CN115456101B - Data security transmission method and system based on data center - Google Patents
Data security transmission method and system based on data center Download PDFInfo
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- CN115456101B CN115456101B CN202211165208.XA CN202211165208A CN115456101B CN 115456101 B CN115456101 B CN 115456101B CN 202211165208 A CN202211165208 A CN 202211165208A CN 115456101 B CN115456101 B CN 115456101B
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Abstract
According to the data security transmission method and system based on the data center, the security assessment topics to be mined are determined according to the collected global feature vectors corresponding to the security assessment topics in the period from history to current, the accuracy of the determined security assessment topics to be mined is improved, and the reliability of the security assessment topics is guaranteed; and by determining the target evaluation data set corresponding to each safety evaluation topic to be mined and determining the safety evaluation topic label of each safety evaluation topic to be mined in the corresponding target evaluation data set according to the appointed abnormal data set, the safety evaluation topic label of each safety evaluation topic to be mined can be accurately classified in each corresponding target evaluation data set, so that the accuracy and the reliability of the safety evaluation topic label are ensured.
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 technology, data transmission is more frequent and the transmission amount is larger. However, in the data transmission process, there is a possibility that data transmission abnormality occurs in 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 improve 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 securely transmitting data based on a data center station is provided, the method at least includes: the method comprises the steps of obtaining 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 co-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; combining the security evaluation topics corresponding to the first data security subjects and the collected global feature vectors corresponding to the security evaluation topics to determine each security evaluation topic to be mined and the target evaluation data set of each security evaluation topic to be mined, wherein the evaluation score of each security evaluation topic to be mined accords with a specified target value; and determining the security evaluation topic labels of the security evaluation topics to be mined in the corresponding target evaluation data sets according to the specified abnormal data sets.
In an independently implemented embodiment, the obtaining the security information set to be mined includes: carrying out convolution processing on the acquired first current data co-defense event to obtain a data security vector; the number of the first current data collaborative prevention events is several; the data security vectors are subjected to data classification processing to obtain a plurality of data security topics; determining a security evaluation topic label corresponding to each data security topic; based on the acquisition period and the acquisition range of the first current data co-defense event belonging to each data security topic, counting the acquisition global feature vectors corresponding to each security assessment topic; and determining the plurality of data security topics, security assessment topic labels corresponding to the data security topics and acquisition global feature vectors corresponding to the security assessment topic labels as the security information set to be mined.
In an independent embodiment, the determining the security assessment topic label corresponding to each data security topic includes: screening important security data in the security topics of the data; comparing the important security data with security data of each appointed sample in the appointed abnormal data set to obtain a comparison result; on the premise that the comparison result shows that the appointed sample security data related to the important security data exists, determining an appointed element label corresponding to the appointed sample security data as a security evaluation topic label corresponding to each data security topic; 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 evaluation topic label corresponding to each data security topic.
In an independently implemented embodiment, the method further comprises: determining a third current data co-defense event which is not related to a plurality of data security topics in the security information set to be mined from the current data co-defense events acquired in real time; performing data classification processing on the third current data security events to obtain a second data security theme; determining newly added security assessment topic labels corresponding to the second data security topics; based on the collection period and collection range of the third current data cooperative prevention event belonging to each second data security topic, counting the collection global feature vector corresponding to each newly-added security assessment topic; and adding the security information set to be mined to the second data security topic, the newly added security evaluation topic labels and the collected global feature vectors corresponding to the newly added security evaluation topic labels to obtain an optimized security information set to be mined.
In an embodiment of the independent implementation, the determining, from the current data security events collected in real time, a second current data security event having an association with a first data security topic in the security information set to be mined includes: determining important security data from each data security topic covered by the security information set to be mined; comparing each important security data with each current data co-protection event acquired in real time to obtain at least one group of inter-related important security data and current data co-protection event; determining a data security topic to which the important security data belongs in the at least one group of related important security data and the current data co-security event as the first data security topic; and determining the current data co-defense event in the at least one group of correlated important security data and the current data co-defense event as the second current data co-defense event.
In an independent embodiment, the comparing each important security data with each current data security event acquired in real time to obtain at least one group of related important security data and current data security event includes: extracting a first data security vector of each important security data and a second data security vector of each current data security event acquired in real time; determining a vector sharing variable between the first data security vector and the second data security vector; on the premise that the vector sharing variable accords with a sharing variable target value, determining that each important security data is associated with each current data security event, and accordingly obtaining at least one group of important security data and current data security events which are associated with each other.
In an independently implemented embodiment, the collecting global feature vectors corresponding to the respective security assessment topics includes: the collection period and the collection range corresponding to each security assessment topic; the determining, by combining the security evaluation topics corresponding to the first data security subject and the collected global feature vectors corresponding to the security evaluation topics, each security evaluation topic to be mined and the target evaluation data set thereof, where the evaluation score meets the specified target value, includes: combining the security evaluation topics corresponding to the first data security topics and the acquisition periods corresponding to the security evaluation topics to determine evaluation scores of the security evaluation topics corresponding to the first data security topics; combining the evaluation scores, and determining a security evaluation topic to be mined, wherein the evaluation scores accord with a specified target value; and determining target evaluation data sets corresponding to the safety evaluation topics to be mined according to the acquisition ranges corresponding to the safety evaluation topics.
In an embodiment of the independent implementation, the determining, in combination with the security assessment topics corresponding to the first data security topics and the collection periods corresponding to the security assessment topics, an assessment score of the security assessment topics corresponding to the first data security topics includes: according to the security evaluation topics corresponding to the first data security topics and the acquisition periods corresponding to the security evaluation topics, determining the evaluation times of the security evaluation topics corresponding to the first data security topics in a designated period; and determining the evaluation times as evaluation scores of the security evaluation topics.
In an independent embodiment, the determining, according to the specified abnormal data set, the security assessment topic label of the to-be-mined security assessment topic in the corresponding target assessment data set includes: on the premise that the corresponding specified key elements exist in the specified abnormal data set in the each to-be-mined security evaluation topic, determining the security evaluation topic label of the each to-be-mined security evaluation topic in the corresponding target evaluation data set according to the corresponding specified key elements; and/or on the premise that the corresponding specified key elements do not exist in the specified abnormal data set, determining that the to-be-mined safety evaluation topics belong to floating safety evaluation topics in the corresponding target evaluation data set.
In an independently implemented embodiment, the specifying the abnormal dataset includes: designating key elements and designating element labels corresponding to the designated key elements; the determining that the corresponding specified key elements exist in the specified abnormal data set for each to-be-mined security assessment topic includes: associating the security evaluation topic labels corresponding to the security evaluation topics to be mined with the appointed element labels in the appointed abnormal data set to obtain association results; on the premise that the association result indicates that the appointed key element label associated with the security assessment topic label corresponding to each security assessment topic to be mined exists, determining that the appointed key element corresponding to each security assessment topic to be mined exists.
In an independent embodiment, the determining that the respective to-be-mined security assessment topics do not have corresponding specified key elements in the specified exception data set includes: on the premise that the association result indicates that the appointed key element label associated with the security assessment topic label corresponding to each security assessment topic to be mined does not exist, determining that the corresponding appointed key element does not exist in each security assessment topic to be mined.
In an independent embodiment, the determining, according to the corresponding specified key element, a security assessment topic label of the to-be-mined security assessment topic in the corresponding target assessment data set includes: correlating the range of the corresponding appointed key element with each target evaluation data set corresponding to each security evaluation question to be mined to obtain a correlation result; on the premise that the association result indicates that a target evaluation data set associated with the range of the specified key element exists, determining that the security evaluation topic labels 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 labels of the security evaluation topics to be mined in the corresponding target evaluation data set not associated with the range of the specified key element are floating security evaluation topics; and/or on the premise that the association result indicates that the target evaluation data sets corresponding to the security evaluation topics to be mined are not associated with the range to which the specified key element belongs, determining that the security evaluation topic labels of the security evaluation topics to be mined in the corresponding target evaluation data sets are all floating security evaluation topics.
In an independently implemented embodiment, the method further comprises: combining the security evaluation topics corresponding to the first data security subjects and the collected global feature vectors corresponding to the security evaluation topics to determine non-analytic security evaluation topics and target evaluation data sets thereof, wherein the evaluation scores of the non-analytic security evaluation topics do not accord with the designated target values; and determining the non-analytic security assessment topics as current security assessment topics in corresponding target assessment data sets.
In a second aspect, a data security transmission system based on a data center is provided, comprising a processor and a memory in communication with each other, the processor being configured to read a computer program from the memory and execute the computer program to implement the method described above.
According to the data security transmission method and system based on the data center, the security information set to be mined, which is obtained by carrying out data classification processing and global feature analysis on the first current data security events acquired in advance, is obtained, the second current data security event which is related to the first data security topic in the security information set to be mined is determined from the current data security events acquired in real time, and the security evaluation topics corresponding to the first data security topics and the global feature vectors acquired corresponding to the security evaluation topics are determined, wherein the evaluation score accords with the global feature vectors acquired corresponding to the appointed target value in the security evaluation topics corresponding to the first data security topics, so that the security evaluation topics to be mined can be determined according to the global feature vectors acquired corresponding to the security evaluation topics in the period from history to current, the accuracy of the determined security evaluation topics to be mined is improved, and the reliability of the security evaluation topics to be mined is guaranteed; and by determining the target evaluation data set corresponding to each safety evaluation topic to be mined and determining the safety evaluation topic label of each safety evaluation topic to be mined in the corresponding target evaluation data set according to the appointed abnormal data set, the safety evaluation topic label of each safety evaluation topic to be mined can be accurately classified in each corresponding target evaluation data set, so that the accuracy and the reliability of the safety evaluation topic label are ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a data security transmission method based on a data center station according to an embodiment of the present application.
Fig. 2 is a block diagram of a data security transmission device based on a data center station according to an embodiment of the present application.
Fig. 3 is a schematic 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 above technical solutions, the following detailed description of the technical solutions of the present application is made by using the accompanying drawings and specific embodiments, and it should be understood that the specific features of the embodiments and the embodiments of the present application are detailed descriptions of the technical solutions of the present application, and not limiting the technical solutions of the present application, and the technical features of the embodiments and the embodiments of the present application may be combined with each other without conflict.
Referring to fig. 1, a data security transmission method based on a data center is shown, and the method may include the following technical solutions described in steps S101-S104.
S101, 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 collaborative security event acquired in advance.
In the embodiment of the present 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 co-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, a security information set to be mined, which is obtained by performing data classification processing and global feature analysis on a first current data co-occurrence acquired in advance, is obtained.
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 acquired in real time.
Further, each data security topic included in the security information set to be mined covers at least one current data security event belonging to the same security assessment topic. According to the obtained security information set to be mined and the current data cooperative protection events acquired in the current period, some current data cooperative protection events in the current data cooperative protection events acquired in real time can be determined, and the correlation conditions exist between the current data cooperative protection events and some data security topics covered in the security information set to be mined, so that a first data security topic in the security information set to be mined and a second current data cooperative protection event in the current data cooperative protection events acquired in real time are determined.
S103, based on the security evaluation topics corresponding to the first data security subjects and the collected global feature vectors corresponding to the security evaluation topics, determining each security evaluation topic to be mined and the target evaluation data set of each security evaluation topic to be mined, wherein the evaluation score of each security evaluation topic corresponds to the first data security subjects.
Further, the security information set to be mined includes security evaluation topic labels corresponding to the first data security topics and collected global feature vectors corresponding to the security evaluation topic labels, and according to the security evaluation topic labels corresponding to the first data security topics and the collected global feature vectors corresponding to the security evaluation topic labels, evaluation scores of the security evaluation topics are determined from all security evaluation topics represented by the security evaluation topic labels corresponding to all the first data security topics, the security evaluation topics to be mined, the evaluation scores of which meet specified target values, and target evaluation data sets corresponding to the security evaluation topics to be mined are determined.
S104, determining the security evaluation topic labels of the security evaluation topics to be mined in the corresponding target evaluation data sets according to the specified abnormal data sets.
For example, according to the specified abnormal data set and the security evaluation topic labels corresponding to the security evaluation topics to be mined, the security evaluation topic labels of the security evaluation topics to be mined in the corresponding target evaluation 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, wherein the recorded security assessment topic may be, for example, an in-album security assessment topic. For example, on the premise that a security evaluation topic label corresponding to a security evaluation topic to be mined is X and the security evaluation topic to be mined corresponds to a first target evaluation data set and a second target evaluation data set, according to a specified abnormal data set and the security evaluation topic label X, whether the security evaluation topic to be mined belongs to a floating security evaluation topic or a recorded security evaluation topic in the first target evaluation data set and whether the security evaluation topic to be mined belongs to a floating security evaluation topic or a recorded security evaluation topic in the second target evaluation data set are analyzed, so that the security evaluation topic label of the security evaluation topic to be mined in the first target evaluation data set and the security evaluation topic label in the second target evaluation data set are finally obtained respectively.
In the embodiment of the application, a security information set to be mined is obtained by carrying out data classification processing and global feature analysis on a first current data security event acquired in advance, a second current data security event with a correlation condition with a first data security topic in the security information set to be mined is determined from the current data security event acquired in real time, and according to a security assessment topic corresponding to the first data security topic and an acquired global feature vector corresponding to each security assessment topic, the security assessment topic corresponding to the first data security topic is determined, the evaluation score of each security assessment topic to be mined is in accordance with a specified target value, and the security assessment topic to be mined can be determined according to the acquired global feature vector corresponding to each security assessment topic in a period from history to current, so that the accuracy of the determined security assessment topic to be mined is improved, and the reliability of the security assessment topic to be mined is facilitated; and by determining the target evaluation data set corresponding to each safety evaluation topic to be mined and determining the safety evaluation topic label of each safety evaluation topic to be mined in the corresponding target evaluation data set according to the appointed abnormal data set, the safety evaluation topic label of each safety evaluation topic to be mined can be accurately classified in each corresponding target evaluation data set, so that the accuracy and the reliability of the safety evaluation topic label are ensured.
In one possible implementation embodiment, the security information set to be mined may be obtained by processing the first current data co-defense event acquired in advance by itself to obtain the security information set to be mined. The above S101 can be realized by S1011-S1015.
S1011, carrying out convolution processing on the collected first current data security event to obtain a data security vector; the number of the first current data collaborative prevention events is a plurality.
S1012, performing data classification processing on the data security vectors to obtain a plurality of data security topics.
For example, convolution processing may be performed on each first current data security event to obtain a data security vector corresponding to each first current data security 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 may be obtained.
S1013, determining security evaluation topic labels corresponding to the data security topics.
For example, a security evaluation topic label corresponding to each data security topic can be determined through an image association method.
In one possible implementation embodiment, for each data security topic, important security data in the data security topic can be screened, the important security data is compared with each appointed sample security data in an appointed abnormal data set to obtain a comparison result, and on the premise that the comparison result indicates that the appointed sample security data associated with the important security data exists, an appointed element label corresponding to the appointed sample security data is determined to be a security evaluation topic label corresponding to the data security topic; 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, the tag of the data security topic is determined to be the security assessment topic tag corresponding to the data security topic. The tags of the data security topics may be tags specified for the data security topics according to a specified numbering scheme and distinguished from the specified element tags.
For each data security topic, a piece of current data security co-protection event can be screened from the data security topic to determine important security data, wherein the important security data can be the current data security co-protection event with a relatively large face range.
By way of example, the comparison between an important piece of security data and a specified piece of sample security data can be achieved by calculating vector sharing variables. The data security vector of the important security data and the data security vector of the appointed sample security data can be respectively extracted, a vector sharing variable between the extracted data security vectors is calculated, and the obtained vector sharing variable is compared with an appointed target value or an appointed target value range, so that the magnitude relation between the vector sharing variable and the appointed target value is obtained, or the belonging relation between the vector sharing variable and the appointed 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 and the specified sample security data can be determined to be associated; 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 irrelevant.
In the embodiment of the application, the important security data of each data security topic is compared with the appointed sample security data, so that the workload in the process of determining the security evaluation topic label corresponding to each data security topic can be reduced, and the processing efficiency is improved.
S1014, based on the collection period and the collection range of the first current data security incident belonging to each data security topic, collecting global feature vectors corresponding to each security assessment topic are counted.
For each data security topic, the statistics may be performed according to the collection period and the collection range corresponding to each first current data security co-protection event belonging to the data security topic, where the security assessment topic corresponding to the data security topic corresponds to some collection ranges in some collection periods, so as to obtain the collection global feature vector of the security assessment topic.
In one possible implementation embodiment, the collected global feature vector corresponding to each security assessment topic may be a collection period and a collection range corresponding to each unit period of the security assessment topic. For example, according to the collection period and collection range corresponding to each first current data security co-protection event belonging to the data security topic, statistics may be made as to whether the security assessment topic corresponding to the data security topic in each unit period corresponds to the collection period and collection range, and some collection periods and some different collection ranges.
S1015, determining a plurality of data security topics, security assessment topic labels corresponding to the data security topics, and acquisition global feature vectors corresponding to the security assessment topic labels as a security information set to be mined.
In a possible implementation embodiment, after the step S101, the method further includes S201 to S205, where S201 to S205 may be performed synchronously with S102 to S104, or may be performed after any step in S102 to S104.
S201, determining a third current data co-defense event which is not related to a plurality of data security topics in the security information set to be mined from current data co-defense events acquired in real time.
The method includes the steps that in the current data cooperative protection events collected in the current period, it is determined that some current data cooperative protection events are not associated with all data security topics covered in the to-be-mined security information set, and in the current data cooperative protection events collected in the current period, current data cooperative protection events which are not associated with all data security topics covered in the to-be-mined security information set are determined to be third current data cooperative protection events.
S202, carrying out data classification processing on the third current data security event to obtain a second data security theme.
For example, convolution processing may be performed on each third current data security 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 topic.
S203, determining newly-added security evaluation topic labels corresponding to the second data security subjects.
The security evaluation topic labels corresponding to the second data security subjects are added with security evaluation topic labels. The same method as S1013 above may be used to determine the newly added security assessment topic tag corresponding to each second data security topic.
S204, based on the collection period and collection range of the third current data security incident belonging to each second data security theme, collecting global feature vectors corresponding to each newly-added security evaluation question are counted.
Similarly, the collected global feature vectors corresponding to the newly added security assessment questions may be counted by the same method as in S1014.
S205, adding a security information set to be mined to the second data security topic, the newly added security evaluation topic labels and the collected global feature vectors corresponding to the newly added security evaluation topic labels to obtain an optimized security information set to be mined.
By means of the method, the security information set to be mined is optimized, the optimized security information set to be mined can cover enough data security topics and security assessment topics, accuracy in determining a second current data security event in the current data security events acquired in real time next is improved, and accordingly reliability of the security assessment topics is guaranteed.
In one possible implementation, the above S102 may be implemented through S1021-S1024.
S1021, determining important security data from all data security topics covered by the security information set to be mined.
S1022, comparing each important security data with each current data security event acquired in real time to obtain at least one group of related important security data and current data security event.
For example, a first data security vector of each important security data can be extracted, a second data security vector of each current data security event acquired 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, determining that the important security data is associated with the current data cooperative security event, so that at least one group of related important security data and current data cooperative security event is obtained.
S1023, determining a data security topic 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 topic.
S1024, determining at least one group of correlated important security data and the current data security event in the current data security events as a second current data security event.
In one possible implementation embodiment, the collecting global feature vectors corresponding to each security assessment topic includes: the collection period and the collection range corresponding to each security assessment topic; based on this, the above S103 can be realized by S1031 to S1033.
S1031, determining evaluation scores of the security evaluation topics corresponding to the first data security topics based on the security evaluation topics corresponding to the first data security topics and the acquisition period corresponding to each security evaluation topic.
According to the embodiment of the application, the evaluation times of the security evaluation topics corresponding to the first data security topics in the specified period can be determined according to the security evaluation topics corresponding to the first data security topics and the acquisition period 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 topics to be mined, the evaluation scores of which accord with the specified target values, based on the evaluation scores.
S1033, determining target evaluation data sets corresponding to the safety evaluation topics to be mined based on the acquisition ranges corresponding to the safety evaluation topics.
The method includes the steps that according to the collection ranges corresponding to the security assessment topics of all first data security topics, the collection ranges corresponding to the security assessment topics to be mined in a specified period are determined, and after repeated ranges are removed from the determined collection ranges, the remaining collection ranges are determined to be one or a plurality of different target assessment data sets corresponding to the security assessment topics to be mined.
In one possible implementation embodiment, S104 may be implemented through S1041 and S1042, or may be implemented through S1041 or S1042.
S1041, on the premise that each security assessment topic to be mined is determined to have a corresponding specified key element in the specified abnormal data set, determining a security assessment topic label of each security assessment topic to be mined in the corresponding target assessment data set according to the corresponding specified key element.
S1042, on the premise that each security assessment topic to be mined is determined to have no corresponding specified key element in the specified abnormal data set, determining that each security assessment topic to be mined belongs to a floating security assessment topic in the corresponding target assessment data set.
In one possible implementation, specifying the abnormal data set includes: the specified key elements and the specified element labels 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 in each to-be-mined security assessment topic can be achieved by the following ways: associating the security evaluation topic labels corresponding to the security evaluation topics to be mined with the appointed element labels in the appointed abnormal data set to obtain association results; on the premise that the association result indicates that the appointed key element label associated with the security evaluation topic label corresponding to each security evaluation topic to be mined exists, determining that each security evaluation topic to be mined exists with the corresponding appointed key element.
For example, for a security assessment topic to be mined, on the premise that the association result indicates that a specified element tag associated with a security assessment topic tag corresponding to the security assessment topic to be mined exists, a specified key element corresponding to the specified element tag can be determined as a specified key element corresponding to the security assessment topic to be mined.
In one possible embodiment, the determining that the corresponding specified key element does not exist in the specified abnormal data set in each to-be-mined security assessment topic in S1042 may be implemented by the following manner: on the premise that the association result indicates that the appointed key element label associated with the security assessment topic label corresponding to each security assessment topic to be mined does not exist, the fact that the corresponding appointed key element does not exist in each security assessment topic to be mined is determined.
In one possible implementation embodiment, the determining, in S1041, the security assessment topic label of each security assessment topic to be mined in the corresponding target assessment data set according to the corresponding specified key element may be implemented through S301-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 question to be mined to obtain an association result.
S302, on the premise that the association result shows that a target evaluation data set associated with the range of the specified key element exists, determining that the security evaluation topic label of each security evaluation topic to be mined in the target evaluation data set is a recorded security evaluation topic, and determining that the security evaluation topic label of each security evaluation topic to be mined in the corresponding target evaluation data set which is not associated with the range of the specified key element is a floating security evaluation topic.
S303, on the premise that the association result indicates that the target evaluation data sets corresponding to the security evaluation topics to be mined are not associated with the range to which the specified key element belongs, determining that the security evaluation topic labels of the security evaluation topics to be mined in the corresponding target evaluation data sets are floating security evaluation topics.
In one possible implementation, for a range to which a specified key element belongs and a target evaluation data set, on the premise that the target evaluation data set belongs to the range to which the specified key element belongs, or the range to which the specified key element belongs is the same range as the target evaluation data set, it may be determined that the range to which the specified key element belongs is associated with the target evaluation data set.
In one possible implementation embodiment, the method further includes S401-S402; S401-S402 can be synchronously executed with S103-S104, and can also be executed after S103 or S104, and the embodiment of the application is not limited to the above; wherein, S401-S402 are as follows:
s401, determining non-analytic safety evaluation topics and target evaluation data sets thereof, wherein the evaluation scores of the non-analytic safety evaluation topics do not accord with the designated target values, based on the safety evaluation topics corresponding to the first data safety protection topics and the collected global feature vectors corresponding to the safety evaluation topics.
For example, on the premise that the evaluation score of the security evaluation topic corresponding to each first data security topic is determined by the method of S1041, the security evaluation topic whose evaluation score does not meet the specified target value is determined to be 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 the method of S1043.
S402, determining that each non-analytic security assessment topic is a current security assessment topic in each corresponding target assessment data set.
On the basis of the foregoing, please refer to fig. 2 in combination, there is provided a data security transmission device 200 based on a data center, which is applied to a data security transmission system based on the data center, the device includes:
the information acquisition module 210 is configured to obtain 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 co-defense event acquired in advance;
the data evaluation module 220 is configured to determine, from current data co-defense events collected in real time, a second current data co-defense event having a correlation with a first data security topic in the security information set to be mined; combining the security evaluation topics corresponding to the first data security subjects and the collected global feature vectors corresponding to the security evaluation topics to determine each security evaluation topic to be mined and the target evaluation data set of each security evaluation topic to be mined, wherein the evaluation score of each security evaluation topic to be mined accords with a specified target value;
The tag determining module 230 is configured to determine, according to the specified abnormal data set, a security evaluation topic tag of the to-be-mined security evaluation topic in the corresponding target evaluation data set.
On the basis of the above, referring to fig. 3 in combination, there is shown a data security transmission system 300 based on a data center station, comprising a processor 310 and a memory 320 in communication with each other, the processor 310 being configured to read and execute a computer program from the memory 320 to implement the method described above.
On the basis of the above, there is also provided a computer readable storage medium on which a computer program stored which, when run, implements the above method.
In summary, based on the above scheme, by obtaining the security information set to be mined obtained by performing data classification processing and global feature analysis on the first current data security event acquired in advance, determining a second current data security event with a correlation condition with the first data security topic in the security information set to be mined from the current data security event acquired in real time, and determining the security evaluation topics corresponding to the first data security topic and the acquired global feature vector corresponding to each security evaluation topic, wherein the evaluation score accords with each security evaluation topic to be mined of the specified target value, so that the security evaluation topic to be mined can be determined according to the acquired global feature vector corresponding to each security evaluation topic in the period from history to current, the accuracy of the determined security evaluation topic to be mined is improved, and the reliability of the security evaluation topic to be mined is facilitated; and by determining the target evaluation data set corresponding to each safety evaluation topic to be mined and determining the safety evaluation topic label of each safety evaluation topic to be mined in the corresponding target evaluation data set according to the appointed abnormal data set, the safety evaluation topic label of each safety evaluation topic to be mined can be accurately classified in each corresponding target evaluation data set, so that the accuracy and the reliability of the safety evaluation topic label are ensured.
It should be appreciated that the systems and modules thereof 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 then be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design 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 as provided on a carrier medium such as a magnetic disk, 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 of the present application and its modules may be implemented not only with hardware circuitry such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also with software executed by various types of processors, for example, and with a combination of the above hardware circuitry and software (e.g., firmware).
It should be noted that, the advantages that may be generated by different embodiments may be different, and in different embodiments, the advantages that may be generated may be any one or a combination of several of the above, or any other possible advantages that may be obtained.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements and adaptations of the application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within the present disclosure, and therefore, such modifications, improvements, and adaptations are intended to be within the spirit and scope of the exemplary embodiments of the present disclosure.
Meanwhile, the present application uses specific words to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the application may be combined as suitable.
Furthermore, those skilled in the art will appreciate that the various aspects of the application are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
The computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. 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 through any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.
The computer program code necessary for operation of 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, etc., a conventional programming language such as C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, ruby and Groovy, or other programming languages, etc. The program code may execute entirely on the user's computer or 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 form of network, 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 the use of services such as software as a service (SaaS) in a cloud computing environment.
Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the application is not intended to limit the sequence of the processes and methods unless specifically recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of example, it is to be understood that such details are merely illustrative 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 included within the spirit and scope of the embodiments of the application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in order to simplify the description of the present disclosure and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are required by the subject application. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the numbers allow for adaptive variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations in some embodiments for use in determining the breadth of the range, in particular embodiments, the numerical values set forth herein are as precisely as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited herein is hereby incorporated by reference in its entirety. Except for the application history file that is inconsistent or conflicting with this disclosure, the file (currently or later attached to this disclosure) that limits the broadest scope of the claims of this disclosure is also excluded. It is noted that the description, definition, and/or use of the term in the appended claims controls the description, definition, and/or use of the term in this application if there is a discrepancy or conflict between the description, definition, and/or use of the term in the appended claims.
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 application. Thus, by way of example, and not limitation, alternative configurations of embodiments of the application may be considered in keeping with the teachings of the application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly described and depicted herein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (9)
1. A data security transmission method based on a data center, the method at least comprising:
the method comprises the steps of obtaining 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 co-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; combining the security evaluation topics corresponding to the first data security subjects and the collected global feature vectors corresponding to the security evaluation topics to determine each security evaluation topic to be mined and the target evaluation data set of each security evaluation topic to be mined, wherein the evaluation score of each security evaluation topic to be mined accords with a specified target value;
determining the security evaluation topic labels of the security evaluation topics to be mined in the corresponding target evaluation data sets according to the appointed abnormal data sets;
the determining, according to the specified abnormal data set, a security evaluation topic label of each security evaluation topic to be mined in the corresponding target evaluation data set includes:
On the premise that the corresponding specified key elements exist in the specified abnormal data set in the each to-be-mined security evaluation topic, determining the security evaluation topic label of the each to-be-mined security evaluation topic in the corresponding target evaluation data set according to the corresponding specified key elements;
and/or on the premise that each security assessment topic to be mined is determined to have no corresponding specified key element in the specified abnormal data set, determining that each security assessment topic to be mined belongs to a floating security assessment topic in the corresponding target assessment data set;
wherein the specified abnormal data set includes: designating key elements and designating element labels corresponding to the designated key elements;
the determining that the corresponding specified key elements exist in the specified abnormal data set for each to-be-mined security assessment topic includes: associating the security evaluation topic labels corresponding to the security evaluation topics to be mined with the appointed element labels in the appointed abnormal data set to obtain association results;
on the premise that the association result indicates that a specified element label associated with a security evaluation topic label corresponding to each security evaluation topic to be mined exists, determining that a corresponding specified key element exists in each security evaluation topic to be mined;
The determining that the corresponding specified key elements do not exist in the specified abnormal data set for each to-be-mined security assessment topic includes: on the premise that the association result indicates that the appointed key element label associated with the security evaluation topic label corresponding to each security evaluation topic to be mined does not exist, determining that the corresponding appointed key element does not exist in each security evaluation topic to be mined;
the determining, according to the corresponding specified key element, a security evaluation topic label of the to-be-mined security evaluation topic in the corresponding target evaluation data set includes:
correlating the range of the corresponding appointed key element with each target evaluation data set corresponding to each security evaluation question to be mined to obtain a correlation result;
on the premise that the association result indicates that a target evaluation data set associated with the range of the specified key element exists, determining that the security evaluation topic labels 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 labels of the security evaluation topics to be mined in the corresponding target evaluation data set not associated with the range of the specified key element are floating security evaluation topics;
And/or on the premise that the association result indicates that the target evaluation data sets corresponding to the security evaluation topics to be mined are not associated with the range to which the specified key element belongs, determining that the security evaluation topic labels of the security evaluation topics to be mined in the corresponding target evaluation data sets are all floating security evaluation topics;
wherein the method further comprises: combining the security evaluation topics corresponding to the first data security subjects and the collected global feature vectors corresponding to the security evaluation topics to determine non-analytic security evaluation topics and target evaluation data sets thereof, wherein the evaluation scores of the non-analytic security evaluation topics do not accord with the designated target values; and determining that each non-analytic security assessment topic is a current security assessment topic in each corresponding target assessment data set.
2. The method of claim 1, wherein the obtaining the set of security information to be mined comprises:
carrying out convolution processing on the acquired first current data co-defense event to obtain a data security vector; the number of the first current data collaborative prevention events is several;
the data security vectors are subjected to data classification processing to obtain a plurality of data security topics;
Determining a security evaluation topic label corresponding to each data security topic;
based on the acquisition period and the acquisition range of the first current data co-defense event belonging to each data security topic, counting the acquisition global feature vectors corresponding to each security assessment topic;
and determining the plurality of data security topics, security assessment topic labels corresponding to the data security topics and acquisition 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 determining a security assessment topic tag corresponding to each data security topic comprises:
screening important security data in the security topics of the data;
comparing the important security data with security data of each appointed sample in the appointed abnormal data set to obtain a comparison result;
on the premise that the comparison result shows that the appointed sample security data related to the important security data exists, determining an appointed element label corresponding to the appointed sample security data as a security evaluation topic label corresponding to each data security topic;
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 evaluation topic label corresponding to each data security topic.
4. A method according to claim 3, characterized in that the method further comprises:
determining a third current data co-defense event which is not related to a plurality of data security topics in the security information set to be mined from the current data co-defense events acquired in real time;
performing data classification processing on the third current data security events to obtain a second data security theme;
determining newly added security assessment topic labels corresponding to the second data security topics;
based on the collection period and collection range of the third current data cooperative prevention event belonging to each second data security topic, counting the collection global feature vector corresponding to each newly-added security assessment topic;
and adding the security information set to be mined to the second data security topic, the newly added security evaluation topic labels and the collected global feature vectors corresponding to the newly added security evaluation topic labels to obtain an optimized security information set to be mined.
5. The method according to claim 4, wherein determining a second current data security event associated with a first data security topic in the set of security information to be mined from the current data security events collected in real time includes:
determining important security data from each data security topic covered by the security information set to be mined;
comparing each important security data with each current data co-protection event acquired in real time to obtain at least one group of inter-related important security data and current data co-protection event;
determining a data security topic to which the important security data belongs in the at least one group of related important security data and the current data co-security event as the first data security topic;
and determining the current data co-defense event in the at least one group of correlated important security data and the current data co-defense event as the second current data co-defense event.
6. The method of claim 5, wherein the obtaining at least one set of interrelated critical security data and current data security events by comparing each critical security data with each current data security 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 security event acquired in real time;
determining a vector sharing variable between the first data security vector and the second data security vector;
on the premise that the vector sharing variable accords with a sharing variable target value, determining that each important security data is associated with each current data security event, and accordingly obtaining at least one group of important security data and current data security events which are associated with each other.
7. The method of claim 6, wherein the collecting global feature vectors corresponding to the respective security assessment topics comprises: the collection period and the collection range corresponding to each security assessment topic; the determining, by combining the security evaluation topics corresponding to the first data security subject and the collected global feature vectors corresponding to the security evaluation topics, each security evaluation topic to be mined and the target evaluation data set thereof, where the evaluation score meets the specified target value, includes: combining the security evaluation topics corresponding to the first data security topics and the acquisition periods corresponding to the security evaluation topics to determine evaluation scores of the security evaluation topics corresponding to the first data security topics; combining the evaluation scores, and determining a security evaluation topic to be mined, wherein the evaluation scores accord with a specified target value; and determining target evaluation data sets corresponding to the safety evaluation topics to be mined according to the acquisition ranges corresponding to the safety evaluation topics.
8. The method of claim 7, wherein the determining the evaluation score of the security assessment topic corresponding to each first data security topic in combination with the security assessment topic corresponding to the first data security topic and the collection period corresponding to each security assessment topic comprises: according to the security evaluation topics corresponding to the first data security topics and the acquisition periods corresponding to the security evaluation topics, determining the evaluation times of the security evaluation topics corresponding to the first data security topics in a designated period; and determining the evaluation times as evaluation scores of the security evaluation topics.
9. A data security transmission system based on a data center, comprising a processor and a memory in communication with each other, the processor being adapted to read a computer program from the memory and execute it to implement the method of any one of claims 1-8.
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