferably, obtaining the key safety points corresponding to the first safety range specifically includes: Acquire a plurality of preset safety points, wherein the plurality of preset safety points include a specific safety point, and the specific safety point is any one of the plurality of preset safety points; By using the approximate value of the first safety keyword group corresponding to the first safety range and the specific safety key point, it is determined whether the approximate value is greater than a preset approximate value; If the approximate value is greater than the preset approximate value, the preset safety point is confirmed to be the safety point corresponding to the first safety range.

[0010] Preferably, according to the data mining intelligent model, before obtaining the first safety degree text corresponding to the first safety range through the first safety keyword group, a data mining intelligent model is constructed, specifically including: Obtain the security data set corresponding to the target security field; According to the security data set, obtain the security data adjacency table corresponding to the target security field; According to the security data adjacency table, obtain the target security graph corresponding to the target security field; Build a data mining intelligent model based on the target safety map.

[0011] Preferably, the security data set includes a customer relationship security data set, a supply chain security data set, and a financial security data set.

[0012] Preferably, obtaining the security data set corresponding to the target security field specifically includes: setting a time period and obtaining the security data set from the target enterprise's data through data mining methods, the data mining methods mainly include python data reading, web crawlers, database queries, etc.

[0013] Preferably, the first safety keyword group and the second safety keyword group have the same optional keywords as preferred keywords, specifically including: Obtain a first keyword of the optional keywords in the first safety keyword group, and obtain a second keyword of the optional keywords in the second safety keyword group, wherein the first keyword is any one of the optional keywords in the first safety keyword group, and the second keyword is any one of the optional keywords in the second safety keyword group; Calculate the accuracy value of the first keyword and the second keyword through programming; Determine whether the accuracy value is within the safety threshold; If the accuracy value is within the safety threshold range, the first keyword and the second keyword appear as the first safety keyword group and the second safety keyword group with the same optional keyword as the preferred keyword.

[0014] Preferably, the preset manner includes any one of report display, table display and picture display.

[0015] Technical effects and advantages of the present invention: 1. The present invention obtains the first security range and the second security range for target log file detection of the target enterprise through the log file acquisition module; the preprocessing module performs preprocessing operations through the first security range and the second security range to respectively obtain the first security keyword group corresponding to the first security range and the second security keyword group corresponding to the second security range; the data analysis module obtains the first security degree text corresponding to the first security range through the first security keyword group according to the data mining intelligent model, obtains the optional keywords in the first security keyword group and the second security keyword group respectively, and constructs the third security keyword group with the same optional keywords appearing in the first security keyword group and the second security keyword group as the preferred keywords; obtains the second security degree text corresponding to the second security range through the third security keyword group according to the data mining intelligent model, and feeds back to the management personnel; when the management personnel conduct in-depth detection on the same security issue, the preferred keywords are selected according to the accuracy, so that the second security degree text corresponding to the second security range is more accurate and faster; 2. The present invention obtains a security data set corresponding to the target security field; based on the security data set, obtains a security data adjacency list corresponding to the target security field; based on the security data adjacency list, obtains a target security graph corresponding to the target security field; based on the target security graph, constructs a data mining intelligent model to make data analysis more accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the system structure of the present invention.

[0017] Figure 2 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0019] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0020] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0021] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0022] As attached Figure 1 Shown in the figure is a data security analysis system based on data mining, including a log file acquisition module, a preprocessing module, a data analysis module and a human-computer interaction module.

[0023] As attached Figure 2Shown in the figure is a schematic diagram of the overall structure of a data security analysis system based on data mining, a system database, a system processor and a system information terminal.

[0024] The output end of the log file acquisition module is connected to the input end of the preprocessing module, the output end of the preprocessing module is connected to the input end of the data analysis module, and the output end of the data analysis module is connected to the input end of the human-computer interaction module.

[0025] The system database stores all security scopes used by the target enterprise for target log file detection; the system processor is used to control all operating instructions and security data processing instructions of each module; and the system information terminal is used to receive equipment for the optimized system.

[0026] The log file acquisition module is used to respond to the data security operation of data mining, obtain a first security range for target log file detection for a target enterprise; obtain a second security range for the log file detection, and the second security range is the security range for the log file detection after the first security range.

[0027] In this embodiment, it should be specifically explained that the administrator can perform data security operations on the corresponding terminal devices. The types of information terminal devices mainly include: smart phones, laptops, tablets, etc.; there are many ways for administrators to detect target log files, including but not limited to: text, tables, images, etc. The administrator obtains the corresponding security range through the corresponding terminal device, the corresponding data security operation of the data security system of data mining, and performs intelligent security analysis on the system according to the security range of the administrator. This embodiment does not limit the methods of information terminal devices and log files; when performing data security operations, the administrator establishes multiple status windows. In each status window, the administrator can perform data security operations. Each status window exists independently, and each status window will not affect each other. In this embodiment, one of the status windows is used for operation. The first security range can be all historical security ranges before the administrator enters the first security range in the status window.

[0028] Preprocessing module: performing a preprocessing operation on the first safety range, the preprocessing operation is used to obtain a first safety keyword group corresponding to the first safety range; performing the preprocessing operation on the second safety range, the preprocessing operation is used to obtain a second safety keyword group corresponding to the second safety range.

[0029] In this embodiment, it should be specifically explained that after obtaining the first security range for target log file detection for the target enterprise, the data mining data security system can obtain the first security keyword group corresponding to the first security range through preprocessing operation. Specific steps of preprocessing operation: A1: Safe range reading: Define the formats of texts within the safe range, such as CSV (Comma-Separated Values), TXT (plain text), JSON (JavaScript Object Notation), etc.

[0030] A2: Removal of irrelevant content: For example, remove special characters, identify and delete special characters in the text, such as punctuation marks, symbols, control characters, etc., which may interfere with subsequent analysis. Remove stop words. Stop words are words that frequently appear in the text but usually do not contain much meaningful information, such as "的" (of), "是" (is), "在" (at), etc. Removing stop words can reduce the dimensionality of the data and improve the efficiency of subsequent processing.

[0031] A3: Standardization: For example, case conversion, convert all letters in the text to uppercase or lowercase to ensure the consistency of words. This helps to avoid being regarded as different words due to different cases in subsequent processing. Remove duplicate data, identify and delete duplicate content in the text data to ensure the uniqueness of the data, which can avoid unnecessary processing of duplicate data in subsequent analysis.

[0032] A4: Keyword extraction: Based on statistical methods, after the preprocessing process, extract multiple keywords with important significance from the first safe range, which are the first keyword group. The multiple keywords with specific important significance are generally noun annotations and verb annotations. The specific statistical methods include word frequency statistics, term frequency-inverse document frequency, N-gram method, etc.

[0033] According to the same preprocessing operation method, obtain the second safe keyword group corresponding to the second safe range.

[0034] Data analysis module: Obtain a data mining intelligent model, according to the data mining intelligent model through the first safe keyword group, obtain the first safety degree text corresponding to the first safe range; respectively obtain the optional keywords in the first safe keyword group and the second safe keyword group, and use the same optional keywords that appear in the first safe keyword group and the second safe keyword group as preferred keywords to construct the third safe keyword group; according to the data mining intelligent model through the third safe keyword group, obtain the second safety degree text corresponding to the second safe range.

[0035] In this embodiment, specifically, it should be noted that the data mining intelligent model is a pre-constructed intelligent model. By inputting the first safe keyword group into the data mining intelligent model, the data mining intelligent model will obtain the first safety degree text according to the first safe keyword group and send the first safety degree text to the management personnel.

[0036] Obtaining the data mining intelligent model specifically includes: B1: According to the first security keyword group, a security detection classification operation is performed on the first security scope to obtain the security key points corresponding to the first security scope. The security key points corresponding to the first security scope are the target security areas corresponding to the first security scope. The target security areas include customer relationship management, supply chain management and financial management. B2: In a preset security database, a data mining intelligent model corresponding to the security key points is obtained. The preset security database is used to store the corresponding relationship between the security key points and the data mining intelligent model.

[0037] In this embodiment, it is specifically necessary to explain that, through the first security keyword group, the first security scope is classified to obtain the security points corresponding to the first security scope, and the security points corresponding to the first security scope are the target security fields corresponding to the first security scope, that is, the security fields to which the first security scope belongs are confirmed, which helps the data security analysis system to understand the intentions of the managers more accurately, and selects the appropriate data mining intelligent model for corresponding processing according to the first security scope, and the target security fields include but are not limited to: customer relationship management field, supply chain management field and financial management field. After determining the security points of the first security scope, through the corresponding relationship between the security points and the data mining intelligent model in the preset security database, the appropriate data mining intelligent model can be obtained in the preset security database. The preset security database is a pre-built database for storing multiple data mining intelligent models, multiple security points and the corresponding relationship between multiple security points and multiple data mining intelligent models. The security points correspond to the data mining intelligent models one by one, including but not limited to: the customer relationship management intelligent model corresponding to the customer relationship management field, the supply chain management intelligent model corresponding to the supply chain management field and the financial management intelligent model corresponding to the financial management field.

[0038] Obtain the safety points corresponding to the first safety range, including: C1: Acquire multiple preset safety points, where the multiple preset safety points include a specific safety point, and the specific safety point is any one of the multiple preset safety points; C2: judging whether the approximate value between the first safety keyword group and the specific safety key point corresponding to the first safety range is greater than a preset approximate value; C3: If the approximate value is greater than the preset approximate value, confirm that the preset safety point is the safety point corresponding to the first safety range.

[0039] In the present embodiment, it is specifically necessary to explain that by constructing a preset security point database for storing multiple preset security points, the data security analysis system classifies them according to different security fields, and each preset security point includes a security keyword associated with the security field corresponding to the preset security point. The security keyword corresponding to each preset security point can be obtained from the corresponding security field through a Python crawler. After the data security analysis system classifies them according to different security fields, the preset security points, the security keywords associated with the security field corresponding to the preset security points, and the corresponding relationship between the preset security points and the security keywords associated with the security field corresponding to the preset security points are stored in the database. Then, when the data security analysis system obtains the first security keyword group corresponding to the first security range, by calculating the approximate value of the first keyword group and the specific security point, the specific security point is any one of the multiple preset security points, and the data security analysis system compares each security keyword in the first security keyword group with each security keyword corresponding to the preset security point through a programming code method, and obtains multiple calculation results. After that, the multiple calculation results are calculated to obtain the arithmetic mean, which is the approximate value of the first security keyword group and the specific security point; it is determined whether the approximate value is greater than the preset approximate value. If the approximate value is greater than the preset approximate value, it is confirmed that the preset security point is the security point corresponding to the first security range. The preset approximate value is the result of the target enterprise's statistics in previous years, and this embodiment does not limit the setting of the preset approximate value.

[0040] According to the data mining intelligent model, before obtaining the first safety degree text corresponding to the first safety range through the first safety keyword group, a data mining intelligent model is constructed, specifically including: D1: Obtain the security data set corresponding to the target security field; D2: Obtain the security data adjacency table corresponding to the target security domain based on the security data set; D3: Obtain a target security graph corresponding to the target security field according to the security data adjacency table; D4: Build a data mining intelligent model based on the target safety map.

[0041] In this embodiment, it should be specifically explained that the intelligent detection model is a learning model built in advance. The data security analysis system establishes a corresponding intelligent detection model for each security point. First, it is necessary to build a target security graph corresponding to the same security point. The steps for building the target security graph are as follows: According to the target security field corresponding to the security point, obtain the security data set corresponding to the target security field. The security data set includes but is not limited to: text, log files, web pages and other information related to the target security field. The security data set can be obtained from the data channel of the target enterprise through a python crawler program. Select the corresponding data in the security data set and use it as the node corresponding to the target security graph for building the intelligent detection model. Afterwards, by processing these data, the entities, attributes and relationships therein can be extracted, with the entities as nodes of the graph and the relationships as edges of the graph, thereby building a target security graph.

[0042] In this embodiment, it is specifically necessary to explain that, according to the above-mentioned target security graph, a data mining intelligent model is constructed, and the steps of constructing the data mining intelligent model are as follows: extract entities, attributes and relationships from the target security graph, and perform preprocessing operations on the extracted entities, attributes and relationships, including removing irrelevant content, text standardization, data cleaning, etc. By using graph neural network technology to learn the representation of entities and relationships in the structure of the graph, for example, based on the recursive graph neural network (RecGNN) to learn the representation of nodes by recursive propagation on the graph; based on the spatiotemporal graph neural network (STGNN), the spatial structure and time dynamic information of the graph can be processed simultaneously. Combined with the above-mentioned target security graph and graph neural network technology, a data mining intelligent model is constructed. The constructed data mining intelligent model is pre-trained using the data extracted from the target security graph. During the training process, the model will learn according to the input security information and security graph, and then learn the corresponding relationship between the input security information and the solution security information. The well-trained data mining intelligent model is estimated, and the detection data mining intelligent model is intended to detect the security performance of the security data set, and the security performance is evaluated by respective security evaluation indicators, such as vulnerability management level, detection accuracy, backup frequency, etc. to evaluate the data mining intelligent model. The evaluated data mining intelligent model is optimized. The optimization process includes: adjusting the training data of the data mining intelligent model, modifying the structure of the data mining intelligent model, etc. The goal of the optimization process is to improve the overall performance of the data mining intelligent model so that it can be more in line with the security scope of the actual application scenario. After the data mining intelligent model is trained, the data mining intelligent model can obtain the edge closest to the first security keyword in the target security, that is, the first security degree text corresponding to the first security scope. In addition, the security data set corresponding to the target security field is obtained, specifically including: by setting the time period, the data of the target enterprise is used to obtain the security data set through data mining. The main data mining methods include python data reading, web crawler, database query, etc.

[0043] Respectively obtaining optional keywords in the first safety keyword group and the second safety keyword group, and constructing a third safety keyword group by using the same optional keywords in the first safety keyword group and the second safety keyword group as preferred keywords, specifically including: E1: Obtain a first keyword of the optional keywords in the first safety keyword group, and obtain a second keyword of the optional keywords in the second safety keyword group, wherein the first keyword is any one of the optional keywords in the first safety keyword group, and the second keyword is any one of the optional keywords in the second safety keyword group; E2: Calculate the accuracy value of the first keyword and the second keyword through programming; E3: Determine whether the accuracy value is within the safety threshold; E4: If the accuracy value is within the safety threshold range, the first keyword and the second keyword as the first safety keyword group and the second safety keyword group have the same optional keywords as preferred keywords; that is, multiple preferred keywords constitute a third safety keyword group.

[0044] In this embodiment, it should be specifically explained that the specific formula for programming to calculate the accuracy value of the first keyword and the second keyword is: , P represents the accuracy value of the first keyword and the second keyword, p represents the accuracy value of the historical data statistical evaluation, K1 represents the first keyword, and K2 represents the second keyword.

[0045] In this embodiment, it is specifically necessary to explain that a first keyword of the optional keywords in the first safety keyword group is obtained, and a second keyword of the optional keywords in the second safety keyword group is obtained, the first keyword is any optional keyword of the optional keywords in the first safety keyword group, and the second keyword is any optional keyword of the optional keywords in the second safety keyword group, that is, the accuracy value of each keyword in the first safety keyword group and each keyword in the second safety keyword group are calculated respectively. If, for any first keyword in the first safety keyword group, there is any second keyword in the second safety keyword group and the accuracy value calculated with the first keyword is within the safety threshold range, it is confirmed that there is a second keyword in the second safety keyword group that is the same as the first keyword; if, for any first keyword in the first safety keyword group, there is any second keyword in the second safety keyword group and the accuracy value calculated with the first keyword is not within the safety threshold range, it is confirmed that there is no second keyword in the second safety keyword group that is the same as the first keyword, and the accuracy values ​​of other second keywords in the second safety keyword group and the first keyword are continued to be determined. The above operation is repeated for each first keyword in the first safety keyword group until all first keywords in the first safety keyword group are traversed, and all preferred keywords in the first safety keyword group and the second safety keyword are traversed, that is, used as the third safety keyword group.

[0046] According to the data mining intelligent model, the second safety degree text corresponding to the second safety range is obtained through the third safety keyword group.

[0047] In this embodiment, it should be specifically explained that the data mining intelligent model can obtain the edge closest to the third security keyword in the target security graph, and use the above edge as the second security level text corresponding to the second security range.

[0048] Human-computer interaction module: Send the second safety degree text corresponding to the second safety range to the manager of the target enterprise in a preset manner.

[0049] In this embodiment, it should be specifically explained that the second safety level text corresponding to the second safety range is in a preset manner, mainly including but not limited to: any one of report display, table display and picture display.

[0050] The present invention obtains the first security range and the second security range for target log file detection of the target enterprise through the log file acquisition module; through the preprocessing module, the first security range and the second security range are preprocessed to obtain the first security keyword group corresponding to the first security range and the second security keyword group corresponding to the second security range respectively; the data analysis module obtains the first security degree text corresponding to the first security range through the first security keyword group according to the data mining intelligent model, obtains the optional keywords in the first security keyword group and the second security keyword group respectively, and constructs the third security keyword group with the same optional keywords as the preferred keywords through the first security keyword group and the second security keyword group; obtains the second security degree text corresponding to the second security range through the third security keyword group according to the data mining intelligent model; when the management personnel conduct in-depth detection on the same security problem, the preferred keywords are selected according to the accuracy, so that the second security degree text corresponding to the second security range is more accurate and fast. By obtaining the security data set corresponding to the target security field; according to the security data set, the security data adjacency table corresponding to the target security field is obtained; according to the security data adjacency table, the target security map corresponding to the target security field is obtained; according to the target security map, the data mining intelligent model is constructed to make the data analysis more accurate and reliable.

[0051] Secondly, in the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data security analysis system based on data mining, comprising a database, a system processor and a system information terminal, characterized in that: Also includes: Log file acquisition module, preprocessing module, data analysis module and human-computer interaction module; The database stores all security scopes used by the target enterprise for target log file detection; The system processor is used to control all operation instructions and safety data processing instructions of each module; the system information terminal is used to receive optimized information data; The log file acquisition module: in response to the data security operation of data mining, acquires a first security range for target log file detection for the target enterprise; Acquire a second security range for detecting the log file, where the second security range is a security range for detecting the log file after the first security range; The preprocessing module performs a preprocessing operation on the first safety range, wherein the preprocessing operation is used to obtain a first safety keyword group corresponding to the first safety range; Performing the preprocessing operation on the second safety range, wherein the preprocessing operation is used to obtain a second safety keyword group corresponding to the second safety range; The data analysis module: obtains a data mining intelligent model, and obtains a first safety degree text corresponding to the first safety range through the first safety keyword group according to the data mining intelligent model; obtains optional keywords in the first safety keyword group and the second safety keyword group respectively, and constructs a third safety keyword group by using the same optional keywords in the first safety keyword group and the second safety keyword group as preferred keywords; Acquire a second safety degree text corresponding to the second safety range through the third safety keyword group according to the data mining intelligent model; The human-computer interaction module sends the second safety degree text corresponding to the second safety range to the management personnel of the target enterprise according to a preset analysis method.

2. According to claim 1, a data security analysis system based on data mining is characterized in that: The obtaining of the data mining intelligent model specifically includes: According to the first security keyword group, a security detection classification operation is performed on the first security scope to obtain security points corresponding to the first security scope, where the security points corresponding to the first security scope are target security fields corresponding to the first security scope, and the target security fields include customer relationship management field, supply chain management field, and financial management field; The data mining intelligent model corresponding to the security key point is obtained in a preset security database, and the preset security database is used to store the corresponding relationship between the security key point and the data mining intelligent model.

3. A data security analysis system based on data mining according to claim 2, characterized in that: The obtaining of the key safety points corresponding to the first safety range specifically includes: Acquire a plurality of preset safety points, wherein the plurality of preset safety points include a specific safety point, and the specific safety point is any one of the plurality of preset safety points; By using the approximate value of the first safety keyword group corresponding to the first safety range and the specific safety key point, determining whether the approximate value is greater than a preset approximate value; If the approximate value is greater than the preset approximate value, the preset safety point is confirmed to be the safety point corresponding to the first safety range.

4. The data security analysis system based on data mining according to claim 2 is characterized in that: According to the data mining intelligent model, before obtaining the first safety degree text corresponding to the first safety range through the first safety keyword group, constructing the data mining intelligent model specifically includes: Acquire a security data set corresponding to the target security domain; According to the security data set, obtaining a security data adjacency table corresponding to the target security domain; According to the security data adjacency table, obtaining a target security graph corresponding to the target security field; According to the target safety map, the data mining intelligent model is constructed.

5. The data security analysis system based on data mining according to claim 4 is characterized in that: The security data set includes a customer relationship security data set, a supply chain security data set and a financial security data set.

6. A data security analysis system based on data mining according to claim 4, characterized in that: The obtaining of the security data set corresponding to the target security field specifically includes: obtaining the security data set by setting a time period and using data mining methods to obtain the security data set from the data of the target enterprise. The data mining methods mainly include python data reading, web crawlers, database queries, etc.

7. The data security analysis system based on data mining according to claim 1 is characterized in that: The first safety keyword group and the second safety keyword group have the same optional keywords as preferred keywords, specifically including: Obtain a first keyword of the optional keywords in the first safety keyword group, and obtain a second keyword of the optional keywords in the second safety keyword group, wherein the first keyword is any one of the optional keywords in the first safety keyword group, and the second keyword is any one of the optional keywords in the second safety keyword group; Calculate the accuracy value of the first keyword and the second keyword through programming; Determine whether the accuracy value is within the safety threshold; If the accuracy value is within the safety threshold range, the first keyword and the second keyword as the first safety keyword group and the second safety keyword group appear with the same optional keyword as the preferred keyword.

8. The data security analysis system based on data mining according to claim 1 is characterized in that: The preset method includes any one of report display, table display and picture display.

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