CN116506186A - Big data layering analysis method for network security level protection evaluation data - Google Patents
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Abstract
The application relates to the field of big data analysis methods, in particular to a big data layering analysis method of network security level protection evaluation data, which comprises the following steps: based on a set data operation layer, cleaning data in the data operation layer to form a data warehouse layer, wherein the data warehouse layer comprises dimension domains and data of the data domains; processing and summarizing the data in the data warehouse layer to generate a data service layer; and in the data service layer, generating data of a theme zone, wherein the data is used for carrying out data analysis of different requirements on network security level protection evaluation data. The method and the device protect the evaluation data at the network security level to conduct layered big data analysis, and have the effect of improving the application value of the evaluation data.
Description
Technical Field
The invention relates to the field of big data analysis methods, in particular to a big data layering analysis method of network security level protection evaluation data.
Background
The information security level protection refers to the hierarchical implementation of security protection on information systems for private information, public information and public information of national secrets, legal persons and other organizations and citizens, and storing, transmitting and processing the information, the implementation of hierarchical management on security products used in the information systems, and the hierarchical response and disposal on information security events occurring in the information systems. The information system is a system or network which is formed by a computer and related and matched equipment and facilities and stores, transmits and processes information according to certain application targets and rules. Information refers to digitized information that is stored, transmitted, and processed in an information system.
In order to protect national information security, the information system is required to carry out corresponding grade protection evaluation by the iso-protection 2.0. The grade protection evaluation is that an evaluation organization is entrusted by related units according to national information security grade protection system regulation, and scientific means and methods are applied according to related management standards and technical standards. And detecting and evaluating the protection condition of an information system for processing specific application by adopting a safety technology evaluation and safety management evaluation mode, judging the coincidence degree of the technology and management level of the tested system and the requirement of the safety level, giving a conclusion whether the safety level meets the preset safety level or not based on the coincidence degree, and providing a safety correction proposal for a safety non-coincidence item.
The evaluation work can generate evaluation data, the evaluation data can show geometric grade increase along with the deep development of the evaluation work, and the related data analysis is carried out on the evaluation data, so that great social and commercial values can be dug. The existing data analysis method is inconvenient to extract valuable data from a large amount of measurement and evaluation data by performing single-level data mining analysis, and the application value of the measurement and evaluation data is low.
Disclosure of Invention
In order to analyze the layering big data of the network security level protection evaluation data, the application provides a big data layering analysis method of the network security level protection evaluation data.
In a first aspect, the method for hierarchical analysis of big data of network security level protection evaluation data provided by the present application adopts the following technical scheme:
based on a set data operation layer, cleaning data in the data operation layer to form a data warehouse layer, wherein the data warehouse layer comprises dimension domains and data of the data domains; processing and summarizing the data in the data warehouse layer to generate a data service layer; and in the data service layer, generating data of a theme zone, wherein the data is used for carrying out data analysis of different requirements on network security level protection evaluation data.
By adopting the technical scheme, hierarchical construction of the grade protection evaluation data is carried out according to the multi-bin hierarchical construction method, the data mining analysis efficiency and range of the evaluation data are improved, a more scientific hierarchical analysis method for the evaluation big data is established, the data mining analysis is better carried out on the network security grade protection evaluation data, and the application value of the evaluation data is improved.
By dividing the data according to different layers, the data can be better mined and analyzed, so that the application value of the evaluation data is improved. By establishing a hierarchical analysis method, the security of network security level protection evaluation data is ensured, the data mining analysis efficiency and range of the evaluation data are improved, and a more complete and systematic solution is provided for the processing and analysis of the level protection evaluation data.
Preferably, the data warehouse comprises a DWD detail layer, a DWS summary layer and a DWM aggregation layer, and the data flow directions of the DWD detail layer, the DWS summary layer and the DWM aggregation layer are from the DWD detail layer to the DWS summary layer to the DWM aggregation layer.
Preferably, in the data service layer, data of a topic field is generated, and the data analysis of different requirements topics is performed on the network security level protection evaluation number, including the following steps: establishing a theme zone related to the evaluation data; extracting and processing the data of the evaluation data; constructing a data model; and carrying out multidimensional analysis and mining on the data by using a data analysis tool, finding out association relations, rules and data trends in the data, and generating an analysis list based on analysis results so as to carry out data analysis and application.
Preferably, the data sources of the data operation layer include: index library, instruction library and evaluation center database of business system.
Preferably, the dimension field includes a region dimension, an industry dimension, a grade dimension, an organization difficulty and a risk height dimension.
Preferably, the data field includes an evaluation item, an evaluation object, risk analysis data, non-conforming item data, and an evaluation result.
Preferably, the data service layer stores data in ES, mysql for use by an on-line system, providing data products and data for use in data analysis.
In a second aspect, the present application discloses a big data hierarchical analysis system for network security level protection evaluation data, and the big data hierarchical analysis method for network security level protection evaluation data includes: the data warehouse establishing module is used for cleaning the data in the data operation layer based on the set data operation layer to form a data warehouse layer; and the data service layer establishing module is used for processing and summarizing the data in the data warehouse layer to generate a data service layer. And the analysis module is used for generating data of the subject domain, the dimension domain and the data domain in the data service layer and carrying out hierarchical analysis of the data operation layer and the data warehouse level data service layer on the network security level protection evaluation data.
By adopting the technical scheme, hierarchical construction of the grade protection evaluation data is carried out according to the multi-bin hierarchical construction method, the data mining analysis efficiency and range of the evaluation data are improved, a more scientific hierarchical analysis method for the evaluation big data is established, the data mining analysis is better carried out on the network security grade protection evaluation data, and the application value of the evaluation data is improved. By dividing the data according to different layers, the data can be better mined and analyzed, so that the application value of the evaluation data is improved. By establishing a hierarchical analysis method, the security of network security level protection evaluation data is ensured, the data mining analysis efficiency and range of the evaluation data are improved, and a more complete and systematic solution is provided for the processing and analysis of the level protection evaluation data.
In a third aspect, the present application discloses a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor adopts the above-mentioned big data hierarchical analysis method of network security level protection evaluation data when loading and executing the computer program.
By adopting the technical scheme, the big data layering analysis method of the network security level protection evaluation data generates the computer program, and the computer program is stored in the memory to be loaded and executed by the processor, so that the terminal equipment is manufactured according to the memory and the processor, and the user can conveniently use the terminal equipment.
In a fourth aspect, the present application discloses a computer readable storage medium, which adopts the following technical scheme: the computer readable storage medium stores a computer program, and when the computer program is loaded and executed by a processor, the big data layering analysis method of the network security level protection evaluation data is adopted.
By adopting the technical scheme, the big data layering analysis method of the network security level protection evaluation data is used for generating the computer program, and the computer program is stored in the computer readable storage medium to be loaded and executed by the processor, and the computer program is convenient to read and store through the computer readable storage medium.
Drawings
Fig. 1 is a flowchart of steps S1-S3 of a big data hierarchical analysis method of network security level protection evaluation data according to the present application.
Fig. 2 is a logic block diagram of a direction of an identification data flow in a big data hierarchical analysis method of network security level protection evaluation data according to the present application.
Fig. 3 is a flowchart of steps S10-S13 of a big data hierarchical analysis method of network security level protection evaluation data according to the present application.
Fig. 4 is a flowchart of steps S20-S22 of a big data hierarchical analysis method of network security level protection evaluation data according to the present application.
Fig. 5 is a flowchart of steps S30-S33 of a big data hierarchical analysis method of network security level protection evaluation data according to the present application.
Detailed Description
The present application is described in further detail below in conjunction with figures 1-5.
The embodiment of the application discloses a big data layering analysis method of network security level protection evaluation data, referring to fig. 1 and 2, the big data layering analysis method of network security level protection evaluation data comprises the following steps:
and S1, cleaning data in the data operation layer based on the set data operation layer to form a data warehouse layer, wherein the data warehouse layer comprises dimension domains and data of the data domains.
The data operation layer stores a data table of a data warehouse source system to be used as a source of data warehouse processing data. In this application, the dimension field may include: regional dimension, industry dimension, grade dimension, organization difficulty, and risk height dimension. The data field in the application may include an evaluation item, an evaluation object, risk analysis data, non-conforming item data, and an evaluation result.
Specifically, the data table of the data warehouse source system is stored as it is to establish a data operation layer as a data preparation area, which is also called a source layer or ODS layer (Operation Data Store raw data layer), and the ODS layer is a source of data processed by a subsequent data warehouse. Wherein, the source of the ODS layer data is an index library, a guide library and an evaluation center database of the business system. The ODS layer stores a service system, API data, log data, and offline files.
The data warehouse includes a DWD detail layer (Data Warehouse Details, data detail layer), a DWS summary layer (Data Warehouse Service, service data layer), and a DWM aggregation layer (Data Warehouse Middle, data middle layer); the DWD detail layer is an isolation layer of a business and data warehouse layer and is used for performing data cleaning and normalization operation, removing empty data or dirty data, outliers and the like. And (3) carrying out slight aggregation operation on the basis of the DWD detail layer, and calculating corresponding statistical indexes. The DWS summary layer integrates and assembles a data service layer of a theme on the basis of the DWM aggregation layer.
The data flow directions of the DWD detail layer, the DWS summary layer and the DWM aggregation layer are DWD detail layer to DWS summary layer to DWM aggregation layer. The ODS layer, the data warehouse and the ADS layer data flow are ODS layer to data warehouse to ADS layer.
Referring to fig. 3, step S1 includes steps S10-S13, which are described in detail below,
s10: and cleaning the data in the data operation layer to ensure the accuracy and the integrity of the data.
And cleaning the original data, including deduplication, missing value filling, outlier processing and the like, so as to ensure the accuracy and the integrity of the data.
S11: integrating data in the data operation layer and establishing a data model.
Integrating the cleaned data, extracting, converting and loading the data from different data sources into a data warehouse through an ETL tool to form a unified data model, and designing the data model according to service requirements and analysis purposes, wherein the data model comprises dimension tables, fact tables, relation modes and the like so as to provide multidimensional analysis and query of the data.
S12: and storing the cleaned data and establishing a data model.
The data after cleaning and modeling is stored in a data warehouse, and the data storage scheme adopted by the method can be column storage, compression storage and the like so as to improve the efficiency of data retrieval and query.
S13: data in the data warehouse is obtained for monitoring and managing the data.
The data in the data warehouse is monitored and managed, including data quality assessment, data change management, data backup and the like, so as to ensure the reliability and the safety of the data and manage the data quality.
And S2, processing and summarizing the data in the data warehouse layer to generate a data service layer.
The data processing comprises preprocessing operations such as data selection, cleaning, synthesis, merging, data formatting and the like, abnormal data such as repeated data, evaluation items which are not applicable to basic requirements and the like are removed, and the data is enabled to conform to a standardized format so as to carry out standardized operations. Through the data processing of the data operation layer, the quality and the accuracy of the data can be ensured, and meanwhile, the availability and the operability of the data can be improved. The data sources of the data operation layer include: index library, instruction library and evaluation center database of business system.
And integrating the data after data processing into a data service layer, wherein the data exists in the data service layer in the form of a wide table. The data service layer is also called a data application layer (Application Data Service), abbreviated as ADS layer, and mainly provides data for data products and data analysis, and stores the data in ES and mysql for online systems.
And carrying out data processing on the data in the data warehouse layer, and summarizing and integrating the data into a data service layer for service inquiry, OLAP analysis and data distribution.
The service inquiry refers to inquiring evaluation data according to the service requirement of a user, and the inquiry result is obtained through data mining, so that the inquiry result can effectively help information security management personnel to obtain required data. And establishing a multi-dimensional analysis model by an OLAP technology, and reflecting all information such as data, personnel, equipment and the like related to the equal-protection evaluation in a dimensional model, wherein the multi-dimensional analysis comprises cross analysis of transverse dimensions and submerging analysis of longitudinal dimensions, and valuable information can be obtained by finding problems on a macroscopic level and submerging through transverse cross comparison and longitudinal layer-by-layer.
Data is provided to other systems or users through data distribution at the data service layer. For example, the data in the data service layer can be exported as an Excel table or a CSV file, and the data is transmitted through an FTP or API interface.
The steps of data distribution include steps S21-S23, referring to fig. 4, as follows,
and S20, selecting a proper transmission protocol and a proper mode by determining a data source and a target.
The transmission protocol and manner may be one of FTP, HTTP, SCP, SFTP.
S21, setting authority and security policy, and configuring data transmission parameters.
The transmission parameters are parameters such as bandwidth limitation, data compression, etc.
S22, starting data transmission, monitoring transmission progress and recording logs.
And S3, generating data of a theme zone in the data service layer, wherein the data are used for carrying out data analysis of different requirements on the network security level protection evaluation data.
Referring to fig. 5, step S3 includes steps S30 to S33, specifically as follows:
s30: and establishing a theme zone related to the evaluation data.
And determining the topic domain and a corresponding index system according to the data demand and the service demand. For example, it may be determined that the subject domain is network security level protection evaluation data, and the index system includes device information, attack information, protection information, and the like. The topic domains are usually a collection of data topics that are closely related, and these data topics can be divided into different topic domains according to the focus of the service, so that the use and analysis are convenient.
S31: extracting and processing the data of the evaluation data;
and according to the determined subject field, extracting, converting and loading data by using an ETL tool to acquire a data set with higher consistency, accuracy and completeness, and cleaning and preprocessing the data for subsequent analysis and application.
S32: constructing a data model;
according to the data requirements and the data characteristics, a corresponding data model is established, wherein the data model comprises a star model, a snowflake model and the like so as to support multidimensional analysis and inquiry.
S33: and carrying out multidimensional analysis and mining on the evaluation data by using a data analysis tool, finding out the association relation, the rule and the data trend in the evaluation data, and generating an analysis list based on the analysis result so as to carry out data analysis and application. The analysis list can be in various report forms, graphs and the like.
In the application, the application layer data analysis dimension domain is an industry unit dimension, and the industry unit dimension is an industry unit overview analysis, such as unit total number, unit number summarization analysis of each district in jurisdiction, industry system dimension, region dimension and administrative region dimension, equipment dimension, system dimension, security layer dimension, evaluation point dimension, evaluation item dimension, customer importance dimension and project amount dimension, so as to perform security portrait analysis work from the classification angle of security evaluation data.
The implementation principle of the big data layering analysis method of the network security level protection evaluation data in the embodiment of the application is as follows: the hierarchical construction of the grade protection evaluation data is carried out according to the multi-bin hierarchical construction method, the data mining analysis efficiency and range of the evaluation data are improved, a more scientific hierarchical analysis method for the large evaluation data is established, the data mining analysis is better carried out on the network security grade protection evaluation data, and the application value of the evaluation data is improved.
The embodiment of the application also discloses a big data layering analysis system of network security level protection evaluation data, comprising: the data warehouse establishing module is used for cleaning the data in the data operation layer based on the set data operation layer to form a data warehouse layer, wherein the data warehouse layer comprises a dimension domain and the data of the data domain; and the data service layer establishing module is used for processing and summarizing the data in the data warehouse layer to generate the data service layer. And the analysis module is used for generating data of the theme zone in the data service layer and carrying out data analysis of different requirements on the network security level protection evaluation data.
The implementation principle of the big data layering analysis method of the network security level protection evaluation data in the embodiment of the application is as follows: the data warehouse establishing module, the data service layer establishing module and the analyzing module are used for realizing hierarchical construction of the grade protection evaluation data according to the multi-bin hierarchical construction method, improving the data mining analysis efficiency and range of the evaluation data, establishing a more scientific hierarchical analysis method for the evaluation big data, better carrying out data mining analysis on the network security grade protection evaluation data and improving the application value of the evaluation data.
The embodiment of the application also discloses a terminal device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor adopts the big data layering analysis method of the network security level protection evaluation data of the embodiment when executing the computer program.
The terminal device may be a computer device such as a desktop computer, a notebook computer, or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory, for example, the terminal device may further include an input/output device, a network access device, a bus, and the like.
The processor may be a Central Processing Unit (CPU), or of course, according to actual use, other general purpose processors, digital Signal Processors (DSP), application Specific Integrated Circuits (ASIC), ready-made programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., and the general purpose processor may be a microprocessor or any conventional processor, etc., which is not limited in this application.
The memory may be an internal storage unit of the terminal device, for example, a hard disk or a memory of the terminal device, or may be an external storage device of the terminal device, for example, a plug-in hard disk, a Smart Memory Card (SMC), a secure digital card (SD), or a flash memory card (FC) equipped on the terminal device, or the like, and may be a combination of the internal storage unit of the terminal device and the external storage device, where the memory is used to store a computer program and other programs and data required by the terminal device, and the memory may be used to temporarily store data that has been output or is to be output, which is not limited in this application.
The big data layering analysis method of the network security level protection evaluation data of the embodiment is stored in the memory of the terminal device through the terminal device, and is loaded and executed on the processor of the terminal device, so that the user can use the method conveniently.
The embodiment of the application also discloses a computer readable storage medium, and the computer readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the big data layering analysis method of the network security level protection assessment data of the embodiment is adopted.
The computer program may be stored in a computer readable medium, where the computer program includes computer program code, where the computer program code may be in a source code form, an object code form, an executable file form, or some middleware form, etc., and the computer readable medium includes any entity or device capable of carrying the computer program code, a recording medium, a usb disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, a software distribution medium, etc., where the computer readable medium includes, but is not limited to, the above components.
The large data hierarchical analysis method of the network security level protection evaluation data of the embodiment is stored in the computer readable storage medium through the computer readable storage medium, and is loaded and executed on a processor, so that the storage and the application of the large data hierarchical analysis method of the network security level protection evaluation data are facilitated.
The foregoing description of the preferred embodiments of the present application is not intended to limit the scope of the application, in which any feature disclosed in this specification (including abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. That is, each feature is one example only of a generic series of equivalent or similar features, unless expressly stated otherwise.
Claims (10)
1. The big data layering analysis method of the network security level protection evaluation data is characterized by comprising the following steps of:
based on a set data operation layer, cleaning data in the data operation layer to form a data warehouse layer, wherein the data warehouse layer comprises dimension domains and data of the data domains;
processing and summarizing the data in the data warehouse layer to generate a data service layer;
and in the data service layer, generating data of a theme zone, wherein the data is used for carrying out data analysis of different requirements on network security level protection evaluation data.
2. The method according to claim 1, wherein the data warehouse includes a DWD detail layer, a DWS summary layer, and a DWM aggregate layer, and the data flow directions of the DWD detail layer, the DWS summary layer, and the DWM aggregate layer are DWD detail layer to DWS summary layer to DWM aggregate layer.
3. The big data hierarchical analysis method of network security level protection evaluation data according to claim 1 or 2, wherein in the data service layer, data of a topic domain is generated, and the method is used for performing data analysis of different requirements topics on the network security level protection evaluation data, and comprises the following steps:
establishing a theme zone related to the evaluation data;
extracting and processing the data of the evaluation data;
constructing a data model;
and carrying out multidimensional analysis and mining on the evaluation data by using a data analysis tool, finding out association relations, rules and data trends in the evaluation data, and generating an analysis list based on analysis results so as to carry out data analysis and application.
4. The method for hierarchical analysis of big data of network security level protection assessment data according to claim 1, wherein the data sources of the data operation layer include: index library, instruction library and evaluation center database of business system.
5. The method for hierarchical analysis of big data of network security level protection assessment data according to claim 1, wherein the dimension fields include a regional dimension, an industry dimension, a level dimension, an organization difficulty, and a risk height dimension.
6. The method for hierarchical analysis of big data of network security level protection assessment data according to claim 1, wherein the data field includes an assessment item, an assessment object, risk analysis data, non-compliance item data, and an assessment result.
7. The method of claim 1, wherein the data service layer stores data for use by an on-line system in ES, mysql, providing data for data production and data analysis.
8. The big data hierarchical analysis system of network security level protection evaluation data, characterized in that the big data hierarchical analysis method of network security level protection evaluation data according to any one of claims 1 to 7 is used, comprising:
the data warehouse establishing module is used for cleaning data in the data operation layer based on the set data operation layer to form a data warehouse layer, wherein the data warehouse layer comprises dimension domains and data of the data domains;
the data service layer establishing module is used for processing and summarizing the data in the data warehouse layer to generate a data service layer;
and the analysis module is used for generating data of the topic domain in the data service layer and carrying out data analysis of different requirements on the network security level protection evaluation data.
9. Terminal equipment comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, characterized in that the processor, when loading and executing the computer program, adopts the big data layering analysis method of the network security level protection assessment data according to any one of claims 1-7.
10. A computer readable storage medium having a computer program stored therein, wherein the computer program, when loaded and executed by a processor, employs the big data hierarchical analysis method of network security level protection evaluation data according to any one of claims 1 to 7.
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| CN117880329B (en) * | 2024-03-12 | 2024-05-28 | 福建时代星云科技有限公司 | Multi-gateway access method for Internet of things equipment |
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