CN115396238B - Big data based security assessment analysis system and method - Google Patents
Big data based security assessment analysis system and method Download PDFInfo
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- CN115396238B CN115396238B CN202211330505.5A CN202211330505A CN115396238B CN 115396238 B CN115396238 B CN 115396238B CN 202211330505 A CN202211330505 A CN 202211330505A CN 115396238 B CN115396238 B CN 115396238B
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- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1433—Vulnerability analysis
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Abstract
The application discloses a big data security assessment analysis system and method, mainly relates to the technical field of security assessment, and aims to solve the problem that existing big data security assessment is inaccurate. The method comprises the following steps: the acquisition module is used for acquiring the running state data of the detection data to create a data safety information table; the classification module is used for classifying the data in the data safety information table according to a preset classification algorithm to obtain a classification result; calculating the class coefficient of each classification; the statistical module is used for obtaining the number of the categories through the classification result; the evaluation module is used for determining a safety evaluation attribute value corresponding to the detection data according to the safety evaluation data of the detection data in the data safety information table; and determining the safety degree of the detection data according to the safety evaluation attribute value, the category coefficient and the category number. According to the method, the safety evaluation accuracy of the big data is improved.
Description
Technical Field
The application relates to the technical field of big data security assessment, in particular to a big data security assessment-based analysis system and a big data security assessment-based analysis method.
Background
The big data safety assessment is a support guarantee for ensuring that the big data can safely provide services, and aims to verify and assess the effectiveness, performance and the like of all safety strategies, safety products and safety technologies for protecting the big data and ensure that all used safety protection means can meet the requirement of big data safety protection.
The currently common big data security assessment method comprises the following steps: firstly, vulnerability assessment, a vulnerability scanner and other automation tools scan big data application to find out security vulnerabilities, and then analyze and assess the safety of big data; and secondly, permeability test, which is to use various means to attack the big data application so as to find out the security vulnerability of the big data and further analyze and evaluate the safety of the big data.
However, the prior art solutions also have different degrees of defects while realizing the functions: the first scheme depends on accurate definition and elaboration of a vulnerability database, and is difficult to form comprehensive evaluation on the security of big data; the second scheme focuses on evaluating the security of the network system, and the evaluation on the security of the big data is not comprehensive.
Disclosure of Invention
In view of the above-mentioned deficiencies of the prior art, the present invention provides a system and a method for security assessment and analysis based on big data, so as to solve the above-mentioned technical problems.
In a first aspect, the present application provides a big data security assessment-based analysis system, which includes: the acquisition module is used for acquiring the running state data of the detection data to create a data safety information table; the classification module is used for classifying the data in the data safety information table according to a preset classification algorithm to obtain a classification result; calculating the class coefficient of each classification; the statistical module is used for obtaining the category number of each category according to the classification result; the evaluation module is used for determining a safety evaluation attribute value corresponding to the detection data according to the safety evaluation data of the detection data in the data safety information table; and determining the safety degree of the detection data according to the safety evaluation attribute value, the category coefficient and the category number.
As an embodiment, the classification module further comprises a classification unit; the classification unit is used for vectorizing the detection data to obtain a retrieval data table; acquiring a preset distance interval, and taking any detection data from a retrieval data table as a classification central point; generating a central point set corresponding to the classification central point; sequentially taking any detection data from the retrieval data table as a calculation point, and determining a distance value between the calculation point and the classification central point; when the distance value is smaller than the preset distance interval, adding the detection data corresponding to the distance value to the corresponding central point set, and deleting the detection data from the retrieval data table; when the distance value is larger than the preset distance interval, setting the detection data corresponding to the distance value as a classification central point; acquiring residual retrieval data and all classification central points of the distance values in a preset distance interval; and calculating the calculation distance between the residual retrieval data and each classification central point, so as to add the residual retrieval data to the central point set of the classification central point corresponding to the minimum calculation distance, thereby realizing the classification of the detection data and obtaining the classification result.
As an embodiment, the classification module further comprises a coefficient calculation unit; the system computing unit is used for clustering the central point set to obtain a clustering center; and acquiring preset main attribute values and preset attribute weights corresponding to the clustering centers, and determining the mean value of the products of the preset main attribute values and the preset attribute weights as a class coefficient.
As an embodiment, the security assessment attribute values include at least confidentiality data, integrity data, and availability data, and the assessment module further includes an attribute value calculation unit; the attribute value calculating unit is used for determining the confidentiality grade assignment, the integrity grade assignment and the available grade assignment corresponding to the detection data through a trained decision tree algorithm; and determining the average value of the secret grade assignment, the complete grade assignment and the available grade assignment as the security assessment attribute value.
As an embodiment, the operation state data further includes vulnerability risk data and configuration non-conformity item data; the evaluation module also comprises a safety degree calculation unit; the safety degree calculation unit is used for determining vulnerability grade assignment corresponding to vulnerability dangerous data in the detection data through a trained decision tree algorithm, and further determining vulnerability mean values of vulnerability grade assignment of all detection data; determining a risk assessment attribute value according to the proportion value of the configuration non-conforming item data and the vulnerability mean value; and determining the ratio of the risk assessment attribute value to the safety assessment attribute value as the safety degree.
As an embodiment, the system further comprises a prediction module; and the prediction module is used for determining the safety level of the prediction system corresponding to the safety degree according to the preset safety degree database and the safety degree.
In a second aspect, the present application provides a big data security assessment-based analysis method, including: acquiring running state data of the detection data to create a data safety information table; classifying data in the data safety information table according to a preset classification algorithm to obtain a classification result; calculating the class coefficient of each classification; obtaining the number of categories of each category according to the classification result; according to the safety evaluation data of the detected data in the data safety information table, determining a safety evaluation attribute value corresponding to the detected data; and determining the safety degree of the detection data according to the safety evaluation attribute value, the category coefficient and the category quantity.
As an embodiment, the classifying the data in the data security information table according to a preset classification algorithm to obtain a classification result specifically includes: vectorizing the detection data to obtain a retrieval data table; acquiring a preset distance interval, and taking any detection data from a retrieval data table as a classification central point; generating a central point set corresponding to the classification central point; sequentially taking any detection data from the retrieval data table as a calculation point, and determining a distance value between the calculation point and the classification central point; when the distance value is smaller than the preset distance interval, adding the detection data corresponding to the distance value to the corresponding central point set, and deleting the detection data from the retrieval data table; when the distance value is larger than the preset distance interval, setting the detection data corresponding to the distance value as a classification central point; acquiring residual retrieval data and all classification central points of the distance values in a preset distance interval; and calculating the calculation distance between the residual retrieval data and each classification central point, so as to add the residual retrieval data to the central point set of the classification central point corresponding to the minimum calculation distance, thereby realizing the classification of the detection data and obtaining a classification result.
As can be appreciated by those skilled in the art, the present invention has at least the following beneficial effects:
the safety evaluation value of the big data application system is analyzed by performing feature extraction, identification and classification on safety data (safety strategies, safety products and safety technologies) used by big data, and then counting the identified strategies, technologies and the like. The method can effectively solve the problems that only the network system safety can be evaluated and the evaluation is incomplete caused by evaluating the safety of the big data through a vulnerability scanner and a permeability test in the prior art, and can identify and count the safety protection technology used in the whole life cycle from birth to destruction of the big data by using the method, thereby realizing the safe and comprehensive evaluation and analysis of the big data.
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Some embodiments of the present disclosure are described below with reference to the accompanying drawings, in which:
fig. 1 is a schematic diagram of an internal structure of a big data security assessment-based analysis system according to an embodiment of the present application.
Fig. 2 is a flowchart of a security assessment analysis method based on big data according to an embodiment of the present application.
Detailed Description
It should be understood by those skilled in the art that the embodiments described below are only preferred embodiments of the present disclosure, and do not mean that the present disclosure can be implemented only by the preferred embodiments, which are merely intended to explain the technical principles of the present disclosure and not to limit the scope of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the preferred embodiments provided by the disclosure without inventive step, shall fall within the scope of protection of the disclosure.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.
The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a big data security assessment analysis system according to an embodiment of the present disclosure. As shown in fig. 1, a system provided in an embodiment of the present application mainly includes: the device comprises an acquisition module, a classification module, a statistical module and an evaluation module.
The acquisition module is any feasible equipment or device capable of acquiring big data and the like, and is mainly used for acquiring running state data of detection data to create a data safety information table; it should be noted that the operation state data is all security policy data, protection technology data, vulnerability data, and configuration non-compliance data used in the whole life cycle from birth to destruction of big data.
The classification module is used for classifying data in the data safety information table according to a preset classification algorithm to obtain a classification result; and calculates a class coefficient for each classification.
Specifically, "obtaining a classification result" may be: vectorizing the detection data through a classification unit in a classification module to obtain a retrieval data table; acquiring a preset distance interval, and taking any detection data from a retrieval data table as a classification central point; generating a central point set corresponding to the classification central point; sequentially taking any detection data from the retrieval data table as a calculation point, and determining a distance value between the calculation point and the classification central point; when the distance value is smaller than the preset distance interval, adding the detection data corresponding to the distance value to the corresponding central point set, and deleting the detection data from the retrieval data table; when the distance value is larger than the preset distance interval, setting the detection data corresponding to the distance value as a classification central point; acquiring residual retrieval data and all classification central points of the distance values in a preset distance interval; and calculating the calculation distance between the residual retrieval data and each classification central point, so as to add the residual retrieval data to the central point set of the classification central point corresponding to the minimum calculation distance, thereby realizing the classification of the detection data and obtaining a classification result. It should be noted that the preset distance interval may be any feasible interval, and those skilled in the art may determine the specific content according to the actual requirement.
Specifically, the class coefficient of each classification is calculated, which may be: clustering the central point set through a coefficient calculation unit in the classification module to obtain a clustering center; and acquiring preset main attribute values and preset attribute weights corresponding to the clustering centers, and determining the mean value of the products of the preset main attribute values and the preset attribute weights as a class coefficient. It should be noted that the clustering process may be implemented by any feasible clustering algorithm, and the present application is not limited thereto.
The statistical module is any feasible device or apparatus capable of counting the number of sets, and is mainly used for obtaining the number of categories (the central point set) of each category through the classification result (the central point set).
The evaluation module can be any feasible equipment or device capable of performing data evaluation and the like, and is mainly used for determining a security evaluation attribute value corresponding to detection data according to the security evaluation data of the detection data in the data security information table; and determining the safety degree of the detection data according to the safety evaluation attribute value, the category coefficient and the category number.
Specifically, the calculation process of the security assessment attribute value may be: determining vulnerability grade assignment corresponding to vulnerability dangerous data in detection data through a trained decision tree algorithm in a safety degree calculation unit in an evaluation module, and further determining vulnerability mean values of vulnerability grade assignment of all detection data; determining a risk assessment attribute value according to the sum of the configuration non-conformity item data ratio and the vulnerability mean value; and determining the ratio of the risk assessment attribute value to the safety assessment attribute value as the safety degree.
In addition, the application can evaluate the safety level of the result.
Illustratively, the system further comprises a prediction module; the prediction module is any feasible device or device capable of inquiring the database and the like, and is mainly used for determining the security level of the prediction system corresponding to the security degree according to the preset security degree database and the security degree. It should be noted that the preset security database stores the corresponding relationship between the security level and the security level of the prediction system. The specific content of the preset security database can be set by those skilled in the art according to actual requirements.
In addition, an embodiment of the present application further provides a big data security assessment-based analysis method, and as shown in fig. 2, the method provided in the embodiment of the present application mainly includes the following steps:
and step 210, acquiring the running state data of the detection data to create a data safety information table.
Step 220, classifying the data in the data safety information table according to a preset classification algorithm to obtain a classification result; calculating the class coefficient of each classification; and obtaining the category number of each category according to the classification result.
The data in the data security information table is classified according to a preset classification algorithm to obtain a classification result, which specifically can be: vectorizing the detection data to obtain a retrieval data table; acquiring a preset distance interval, and taking any detection data from a retrieval data table as a classification central point; generating a central point set corresponding to the classification central point; sequentially taking any detection data from the retrieval data table as a calculation point, and determining a distance value between the calculation point and the classification central point; when the distance value is smaller than the preset distance interval, adding the detection data corresponding to the distance value to the corresponding central point set, and deleting the detection data from the retrieval data table; when the distance value is larger than the preset distance interval, setting the detection data corresponding to the distance value as a classification central point; acquiring residual retrieval data and all classification central points of the distance values in a preset distance interval; and calculating the calculation distance between the residual retrieval data and each classification central point, so as to add the residual retrieval data to the central point set of the classification central point corresponding to the minimum calculation distance, thereby realizing the classification of the detection data and obtaining the classification result.
Step 230, determining a security assessment attribute value corresponding to the detected data according to the security assessment data of the detected data in the data security information table; and determining the safety degree of the detection data according to the safety evaluation attribute value, the category coefficient and the category quantity.
So far, the technical solutions of the present disclosure have been described in connection with the foregoing embodiments, but it is easily understood by those skilled in the art that the scope of the present disclosure is not limited to only these specific embodiments. The technical solutions in the above embodiments can be split and combined, and equivalent changes or substitutions can be made on related technical features by those skilled in the art without departing from the technical principles of the present disclosure, and any changes, equivalents, improvements, and the like made within the technical concept and/or technical principles of the present disclosure will fall within the protection scope of the present disclosure.
Claims (3)
1. A big data based security assessment analysis system, the system comprising:
the acquisition module is used for acquiring the running state data of the detection data to create a data safety information table; the operation state data is all security strategy data, protection technology data, vulnerability risk data and configuration non-conforming item data used in the whole life cycle from birth to destruction of big data;
the classification module is used for classifying the data in the data safety information table according to a preset classification algorithm to obtain a classification result; calculating the class coefficient of each classification;
the classification module further comprises a classification unit; the classification unit is used for vectorizing the detection data to obtain a retrieval data table; acquiring a preset distance interval, and taking any detection data from a retrieval data table as a classification central point; generating a central point set corresponding to the classification central point; sequentially taking any detection data from the retrieval data table as a calculation point, and determining a distance value between the calculation point and the classification central point; when the distance value is smaller than the preset distance interval, adding the detection data corresponding to the distance value to the corresponding central point set, and deleting the detection data from the retrieval data table; when the distance value is larger than the preset distance interval, setting the detection data corresponding to the distance value as a classification central point; acquiring residual retrieval data and all classification central points of the distance values in a preset distance interval; calculating the calculation distance between the residual retrieval data and each classification central point to add the residual retrieval data to the central point set of the classification central point corresponding to the minimum calculation distance so as to realize the classification of the detection data and obtain a classification result;
the classification module further comprises a coefficient calculation unit; the coefficient calculation unit is used for clustering the central point set to obtain a clustering center; acquiring preset main attribute values and preset attribute weights corresponding to the clustering centers, and determining the mean value of the products of the preset main attribute values and the preset attribute weights as a class coefficient;
the statistical module is used for obtaining the number of the categories through the classification result; wherein the number of categories is the statistical number of categories for obtaining the classification result;
the evaluation module is used for determining a safety evaluation attribute value corresponding to the detection data according to the safety evaluation data of the detection data in the data safety information table; determining the safety degree of the detected data according to the safety evaluation attribute value, the category coefficient and the category quantity;
wherein the security assessment data comprises at least confidentiality data, integrity data and availability data, the assessment module further comprising an attribute value calculation unit; the attribute value calculating unit is used for determining secret grade assignment, complete grade assignment and available grade assignment corresponding to safety evaluation data in the detection data through a trained decision tree algorithm; and determining the average value of the secret grade assignment, the complete grade assignment and the available grade assignment as the security assessment attribute value.
2. The big data security assessment based analysis system according to claim 1, wherein said system further comprises a prediction module;
and the prediction module is used for determining the safety level of a prediction system corresponding to the safety degree according to a preset safety degree database and the safety degree.
3. A big data security assessment-based analysis method is characterized by comprising the following steps:
acquiring running state data of the detection data to create a data safety information table; the operation state data is all security strategy data, protection technology data, vulnerability risk data and configuration non-conforming item data used in the whole life cycle from birth to destruction of big data;
classifying data in the data safety information table according to a preset classification algorithm to obtain a classification result; calculating the class coefficient of each classification;
wherein, the classification result is obtained by: vectorizing the detection data to obtain a retrieval data table; acquiring a preset distance interval, and taking any detection data from a retrieval data table as a classification central point; generating a central point set corresponding to the classification central point; sequentially taking any detection data from the retrieval data table as a calculation point, and determining a distance value between the calculation point and the classification central point; when the distance value is smaller than the preset distance interval, adding the detection data corresponding to the distance value to the corresponding central point set, and deleting the detection data from the retrieval data table; when the distance value is larger than the preset distance interval, setting the detection data corresponding to the distance value as a classification central point; acquiring residual retrieval data and all classification central points of the distance values in a preset distance interval; calculating the calculation distance between the residual retrieval data and each classification central point to add the residual retrieval data to the central point set of the classification central point corresponding to the minimum calculation distance so as to realize the classification of the detection data and obtain a classification result;
wherein, calculating the classification coefficient of each classification specifically comprises: clustering the central point set to obtain a clustering center; acquiring preset main attribute values and preset attribute weights corresponding to the clustering centers, and determining the mean value of the products of the preset main attribute values and the preset attribute weights as a class coefficient;
obtaining the number of categories of each category according to the classification result; wherein the number of categories is the statistical number of categories for obtaining the classification result;
determining a security evaluation attribute value corresponding to the detection data according to the security evaluation data of the detection data in the data security information table; determining the safety degree of the detected data according to the safety evaluation attribute value, the category coefficient and the category quantity; wherein the security assessment data comprises at least confidentiality data, integrity data and availability data;
the method comprises the following steps of determining a security assessment attribute value corresponding to detection data according to security assessment data of the detection data in a data security information table, and specifically comprises the following steps: determining confidentiality grade assignment, integrity grade assignment and available grade assignment corresponding to security evaluation data in the detection data through a trained decision tree algorithm; and determining the average value of the secret grade assignment, the complete grade assignment and the available grade assignment as the security assessment attribute value.
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| CN106682527A (en) * | 2016-12-25 | 2017-05-17 | 北京明朝万达科技股份有限公司 | Data security control method and system based on data classification and grading |
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