CN115456101A - Data security transmission method and system based on data center station - Google Patents
Data security transmission method and system based on data center station Download PDFInfo
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Abstract
According to the data security transmission method and the data security transmission system based on the data center station, the security evaluation topic to be mined is determined according to the corresponding acquired global feature vectors of each security evaluation topic in the period from history to current, so that the accuracy of the determined security evaluation topic to be mined is improved, and the reliability of the security evaluation topic is favorably ensured; and determining a security evaluation topic tag of each topic to be mined in the corresponding target evaluation data set according to the specified abnormal data set, and accurately classifying the security evaluation topic tags of the topic to be mined in the corresponding target evaluation data set, thereby ensuring the accuracy and reliability of the security evaluation topic tags.
Description
Technical Field
The application relates to the technical field of data security transmission, in particular to a data security transmission method and system based on a data center station.
Background
With the continuous development and progress of science and technology, data transmission is more and more frequent, and the transmission quantity is also more and more. However, during the data transmission process, there may be a problem that data transmission is abnormal during the data transmission process. It is difficult to ensure the accuracy and reliability of the security assessment topic tag.
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides a data security transmission method and system based on a data center station.
In a first aspect, a method for secure data transmission based on a data center station is provided, the method at least includes: acquiring a security information set to be mined, wherein the security information set to be mined is obtained by carrying out data classification processing and global feature analysis on a first current data defense event acquired in advance; determining a second current data co-defense event which has a correlation condition with a first data security topic in the security information set to be mined from current data co-defense events acquired in real time; determining each to-be-mined security assessment topic with an assessment score meeting a specified target value and a target assessment data set thereof in the security assessment topics corresponding to the first data security topic by combining the security assessment topic corresponding to the first data security topic and the acquired global feature vectors corresponding to each security assessment topic; and determining the security evaluation topic tags of the various to-be-mined security evaluation topics in the corresponding target evaluation data set according to the specified abnormal data set.
In an independently implemented embodiment, the obtaining the security information set to be mined includes: performing convolution processing on the collected first current data collaborative prevention event to obtain a data security vector; the number of the first current data protection events is several; performing data classification processing on the data security vector to obtain a plurality of data security topics; determining a security assessment topic label corresponding to each data security topic; counting the collected global feature vectors corresponding to the security assessment topics based on the collection period and the collection range of the first current data collaborative prevention event belonging to each data security topic; and determining the plurality of data security topics, the security assessment topic labels corresponding to the data security topics and the collected global feature vectors corresponding to the security assessment topic labels as the security information set to be mined.
In an independently implemented embodiment, the determining the security assessment topic tag corresponding to each data security topic includes: screening important security data in the security topics of all data; comparing the important security data with each appointed sample security data in the appointed abnormal data set to obtain a comparison result; on the premise that the comparison result shows that the specified sample security data related to the important security data exists, determining specified element labels corresponding to the specified sample security data as security assessment topic labels corresponding to the security topics of the data; and on the premise that the comparison result shows that the important security data is not associated with the specified sample security data in the specified abnormal data set, determining the label of each data security topic as a security assessment topic label corresponding to each data security topic.
In a separately implemented embodiment, the method further comprises: determining a third current data collaborative prevention event which is not associated with a plurality of data security topics in the security information set to be mined from current data collaborative prevention events acquired in real time; performing data classification processing on the third current data defense event to obtain a second data security theme; determining newly-added security assessment topic labels corresponding to the second data security topics; counting the collected global feature vectors corresponding to the newly added security assessment topics based on the collection period and the collection range of the third current data collaborative prevention event belonging to each second data security topic; and adding the security information set to be mined to the second data security theme, the newly added security assessment topic labels and the acquired global feature vectors corresponding to the newly added security assessment topic labels to obtain the optimized security information set to be mined.
In an independently implemented embodiment, the determining, from the current data collaborative prevention events collected in real time, a second current data collaborative prevention event that is associated with a first data security topic in the security information set to be mined, includes: determining important security data from the security topics of all data covered by the security information set to be mined; comparing each important security data with each current data collaborative prevention event collected in real time to obtain at least one group of interrelated important security data and current data collaborative prevention events; determining a data security theme to which the important security data belongs in the at least one group of interrelated important security data and the current data collaborative security event as the first data security theme; and determining the current data collaborative prevention event in the at least one group of correlated important security data and the current data collaborative prevention event as the second current data collaborative prevention event.
In an independently implemented embodiment, the obtaining at least one group of correlated important security data and current data collaborative prevention events by comparing each important security data with each current data collaborative prevention event collected in real time includes: extracting a first data security vector of each important security data and a second data security vector of each current data collaborative prevention event acquired in real time; determining a vector shareability variable between the first data security vector and the second data security vector; on the premise that the vector sharability variable accords with the shared variable target value, it is determined that each important security data is linked with each current data collaborative prevention event, and therefore at least one group of related important security data and current data collaborative prevention events are obtained.
In an independently implemented embodiment, the collecting global feature vectors corresponding to the security assessment topics comprises: the acquisition period and the acquisition range corresponding to each safety assessment topic; the step of determining each to-be-mined security assessment topic with an assessment score conforming to a specified target value and a target assessment data set thereof in the security assessment topics corresponding to the first data security topic by combining the security assessment topic corresponding to the first data security topic and the acquired global feature vectors corresponding to each security assessment topic comprises: determining an evaluation score of the security evaluation topic corresponding to each first data security topic in combination with the security evaluation topic corresponding to the first data security topic and the acquisition period corresponding to each security evaluation topic; determining the security evaluation topic to be mined with the evaluation score meeting a specified target value in combination with the evaluation score; and determining a target evaluation data set corresponding to each to-be-mined security evaluation topic by combining the acquisition range corresponding to each security evaluation topic.
In an independently implemented embodiment, the determining, in combination with the security assessment topic corresponding to the first data security topic and the acquisition period corresponding to each security assessment topic, an assessment score of the security assessment topic corresponding to each first data security topic includes: determining the evaluation times of the security evaluation topics corresponding to the first data security topics in a specified period according to the security evaluation topics corresponding to the first data security topics and the acquisition periods corresponding to the security evaluation topics; and determining the evaluation times as evaluation scores of the safety evaluation topics.
In an independently implemented embodiment, the determining, according to a specified abnormal data set, a security assessment topic tag of each security assessment topic to be mined in a corresponding target assessment data set includes: on the premise that each to-be-mined security assessment topic has a corresponding specified key element in a specified abnormal data set, determining a security assessment topic tag of each to-be-mined security assessment topic in a corresponding target assessment data set according to the corresponding specified key element; and/or on the premise that the fact that the various to-be-mined security assessment topics do not have corresponding specified key elements in the specified abnormal data set is determined, the fact that the various to-be-mined security assessment topics belong to the floating security assessment topics in the corresponding target assessment data set is determined.
In a separately implemented embodiment, the specifying an exception data set includes: appointing key elements and appointed element labels corresponding to the appointed key elements; the determining that each topic to be mined has a corresponding specified key element in a specified abnormal data set includes: associating the security evaluation topic tags corresponding to the security evaluation topics to be mined with the designated element tags in the designated abnormal data set to obtain association results; and on the premise that the association result indicates that the specified element tags associated with the security evaluation topic tags corresponding to the various to-be-mined security evaluation topics exist, determining that the specified key elements corresponding to the various to-be-mined security evaluation topics exist.
In an independently implemented embodiment, the determining that the respective topic of security assessment to be mined does not have a corresponding specified key element in a specified abnormal data set comprises: and on the premise that the association result indicates that no specified element tag associated with the security evaluation topic tag corresponding to each topic to be mined exists, determining that no corresponding specified key element exists in each topic to be mined.
In an independently implemented embodiment, the determining security assessment topic tags for the respective topics to be mined for security assessment within the corresponding target assessment dataset according to the corresponding specified key elements comprises: associating the range of the corresponding specified key element with each target evaluation data set corresponding to each security evaluation topic to be mined to obtain an association result; on the premise that the association result indicates that a target evaluation data set associated with the range to which the specified key element belongs exists, determining that the security evaluation topic tags of the security evaluation topics to be mined in the target evaluation data set are recorded security evaluation topics, and determining that the security evaluation topic tags of the security evaluation topics to be mined in the corresponding target evaluation data set which is not associated with the range to which the specified key element belongs are floating security evaluation topics; and/or on the premise that the association result indicates that the target evaluation data set corresponding to each topic to be mined is not associated with the range of the specified key element, determining that the security evaluation topic tags of each topic to be mined in the corresponding target evaluation data set are all floating security evaluation topics.
In a separately implemented embodiment, the method further comprises: determining a non-analytic security evaluation topic with an evaluation score not meeting a specified target value and a target evaluation data set thereof by combining the security evaluation topic corresponding to the first data security topic and the acquired global feature vectors corresponding to the security evaluation topics; determining that the respective non-parsed security assessment topics are current security assessment topics within the corresponding respective target assessment data set.
In a second aspect, a data center-based data secure transmission system is provided, which includes a processor and a memory, which are communicated with each other, and the processor is used for reading a computer program from the memory and executing the computer program to implement the method.
According to the data security transmission method and system based on the data center, a security information set to be mined, which is obtained by carrying out data classification processing and global feature analysis on a first current data collaborative prevention event collected in advance, is obtained, a second current data collaborative prevention event which is associated with a first data security topic in the security information set to be mined is determined from the current data collaborative prevention events collected in real time, and each security assessment topic with an assessment score meeting a specified target value in the security assessment topics corresponding to the first data security topic and the collected global feature vectors corresponding to the security assessment topics is determined according to the security assessment topic corresponding to the first data security topic and the collected global feature vectors corresponding to the security assessment topics, so that the security assessment topic to be mined can be determined according to the collected global feature vectors corresponding to the security assessment topics in a period from history to the current, the accuracy of the determined security assessment topic is improved, and the reliability of the security assessment topic is favorably ensured; and determining a security evaluation topic tag of each topic to be mined in the corresponding target evaluation data set according to the specified abnormal data set, and accurately classifying the security evaluation topic tags of the topic to be mined in the corresponding target evaluation data set, thereby ensuring the accuracy and reliability of the security evaluation topic tags.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart of a data secure transmission method based on a data relay station according to an embodiment of the present disclosure.
Fig. 2 is a block diagram of a data security transmission apparatus based on a data center station according to an embodiment of the present application.
Fig. 3 is an architecture diagram of a data security transmission system based on a data center station according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.
Referring to fig. 1, a method for secure data transmission based on a data center station is shown, which may include the following steps S101-S104.
S101, a security information set to be mined is obtained, and the security information set to be mined is obtained by carrying out data classification processing and global feature analysis on a first current data defense event acquired in advance.
In this embodiment of the application, the security information set to be mined may include: the method comprises the steps of carrying out data classification processing on a first current data defense event to obtain a plurality of data security topics, security assessment topic labels corresponding to the data security topics (each security assessment topic label is used for representing one security assessment topic), and collecting global feature vectors corresponding to the security assessment topic labels, wherein the collecting global feature vectors comprise a collecting period and a collecting range.
In one possible implementation example, a security information set to be mined is obtained by performing data classification processing and global feature analysis on a first current data defense event collected in advance.
S102, determining a second current data co-defense event which has a correlation condition with a first data security topic in a security information set to be mined from current data co-defense events collected in real time.
Furthermore, each data security topic included in the security information set to be mined covers at least one current data collaborative prevention event belonging to the same security assessment topic. According to the obtained security information set to be mined and the current data collaborative prevention events collected in the current period, some current data collaborative prevention events in the current data collaborative prevention events collected in real time are determined, and the association condition exists between some current data security protection topics covered in the security information set to be mined, so that a first data security protection topic in the security information set to be mined and a second current data collaborative prevention event in the current data collaborative prevention events collected in real time are determined.
S103, determining each to-be-mined security evaluation topic with an evaluation score conforming to a specified target value and a target evaluation data set thereof in the security evaluation topics corresponding to the first data security subject based on the security evaluation topic corresponding to the first data security subject and the acquired global feature vectors corresponding to each security evaluation topic.
Further, the security information set to be mined includes security assessment topic labels corresponding to the first data security topics and acquisition global feature vectors corresponding to the security assessment topic labels, and according to the security assessment topic labels corresponding to the first data security topics and the acquisition global feature vectors corresponding to the security assessment topic labels, the assessment scores of the security assessment topics are determined from all the security assessment topics represented by the security assessment topic labels corresponding to the first data security topics, the security assessment topics to be mined, of which the assessment scores meet specified target values, and the target assessment data sets corresponding to the security assessment topics to be mined are determined.
And S104, determining the security evaluation topic tags of the security evaluation topics to be mined in the corresponding target evaluation data set according to the specified abnormal data set.
For example, according to the specified abnormal data set and the security assessment topic tag corresponding to each topic to be mined, the security assessment topic tag of each topic to be mined in the corresponding target assessment data set can be determined. For example, the security assessment topic tag may include: a floating security assessment topic and a recorded security assessment topic, where the recorded security assessment topic may be, for example, an on-book security assessment topic. For example, on the premise that a security assessment topic tag corresponding to a to-be-mined security assessment topic is X, and the to-be-mined security assessment topic corresponds to a first target assessment data set and a second target assessment data set, whether the to-be-mined security assessment topic belongs to a floating security assessment topic or a recorded security assessment topic in the first target assessment data set or whether the to-be-mined security assessment topic belongs to a floating security assessment topic or a recorded security assessment topic in the second target assessment data set can be analyzed according to a specified abnormal data set and the security assessment topic tag X, so that a security assessment topic tag of the to-be-mined security assessment topic in the first target assessment data set and a security assessment topic tag of the to-be-mined security assessment topic in the second target assessment data set are finally obtained respectively.
In the embodiment of the application, a security information set to be mined, which is obtained by carrying out data classification processing and global feature analysis on a first current data collaborative prevention event collected in advance, is obtained, a second current data collaborative prevention event which has a correlation condition with a first data security topic in the security information set to be mined is determined from the current data collaborative prevention events collected in real time, and each security evaluation topic with an evaluation score meeting a specified target value in the security evaluation topics corresponding to the first data security topic is determined according to a security evaluation topic corresponding to the first data security topic and a collected global feature vector corresponding to each security evaluation topic, so that the security evaluation topic to be mined can be determined according to the collected global feature vector corresponding to each security evaluation topic in a period from history to the current, the accuracy of the determined security evaluation topic to be mined is improved, and the reliability of the security evaluation topic is favorably ensured; and determining a security evaluation topic tag of each topic to be mined in the corresponding target evaluation data set according to the specified abnormal data set, and accurately classifying the security evaluation topic tags of the topic to be mined in the corresponding target evaluation data set, thereby ensuring the accuracy and reliability of the security evaluation topic tags.
In a possible implementation embodiment, the security information set to be mined can be obtained by processing a first current data collaborative prevention event acquired in advance by the security information mining system to obtain the security information set to be mined. The above S101 can be realized by S1011-S1015.
S1011, performing convolution processing on the collected first current data collaborative prevention event to obtain a data security vector; the first current data protection event is several.
S1012, carrying out data classification processing on the data security vector to obtain a plurality of data security topics.
Illustratively, convolution processing can be performed on each first current data cooperation prevention event to obtain a data security vector corresponding to each first current data cooperation prevention event, and then data classification processing is performed on all extracted data security vectors according to an existing data classification processing algorithm, so that a plurality of data security topics can be obtained.
And S1013, determining the security evaluation topic labels corresponding to the security protection topics of the data.
Illustratively, the security assessment topic tags corresponding to the security topics of the data can be determined through an image association method.
In a possible implementation embodiment, for each data security theme, important security data in the data security theme can be screened, the important security data is compared with each specified sample security data in a specified abnormal data set to obtain a comparison result, and on the premise that the comparison result indicates that the specified sample security data associated with the important security data exists, a specified element tag corresponding to the specified sample security data is determined as a security evaluation topic tag corresponding to the data security theme; and on the premise that the comparison result shows that the important security data is not associated with the specified sample security data in the specified abnormal data set, determining the label of the data security theme as the security assessment topic label corresponding to the data security theme. The label of the data security theme can be a label which is specified for the data security theme according to a specified numbering mode and is distinguished from a specified element label.
For each data security theme, a current data collaborative prevention event can be screened from the data security theme and determined as important security data, wherein the important security data can be the current data collaborative prevention event with a large face range.
Illustratively, the comparison between an important security data and a specified sample security data can be realized by calculating a vector shared variable. The data security vector of the important security data and the data security vector of the specified sample security data can be respectively extracted, the vector sharing variable between the extracted data security vectors is calculated, and the obtained vector sharing variable is compared with a specified target value or a specified target value range, so that the magnitude relation between the vector sharing variable and the specified target value or the affiliated relation between the vector sharing variable and the specified target value range is obtained. On the premise that the obtained vector sharing variable is greater than or equal to the specified target value or the obtained vector sharing variable belongs to the specified target value range, the important security data can be determined to be associated with the specified sample security data; on the premise that the obtained vector sharing variable is smaller than the specified target value or does not belong to the specified target value range, the important security data and the specified sample security data can be determined to be not related.
In the embodiment of the application, the important security data of each data security theme is compared with the specified sample security data, so that the workload in the process of determining the security assessment topic labels corresponding to the data security themes can be reduced, and the processing efficiency is improved.
S1014, counting the collected global feature vectors corresponding to the security assessment topics based on the collection period and the collection range of the first current data collaborative prevention event belonging to each data security topic.
For each data security topic, statistics can be performed on security assessment topics corresponding to the data security topic in some acquisition cycles corresponding to some acquisition ranges according to acquisition cycles and acquisition ranges corresponding to each first current data defense event belonging to the data security topic, so that acquisition global feature vectors of the security assessment topics are obtained.
In one possible implementation, the acquisition global feature vector corresponding to each security assessment topic may be an acquisition period and an acquisition range corresponding to each unit period of the security assessment topic. For example, according to the acquisition period and the acquisition range corresponding to each first current data defense event belonging to the data security topic, whether the security assessment topic corresponding to the data security topic corresponds to the acquisition period and the acquisition range in each unit period, and whether some acquisition periods and some different acquisition ranges correspond to the security assessment topic in each unit period may be counted.
S1015, determining the plurality of data security topics, the security assessment topic labels corresponding to the data security topics and the collected global feature vectors corresponding to the security assessment topic labels as a security information set to be mined.
In a possible implementation example, after the above S101, the method further includes S201 to S205, where S201 to S205 may be executed synchronously with S102 to S104, or may be executed after any step in S102 to S104.
S201, determining a third current data cooperation prevention event which is not related to a plurality of data security topics in a security information set to be mined from the current data cooperation prevention events collected in real time.
For example, some current data collaborative prevention events are determined to be not associated with each data security topic included in the security information set to be mined from current data collaborative prevention events collected in a current period, and the current data collaborative prevention event which is not associated with each data security topic included in the security information set to be mined in the current data collaborative prevention events collected in the current period is determined to be a third current data collaborative prevention event.
S202, performing data classification processing on the third current data defense event to obtain a second data security theme.
Illustratively, convolution processing can be performed on each third current data cooperation prevention event to obtain a data security vector, and data classification processing is performed on the data security vector to obtain at least one second data security theme.
S203, determining the newly added security assessment topic label corresponding to each second data security topic.
Illustratively, the security assessment topic tag corresponding to each second data security topic is a new security assessment topic tag. The newly added security evaluation topic label corresponding to each second data security topic can be determined by the same method as S1013.
S204, counting the collected global feature vectors corresponding to the newly added security assessment topics based on the collection period and the collection range of the third current data collaborative prevention event belonging to each second data security topic.
Similarly, the collected global feature vectors corresponding to the newly added security assessment topics may be counted in the same manner as in S1014.
S205, adding the second data security topic, the newly added security assessment topic labels and the collected global feature vectors corresponding to the newly added security assessment topic labels to the security information set to be mined to obtain the optimized security information set to be mined.
By optimizing the security information set to be mined by the method, the optimized security information set to be mined can cover enough data security topics and security assessment topics, the accuracy of determining the second current data coordination event in the next real-time collected current data coordination event is improved, and therefore the reliability of the security assessment topics is favorably ensured.
In one possible implementation, the above S102 may be implemented by S1021 to S1024.
S1021, important security data are determined from all data security topics covered by the security information set to be mined.
S1022, comparing each important security data with each current data collaborative prevention event collected in real time to obtain at least one group of correlated important security data and current data collaborative prevention events.
For example, a first data security vector of each important security data and a second data security vector of each current data collaborative prevention event collected in real time can be extracted, and a vector sharing variable between the first data security vector and the second data security vector is determined; on the premise that the vector sharing variable accords with the sharing variable target value, the important security data is determined to be linked with the current data collaborative prevention event, and therefore at least one group of related important security data and the current data collaborative prevention event are obtained.
S1023, determining a data security theme to which the important security data belongs in at least one group of correlated important security data and the current data security event as a first data security theme.
And S1024, determining at least one group of related important security data and the current data defense event in the current data defense event as a second current data defense event.
In one possible implementation, the collecting global feature vector corresponding to each security assessment topic comprises: acquiring periods and acquiring ranges corresponding to the security assessment topics; based on this, the above S103 may be realized by S1031 to S1033.
And S1031, determining the evaluation scores of the safety evaluation topics corresponding to the first data security topics based on the safety evaluation topics corresponding to the first data security topics and the acquisition periods corresponding to the safety evaluation topics.
In the embodiment of the application, the evaluation times of the security evaluation topics corresponding to the first data security topics in a specified period can be determined according to the security evaluation topics corresponding to the first data security topics and the acquisition periods corresponding to the security evaluation topics, and the evaluation times are determined as the evaluation scores of the security evaluation topics.
S1032, determining the security evaluation topic to be excavated, of which the evaluation score is in accordance with the specified target value, based on the evaluation score.
S1033, determining a target evaluation data set corresponding to each to-be-mined security evaluation topic based on the acquisition range corresponding to each security evaluation topic.
For example, according to the acquisition ranges corresponding to the security assessment topics of all the first data security topics, the acquisition range corresponding to each to-be-mined security assessment topic in a specified period is determined, and after a repeated range is removed from the determined acquisition range, the remaining acquisition range is determined as one or a plurality of different target assessment data sets corresponding to the to-be-mined security assessment topic.
In a possible implementation, the above S104 may be implemented by S1041 and S1042, or may be implemented by S1041 or S1042.
S1041, on the premise that each topic to be mined for safety assessment has a corresponding specified key element in the specified abnormal data set, determining a safety assessment topic tag of each topic to be mined for safety assessment in the corresponding target assessment data set according to the corresponding specified key element.
S1042, on the premise that each to-be-mined security assessment topic does not have a corresponding specified key element in the specified abnormal data set, determining that each to-be-mined security assessment topic belongs to a floating security assessment topic in the corresponding target assessment data set.
In one possible implementation, specifying the outlier data set comprises: specifying key elements and specified element tags corresponding to the specified key elements, so that the determination in S1041 that the corresponding specified key elements exist in the specified abnormal data set for each topic to be mined for security assessment can be implemented by: associating the security evaluation topic tag corresponding to each security evaluation topic to be mined with each specified element tag in the specified abnormal data set to obtain an association result; and on the premise that the association result indicates that the specified element tags associated with the security evaluation topic tags corresponding to the security evaluation topics to be mined exist, determining that the specified key elements corresponding to the security evaluation topics to be mined exist.
For example, for a topic to be mined for security assessment, on the premise that the association result indicates that there is a specified element tag associated with the security assessment topic tag corresponding to the topic to be mined for security assessment, a specified key element corresponding to the specified element tag may be determined as a specified key element corresponding to the topic to be mined for security assessment.
In a possible implementation example, the determining in S1042 that each topic to be mined has no corresponding specified key element in the specified abnormal data set may be implemented by: and on the premise that the association result indicates that no specified element tag associated with the security evaluation topic tag corresponding to each topic to be mined exists, determining that no corresponding specified key element exists in each topic to be mined.
In a possible implementation example, the determining of the security assessment topic tag of each security assessment topic to be mined in the corresponding target assessment data set according to the corresponding specified key element in S1041 may be implemented through S301 to S303, or through S301, and S302 or S303.
S301, associating the range of the corresponding specified key element with each target evaluation data set corresponding to each security evaluation topic to be mined to obtain an association result.
And S302, on the premise that the association result indicates that the target evaluation data set associated with the range to which the specified key element belongs exists, determining that the security evaluation topic tags of the security evaluation topics to be mined in the target evaluation data set are recorded security evaluation topics, and determining that the security evaluation topic tags of the security evaluation topics to be mined in the target evaluation data set which correspond to the security evaluation topics and are not associated with the range to which the specified key element belongs are floating security evaluation topics.
And S303, on the premise that the association result shows that the target evaluation data set corresponding to each topic to be mined is not associated with the range of the specified key element, determining that the security evaluation topic tags of each topic to be mined in the corresponding target evaluation data set are all floating security evaluation topics.
In one possible implementation, for a given key element and a target evaluation dataset, the given key element may be determined to be associated with the target evaluation dataset on the premise that the target evaluation dataset belongs to the given key element or the given key element is in the same range as the target evaluation dataset.
In a possible embodiment, the method further includes S401-S402; s401 to S402 may be executed synchronously with S103 to S104, or may be executed after S103 or S104, which is not limited in the embodiment of the present application; wherein S401-S402 are as follows:
s401, determining a non-analytic security evaluation topic with an evaluation score not conforming to a specified target value and a target evaluation data set thereof based on a security evaluation topic corresponding to a first data security topic and a collection global feature vector corresponding to each security evaluation topic.
For example, on the premise that the evaluation score of the security evaluation topic corresponding to each first data security topic is determined by using the method in S1041, the security evaluation topic whose evaluation score does not meet the specified target value is determined as a non-analytic security evaluation topic according to the evaluation score of the security evaluation topic corresponding to each first data security topic, and then, one or several different target evaluation data sets corresponding to each non-analytic security evaluation topic are determined by using the method in S1043.
S402, determining each non-analytic security evaluation topic as a current security evaluation topic in each corresponding target evaluation data set.
On the basis of the above, please refer to fig. 2 in combination, there is provided an apparatus 200 for secure data transmission based on a data center station, which is applied to a system for secure data transmission based on a data center station, the apparatus includes:
the information acquisition module 210 is configured to acquire a security information set to be mined, where the security information set to be mined is obtained by performing data classification processing and global feature analysis on a first current data defense event acquired in advance;
the data evaluation module 220 is configured to determine, from current data collaborative prevention events collected in real time, a second current data collaborative prevention event having a correlation condition with a first data security topic in the security information set to be mined; determining each to-be-mined security assessment topic with an assessment score meeting a specified target value and a target assessment data set thereof in the security assessment topics corresponding to the first data security topic by combining the security assessment topic corresponding to the first data security topic and the acquired global feature vectors corresponding to each security assessment topic;
a tag determining module 230, configured to determine, according to the specified abnormal data set, security assessment topic tags of the security assessment topics to be mined in the corresponding target assessment data set.
On the basis of the above, please refer to fig. 3, which shows a data security transmission system 300 based on a data center station, comprising a processor 310 and a memory 320, which are communicated with each other, wherein the processor 310 is configured to read a computer program from the memory 320 and execute the computer program to implement the above method.
On the basis of the above, there is also provided a computer-readable storage medium on which a computer program is stored, which when executed implements the above-described method.
In summary, based on the above scheme, a security information set to be mined, which is obtained by performing data classification processing and global feature analysis on a first current data collaborative prevention event collected in advance, is obtained, a second current data collaborative prevention event having a correlation condition with a first data security topic in the security information set to be mined is determined from the current data collaborative prevention events collected in real time, and each security evaluation topic with an evaluation score meeting a specified target value in security evaluation topics corresponding to a first data security topic is determined according to a security evaluation topic corresponding to the first data security topic and a collected global feature vector corresponding to each security evaluation topic, so that the security evaluation topic to be mined can be determined according to the collected global feature vector corresponding to each security evaluation topic in a period from history to the current, the accuracy of the determined security evaluation topic to be mined is improved, and the reliability of the security evaluation topic is favorably ensured; and determining a security assessment topic tag of each topic to be mined in the corresponding target assessment data set according to the specified abnormal data set, and accurately classifying the security assessment topic tags of the topic to be mined in the corresponding target assessment data set, thereby ensuring the accuracy and reliability of the security assessment topic tags.
It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).
It is to be noted that different embodiments may produce different advantages, and in different embodiments, the advantages that may be produced may be any one or combination of the above, or any other advantages that may be obtained.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered as illustrative only and not limiting of the application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.
Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C + +, C #, VB.NET, python, and the like, a conventional programming language such as C, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, unless explicitly recited in the claims, the order of processing elements and sequences, use of numbers and letters, or use of other designations in this application is not intended to limit the order of the processes and methods in this application. While certain presently contemplated useful embodiments of the invention have been discussed in the foregoing disclosure by way of various examples, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments of the disclosure. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A data security transmission method based on a data center station, characterized in that the method at least comprises:
acquiring the security information set to be mined, wherein the security information set to be mined is obtained by carrying out data classification processing and global feature analysis on a first current data defense event acquired in advance;
determining a second current data co-defense event which has a correlation condition with a first data security topic in the security information set to be mined from current data co-defense events acquired in real time; determining each to-be-mined security assessment topic with an assessment score meeting a specified target value and a target assessment data set thereof in the security assessment topics corresponding to the first data security topic by combining the security assessment topic corresponding to the first data security topic and the acquired global feature vectors corresponding to each security assessment topic;
and determining the security evaluation topic tags of the various to-be-mined security evaluation topics in the corresponding target evaluation data set according to the specified abnormal data set.
2. The method of claim 1, wherein the obtaining the set of security information to be mined comprises:
performing convolution processing on the acquired first current data collaborative prevention event to obtain a data security vector; the number of the first current data protection events is several;
performing data classification processing on the data security vector to obtain a plurality of data security topics;
determining security assessment topic labels corresponding to the data security topics;
counting the collected global feature vectors corresponding to the security assessment topics based on the collection period and the collection range of the first current data collaborative prevention event belonging to each data security topic;
and determining the plurality of data security topics, the security assessment topic labels corresponding to the data security topics and the collected global feature vectors corresponding to the security assessment topic labels as the security information set to be mined.
3. The method of claim 2, wherein the determining the security assessment topic tag corresponding to each data security topic comprises:
screening important security data in the security topics of all the data;
comparing the important security data with each appointed sample security data in the appointed abnormal data set to obtain a comparison result;
on the premise that the comparison result shows that the specified sample security data related to the important security data exists, determining specified element labels corresponding to the specified sample security data as security assessment topic labels corresponding to the security topics of the data;
and on the premise that the comparison result shows that the important security data is not associated with the specified sample security data in the specified abnormal data set, determining the label of each data security topic as a security assessment topic label corresponding to each data security topic.
4. The method of claim 3, further comprising:
determining a third current data collaborative prevention event which is not related to a plurality of data security topics in the security information set to be mined from current data collaborative prevention events collected in real time;
performing data classification processing on the third current data defense event to obtain a second data security theme;
determining a newly added security assessment topic label corresponding to each second data security topic;
counting the collected global feature vectors corresponding to the newly added security assessment topics based on the collection period and the collection range of the third current data collaborative prevention event belonging to each second data security topic;
and adding the security information set to be mined to the second data security theme, the newly added security assessment topic labels and the acquired global feature vectors corresponding to the newly added security assessment topic labels to obtain the optimized security information set to be mined.
5. The method of claim 4, wherein the determining, from the current data co-defense events collected in real-time, a second current data co-defense event associated with a first data security topic in the security information set to be mined comprises:
important security data are determined from the security topics of all data covered by the security information set to be mined;
comparing each important security data with each current data collaborative prevention event collected in real time to obtain at least one group of interrelated important security data and current data collaborative prevention events;
determining a data security theme to which the important security data belongs in the at least one group of interrelated important security data and the current data collaborative security event as the first data security theme;
and determining the current data collaborative prevention event in the at least one group of the correlated important security data and the current data collaborative prevention event as the second current data collaborative prevention event.
6. The method as claimed in claim 5, wherein the obtaining of at least one group of correlated important security data and current data co-defense events by comparing each important security data with each current data co-defense event collected in real time comprises:
extracting a first data security vector of each important security data and a second data security vector of each current data collaborative security event acquired in real time;
determining a vector shareability variable between the first data security vector and the second data security vector;
on the premise that the vector sharability variable accords with the shared variable target value, it is determined that each important security data is linked with each current data collaborative prevention event, and therefore at least one group of related important security data and current data collaborative prevention events are obtained.
7. The method of claim 6, wherein the collecting global feature vector for each security assessment topic comprises: the acquisition period and the acquisition range corresponding to each security assessment topic; the determining, by combining the security assessment topics corresponding to the first data security topic and the acquired global feature vectors corresponding to the security assessment topics, each to-be-mined security assessment topic and the target assessment data set thereof, of which the assessment scores meet a specified target value, in the security assessment topics corresponding to the first data security topic, includes: determining an evaluation score of each safety evaluation topic corresponding to each first data security topic by combining the safety evaluation topic corresponding to the first data security topic and the acquisition period corresponding to each safety evaluation topic; determining the security evaluation topic to be mined with the evaluation score meeting a specified target value in combination with the evaluation score; and determining a target evaluation data set corresponding to each to-be-mined security evaluation topic by combining the acquisition range corresponding to each security evaluation topic.
8. The method of claim 7, wherein the determining the evaluation score of the security evaluation topic corresponding to each first data security topic in combination with the security evaluation topic corresponding to the first data security topic and the acquisition period corresponding to each security evaluation topic comprises: determining the evaluation times of the security evaluation topics corresponding to the first data security topics in a specified period according to the security evaluation topics corresponding to the first data security topics and the acquisition period corresponding to the security evaluation topics; and determining the evaluation times as evaluation scores of the safety evaluation topics.
9. The method of claim 8, wherein the determining the security assessment topic tag of each security assessment topic to be mined within the corresponding target assessment data set according to the specified abnormal data set comprises:
on the premise that each to-be-mined security assessment topic has a corresponding specified key element in a specified abnormal data set, determining a security assessment topic tag of each to-be-mined security assessment topic in a corresponding target assessment data set according to the corresponding specified key element;
and/or on the premise that each to-be-mined security assessment topic does not have a corresponding specified key element in a specified abnormal data set, determining that each to-be-mined security assessment topic belongs to a floating security assessment topic in a corresponding target assessment data set;
wherein the specifying an exception dataset comprises: appointing key elements and appointed element labels corresponding to the appointed key elements;
the determining that each topic to be mined has a corresponding specified key element in a specified abnormal data set includes: associating the security evaluation topic tags corresponding to the security evaluation topics to be mined with the designated element tags in the designated abnormal data set to obtain association results;
on the premise that the association result indicates that the specified element tags associated with the security evaluation topic tags corresponding to the security evaluation topics to be mined exist, determining that the specified key elements corresponding to the security evaluation topics to be mined exist;
wherein the determining that each topic to be mined for security assessment does not have a corresponding specified key element in a specified abnormal data set includes: on the premise that the association result indicates that no specified element tag associated with the security evaluation topic tag corresponding to each topic to be mined exists, determining that no corresponding specified key element exists in each topic to be mined;
wherein the determining the security assessment topic tags of the respective to-be-mined security assessment topics in the corresponding target assessment data set according to the corresponding specified key elements comprises:
associating the range of the corresponding specified key element with each target evaluation data set corresponding to each security evaluation topic to be mined to obtain an association result;
on the premise that the association result indicates that a target evaluation data set associated with the range to which the specified key element belongs exists, determining that the security evaluation topic tags of the security evaluation topics to be mined in the target evaluation data set are recorded security evaluation topics, and determining that the security evaluation topic tags of the security evaluation topics to be mined in the corresponding target evaluation data set which is not associated with the range to which the specified key element belongs are floating security evaluation topics;
and/or on the premise that the association result indicates that the target evaluation data set corresponding to each topic to be mined is not associated with the range of the specified key element, determining that the security evaluation topic tags of each topic to be mined in the corresponding target evaluation data set are floating security evaluation topics;
wherein the method further comprises: determining a non-analytic security evaluation topic with an evaluation score not meeting a specified target value and a target evaluation data set thereof by combining a security evaluation topic corresponding to the first data security subject and an acquired global feature vector corresponding to each security evaluation topic; determining that the respective non-resolved security assessment topic is a current security assessment topic within the corresponding respective target assessment dataset.
10. A data station-based secure data transmission system, comprising a processor and a memory communicating with each other, the processor being adapted to read a computer program from the memory and execute it, so as to implement the system according to any one of claims 1 to 9.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105119750A (en) * | 2015-09-08 | 2015-12-02 | 南京联成科技发展有限公司 | Distributed information security operation and maintenance management platform based on massive data |
| CN110765337A (en) * | 2019-11-15 | 2020-02-07 | 中科院计算技术研究所大数据研究院 | Service providing method based on internet big data |
| CN111740993A (en) * | 2020-06-18 | 2020-10-02 | 河南优易信息技术有限公司 | Internal and external network safety data exchange method |
| CN112333288A (en) * | 2021-01-04 | 2021-02-05 | 三盟科技股份有限公司 | Intelligent classroom data safety protection method, system and readable storage medium |
| CN112685787A (en) * | 2021-02-23 | 2021-04-20 | 张雪君 | Big data information security protection method applied to artificial intelligence and cloud server |
| CN112818398A (en) * | 2021-02-06 | 2021-05-18 | 陈笑男 | Data processing method and big data processing equipment for big data privacy protection |
| CN113347193A (en) * | 2021-06-16 | 2021-09-03 | 广州云莫凡信息科技有限公司 | Data request permission method and system based on cloud server and cloud platform |
| CN113435280A (en) * | 2021-06-18 | 2021-09-24 | 上海熙瑾信息技术有限公司 | Testimony verification method |
| CN113595926A (en) * | 2021-07-28 | 2021-11-02 | 南方电网数字电网研究院有限公司 | API data transmission method, device, equipment and medium based on data middlebox |
| CN113656808A (en) * | 2021-08-31 | 2021-11-16 | 平安医疗健康管理股份有限公司 | Data security evaluation method, device, equipment and storage medium |
| CN114493535A (en) * | 2022-01-28 | 2022-05-13 | 北京乐开科技有限责任公司 | Asset model-based data center system for data driving |
| CN114924943A (en) * | 2022-05-27 | 2022-08-19 | 中国平安财产保险股份有限公司 | Data middling station evaluation method based on artificial intelligence and related equipment |
-
2022
- 2022-09-23 CN CN202211165208.XA patent/CN115456101B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105119750A (en) * | 2015-09-08 | 2015-12-02 | 南京联成科技发展有限公司 | Distributed information security operation and maintenance management platform based on massive data |
| CN110765337A (en) * | 2019-11-15 | 2020-02-07 | 中科院计算技术研究所大数据研究院 | Service providing method based on internet big data |
| CN111740993A (en) * | 2020-06-18 | 2020-10-02 | 河南优易信息技术有限公司 | Internal and external network safety data exchange method |
| CN112333288A (en) * | 2021-01-04 | 2021-02-05 | 三盟科技股份有限公司 | Intelligent classroom data safety protection method, system and readable storage medium |
| CN112818398A (en) * | 2021-02-06 | 2021-05-18 | 陈笑男 | Data processing method and big data processing equipment for big data privacy protection |
| CN112685787A (en) * | 2021-02-23 | 2021-04-20 | 张雪君 | Big data information security protection method applied to artificial intelligence and cloud server |
| CN113347193A (en) * | 2021-06-16 | 2021-09-03 | 广州云莫凡信息科技有限公司 | Data request permission method and system based on cloud server and cloud platform |
| CN113435280A (en) * | 2021-06-18 | 2021-09-24 | 上海熙瑾信息技术有限公司 | Testimony verification method |
| CN113595926A (en) * | 2021-07-28 | 2021-11-02 | 南方电网数字电网研究院有限公司 | API data transmission method, device, equipment and medium based on data middlebox |
| CN113656808A (en) * | 2021-08-31 | 2021-11-16 | 平安医疗健康管理股份有限公司 | Data security evaluation method, device, equipment and storage medium |
| CN114493535A (en) * | 2022-01-28 | 2022-05-13 | 北京乐开科技有限责任公司 | Asset model-based data center system for data driving |
| CN114924943A (en) * | 2022-05-27 | 2022-08-19 | 中国平安财产保险股份有限公司 | Data middling station evaluation method based on artificial intelligence and related equipment |
Non-Patent Citations (4)
| Title |
|---|
| LAURA GIARRÉ ET AL.: "Coloring-based resource allocations in ad-hoc wireless networks", 《2011 THE 10TH IFIP ANNUAL MEDITERRANEAN AD HOC NETWORKING WORKSHOP》 * |
| 刘晓疆等: "大型枢纽机场安检信息***设计", 《信息技术与信息化》 * |
| 邓娟等: "数据治理助力数据中台能力建设的探索与实践", 《电脑知识与技术》 * |
| 邓子健: "WiiSE网络应用安全技术研究", 《中国博士学位论文全文数据库 (信息科技辑)》 * |
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