CN114598547A - Data analysis method applied to network attack recognition and electronic equipment - Google Patents
Data analysis method applied to network attack recognition and electronic equipment Download PDFInfo
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- CN114598547A CN114598547A CN202210292467.2A CN202210292467A CN114598547A CN 114598547 A CN114598547 A CN 114598547A CN 202210292467 A CN202210292467 A CN 202210292467A CN 114598547 A CN114598547 A CN 114598547A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1425—Traffic logging, e.g. anomaly detection
Abstract
According to the data analysis method and the electronic equipment applied to network attack identification, the risk trend vector expression is obtained through vector expression adjustment operation and attack characteristic mining operation, wind control coping deployment is convenient to carry out on the network service interaction log, and the risk trend vector expression is determined without consuming a large amount of computing resources, so that the risk trend vector expression which is as complete and accurate as possible can be determined on the basis of not increasing the complexity of a network attack identification mechanism, and thus the risk trend vector expression can carry out wind control coping deployment on the network service interaction log triggering network attack risk detection so as to obtain an accurate and credible wind control coping deployment list.
Description
Technical Field
The present application relates to the field of network attack recognition technologies, and in particular, to a data analysis method and an electronic device applied to network attack recognition.
Background
Network attack identification is receiving more and more attention at present, and how to obtain an accurate and credible wind control coping deployment list is a difficult problem in terms of network attack identification.
Disclosure of Invention
In order to solve the technical problems in the related art, the application provides a data analysis method and electronic equipment applied to network attack identification.
The application provides a data analysis method applied to network attack identification, which comprises the following steps:
determining a plurality of target network service interaction reports in a plurality of network service interaction reports of a network service interaction log for triggering network attack risk detection;
carrying out attack feature mining operation of a plurality of attack tendency layers on the plurality of target network service interaction reports to obtain first attack feature vector expressions of the plurality of attack tendency layers of the target network service interaction reports;
performing risk trend mining operation on a first attack feature vector expression of the target network service interaction report to obtain a risk trend vector expression of the target network service interaction report, wherein the risk trend mining operation comprises vector expression adjusting operation and attack feature mining operation, and the vector expression adjusting operation comprises queue adjusting on the first attack feature vector expression and a second attack feature vector expression of at least one attack feature mining operation;
and performing wind control corresponding deployment on the network service interaction log triggering the network attack risk detection based on the risk trend vector expression to obtain a wind control corresponding deployment list.
In some optional embodiments, performing risk trend mining on the first attack feature vector expression of the target network service interaction report to obtain a risk trend vector expression of the target network service interaction report includes:
performing a first vector expression adjustment operation on a first attack feature vector expression of the target network service interaction report to obtain a plurality of first attack feature vector expression sets, wherein a queue number of the first attack feature vector expression in the first attack feature vector expression sets is consistent with a queue number of the corresponding target network service interaction report;
carrying out attack feature mining operation on a first attack feature vector expression in the first attack feature vector expression set to obtain a second attack feature vector expression set, wherein the second attack feature vector expression set comprises a second attack feature vector expression corresponding to the first attack feature vector expression;
and performing second vector expression adjustment operation on the second attack feature vector expression set to obtain the risk trend vector expression.
In some optional embodiments, performing a first vector expression adjustment operation on a first attack feature vector expression reported by the target network service interaction to obtain a plurality of first attack feature vector expression sets, including:
for each target network service interaction report, decomposing a plurality of first attack characteristic vector expressions of the target network service interaction report to obtain a plurality of first attack characteristic vector expression lists of the target network service interaction report, wherein each first attack characteristic vector expression list has keywords, and the first attack characteristic vector expression lists with the same keywords of each target network service interaction report correspond to the same group of attack tendency levels in a plurality of attack tendency levels;
and arranging the first attack characteristic vector expression lists with consistent keywords in the first attack characteristic vector expression lists of the target network service interaction reports according to the queue numbers of the target network service interaction reports to obtain a first attack characteristic vector expression set corresponding to the keywords.
In some optional embodiments, performing a second vector expression adjustment operation on the second attack feature vector expression set to obtain the risk trend vector expression includes:
decomposing second attack feature vector expressions in the second attack feature vector expression set according to corresponding target network service interaction reports to obtain a second attack feature vector expression list, wherein the second attack feature vector expressions in the same second attack feature vector expression list correspond to the same target network service interaction report;
and arranging all the second attack characteristic vector expression lists to obtain risk trend vector expressions corresponding to the target network service interaction reports.
In some optional embodiments, performing risk trend mining operation on the first attack feature vector expression of the target network service interaction report to obtain a risk trend vector expression of the target network service interaction report, includes:
performing first attack characteristic mining operation on a first attack characteristic vector expression of the target network service interaction report to obtain a third attack characteristic vector expression corresponding to the first attack characteristic vector expression;
performing third vector expression adjustment operation on the third attack feature vector expressions to obtain a plurality of third attack feature vector expression sets, wherein the queue numbers of the third attack feature vector expressions in the third attack feature vector expression sets are consistent with the queue numbers of the corresponding target network service interaction reports;
performing second attack feature mining operation on a third attack feature vector expression in the third attack feature vector expression set to obtain a fourth attack feature vector expression set, wherein the fourth attack feature vector expression set comprises a fourth attack feature vector expression corresponding to the third attack feature vector expression;
performing fourth vector expression adjustment operation on the fourth attack feature vector expression set to obtain a third attack feature vector expression list corresponding to the target network service interaction report;
and performing third attack feature mining operation on a fourth attack feature vector expression in the third attack feature vector expression list to obtain a risk trend vector expression corresponding to the target network service interaction report.
In some optional embodiments, determining a plurality of target network service interaction reports from a plurality of network service interaction reports of a network service interaction log that triggers network attack risk detection includes:
sampling a plurality of network service interaction reports of a network service interaction log for triggering network attack risk detection to obtain a plurality of candidate network service interaction reports;
and processing the candidate network service interaction reports to obtain the target network service interaction reports.
In some optional embodiments, the data analysis method applied to network attack identification is implemented by a network attack identification mechanism, and the method further includes:
determining an example network service interaction report from a plurality of network service interaction reports of an example network service interaction log;
loading the example network service interaction report to the network attack recognition mechanism to obtain a deployment list corresponding to the prediction wind control of the example network service interaction log;
determining the mechanism loss of the network attack identification mechanism based on the priori knowledge of the predicted wind control corresponding deployment list and the example network service interaction log;
optimizing the network attack recognition mechanism based on the mechanism loss.
In some optional embodiments, said optimizing said network attack recognition mechanism based on said mechanism loss comprises: and optimizing the network attack recognition mechanism based on the mechanism loss to obtain the optimized network attack recognition mechanism.
The application also provides an electronic device, which comprises a memory, a processor and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor implements the method of any one of the above by reading the computer program from the memory and executing the computer program.
The present application also provides a readable storage medium having stored thereon a program which, when executed by a processor, performs the method described above.
For the embodiment of the application, the risk trend vector expression is obtained through vector expression adjustment operation and attack feature mining operation, wind control coping deployment is conveniently carried out on the network service interaction log, a large number of computing resources are not required to be consumed to determine the risk trend vector expression, so that the risk trend vector expression which is as complete and accurate as possible can be determined on the basis of not increasing the complexity of a network attack identification mechanism, and thus the risk trend vector expression can carry out wind control coping deployment on the network service interaction log which triggers network attack risk detection so as to obtain an accurate and credible wind control coping deployment list.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a flowchart of a data analysis method applied to network attack identification according to an embodiment of the present application.
Fig. 2 is a schematic hardware structure diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
Referring to fig. 1, an embodiment of the present application provides a flowchart of a data analysis method applied to network attack recognition, which is applied to an electronic device.
S10, determining a plurality of target network service interaction reports in a plurality of network service interaction reports of the network service interaction log triggering network attack risk detection; and carrying out attack characteristic mining operation of a plurality of attack tendency layers on the plurality of target network service interaction reports to obtain first attack characteristic vector expressions of the plurality of attack tendency layers of the target network service interaction reports.
For some preferred embodiments, the step of determining a plurality of target network service interaction reports from the plurality of network service interaction reports of the network service interaction log triggering the network attack risk detection, as described in S10, may include the following steps: sampling a plurality of network service interaction reports of a network service interaction log triggering network attack risk detection to obtain a plurality of candidate network service interaction reports; and processing the candidate network service interaction reports to obtain the multiple target network service interaction reports.
And S20, performing risk trend mining operation on the first attack feature vector expression of the target network service interaction report to obtain a risk trend vector expression of the target network service interaction report, wherein the risk trend mining operation comprises vector expression adjusting operation and attack feature mining operation, and the vector expression adjusting operation comprises queue adjusting on the first attack feature vector expression and the second attack feature vector expression of at least one attack feature mining operation.
Under some possible technical solutions, performing risk trend mining on the first attack feature vector expression of the target network service interaction report described in S20 to obtain a risk trend vector expression of the target network service interaction report, including: performing a first vector expression adjustment operation on a first attack feature vector expression of the target network service interaction report to obtain a plurality of first attack feature vector expression sets, wherein a queue number of the first attack feature vector expression in the first attack feature vector expression sets is consistent with a queue number of the corresponding target network service interaction report; carrying out attack feature mining operation on a first attack feature vector expression in the first attack feature vector expression set to obtain a second attack feature vector expression set, wherein the second attack feature vector expression set comprises a second attack feature vector expression corresponding to the first attack feature vector expression; and performing second vector expression adjustment operation on the second attack feature vector expression set to obtain the risk trend vector expression.
In some examples, performing a first vector expression adjustment operation on a first attack feature vector expression of the target network traffic interaction report to obtain a plurality of first attack feature vector expression sets includes: for each target network service interaction report, decomposing a plurality of first attack characteristic vector expressions of the target network service interaction report to obtain a plurality of first attack characteristic vector expression lists of the target network service interaction report, wherein each first attack characteristic vector expression list has keywords, and the first attack characteristic vector expression lists with the same keywords of each target network service interaction report correspond to the same group of attack tendency levels in a plurality of attack tendency levels; and arranging the first attack characteristic vector expression lists with consistent keywords in the first attack characteristic vector expression lists of the target network service interaction reports according to the queue numbers of the target network service interaction reports to obtain a first attack characteristic vector expression set corresponding to the keywords.
For some further embodiments, performing a second vector expression adjustment operation on the second set of attack feature vector expressions to obtain the risk trend vector expression comprises: decomposing second attack feature vector expressions in the second attack feature vector expression set according to corresponding target network service interaction reports to obtain a second attack feature vector expression list, wherein the second attack feature vector expressions in the same second attack feature vector expression list correspond to the same target network service interaction report; and arranging all the second attack characteristic vector expression lists to obtain risk trend vector expressions corresponding to the target network service interaction reports.
In some other embodiments, performing risk trend mining on the first attack feature vector expression of the target network service interaction report to obtain a risk trend vector expression of the target network service interaction report includes: performing first attack characteristic mining operation on a first attack characteristic vector expression of the target network service interaction report to obtain a third attack characteristic vector expression corresponding to the first attack characteristic vector expression; performing third vector expression adjustment operation on the third attack feature vector expressions to obtain a plurality of third attack feature vector expression sets, wherein the queue numbers of the third attack feature vector expressions in the third attack feature vector expression sets are consistent with the queue numbers of the corresponding target network service interaction reports; performing second attack feature mining operation on a third attack feature vector expression in the third attack feature vector expression set to obtain a fourth attack feature vector expression set, wherein the fourth attack feature vector expression set comprises a fourth attack feature vector expression corresponding to the third attack feature vector expression; performing fourth vector expression adjustment operation on the fourth attack feature vector expression set to obtain a third attack feature vector expression list corresponding to the target network service interaction report; and performing third attack feature mining operation on a fourth attack feature vector expression in the third attack feature vector expression list to obtain a risk trend vector expression corresponding to the target network service interaction report.
And S30, carrying out wind control corresponding deployment on the network service interaction log triggering the network attack risk detection based on the risk trend vector expression to obtain a wind control corresponding deployment list.
Under other independent embodiments, the data analysis method applied to network attack identification is implemented by a network attack identification mechanism, and the method further comprises the following steps: determining an example network service interaction report from a plurality of network service interaction reports of an example network service interaction log; loading the example network service interaction report to the network attack recognition mechanism to obtain a deployment list corresponding to the prediction wind control of the example network service interaction log; determining the mechanism loss of the network attack identification mechanism based on the priori knowledge of the predicted wind control corresponding deployment list and the example network service interaction log; optimizing the network attack recognition mechanism based on the mechanism loss.
Based on the above, the optimizing the network attack recognition mechanism based on the mechanism loss includes: and optimizing the network attack recognition mechanism based on the mechanism loss to obtain the optimized network attack recognition mechanism.
In summary, the method is applied to S10-S40, the risk trend vector expression is obtained through vector expression adjustment operation and attack feature mining operation, wind control coping deployment is convenient for the network service interaction log, and the risk trend vector expression is determined without consuming a large amount of computing resources, so that the risk trend vector expression which is as complete and accurate as possible can be determined on the basis of not increasing the complexity of a network attack identification mechanism, and thus the risk trend vector expression can perform wind control coping deployment on the network service interaction log which triggers network attack risk detection so as to obtain an accurate and credible wind control coping deployment list.
On the basis, please refer to fig. 2 in combination, the present application further provides a schematic diagram of a hardware structure of the electronic device 20, which specifically includes a memory 21, a processor 22, a network module 23, and an information processing apparatus applied to online office. The memory 21, the processor 22 and the network module 23 are electrically connected directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 21 stores an information processing apparatus for online office, the information processing apparatus for online office includes at least one software functional module which can be stored in the memory 21 in the form of software or firmware (firmware), and the processor 22 executes software programs and modules stored in the memory 21.
The Memory 21 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 21 is configured to store a program, and the processor 22 executes the program after receiving the execution instruction.
The processor 22 may be an integrated circuit chip having data processing capabilities. The Processor 22 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The network module 23 is used for establishing a communication connection between the electronic device 20 and other communication terminal devices through a network, so as to implement transceiving operations of network signals and data. The network signal may include a wireless signal or a wired signal.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
The present application may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present application.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the application is defined by the appended claims.
Claims (10)
1. A data analysis method applied to network attack recognition is characterized by comprising the following steps:
determining a plurality of target network service interaction reports in a plurality of network service interaction reports of a network service interaction log for triggering network attack risk detection;
carrying out attack characteristic mining operation of a plurality of attack tendency layers on the target network service interaction reports to obtain first attack characteristic vector expressions of the attack tendency layers of the target network service interaction reports;
performing risk trend mining operation on a first attack feature vector expression of the target network service interaction report to obtain a risk trend vector expression of the target network service interaction report, wherein the risk trend mining operation comprises vector expression adjusting operation and attack feature mining operation, and the vector expression adjusting operation comprises queue adjusting on the first attack feature vector expression and a second attack feature vector expression of at least one attack feature mining operation;
and performing wind control corresponding deployment on the network service interaction log triggering the network attack risk detection based on the risk trend vector expression to obtain a wind control corresponding deployment list.
2. The method of claim 1, wherein performing risk trend mining on the first attack feature vector expression of the target network service interaction report to obtain a risk trend vector expression of the target network service interaction report comprises:
performing a first vector expression adjustment operation on a first attack feature vector expression of the target network service interaction report to obtain a plurality of first attack feature vector expression sets, wherein a queue number of the first attack feature vector expression in the first attack feature vector expression sets is consistent with a queue number of the corresponding target network service interaction report;
carrying out attack feature mining operation on a first attack feature vector expression in the first attack feature vector expression set to obtain a second attack feature vector expression set, wherein the second attack feature vector expression set comprises a second attack feature vector expression corresponding to the first attack feature vector expression;
and performing second vector expression adjustment operation on the second attack feature vector expression set to obtain the risk trend vector expression.
3. The method of claim 2, wherein performing a first vector expression adjustment operation on a first attack feature vector expression of the target network traffic interaction report to obtain a plurality of first attack feature vector expression sets comprises:
for each target network service interaction report, decomposing a plurality of first attack characteristic vector expressions of the target network service interaction report to obtain a plurality of first attack characteristic vector expression lists of the target network service interaction report, wherein each first attack characteristic vector expression list has keywords, and the first attack characteristic vector expression lists with the same keywords of each target network service interaction report correspond to the same group of attack tendency levels in a plurality of attack tendency levels;
and arranging the first attack characteristic vector expression lists with consistent keywords in the first attack characteristic vector expression lists of the target network service interaction reports according to the queue numbers of the target network service interaction reports to obtain a first attack characteristic vector expression set corresponding to the keywords.
4. The method of claim 2, wherein performing a second vector expression adjustment operation on the second set of attack feature vector expressions to obtain the risk trend vector expression comprises:
decomposing second attack feature vector expressions in the second attack feature vector expression set according to corresponding target network service interaction reports to obtain a second attack feature vector expression list, wherein the second attack feature vector expressions in the same second attack feature vector expression list correspond to the same target network service interaction report;
and arranging all the second attack characteristic vector expression lists to obtain risk trend vector expressions corresponding to the target network service interaction reports.
5. The method of claim 1, wherein performing risk trend mining on the first attack feature vector expression of the target network service interaction report to obtain a risk trend vector expression of the target network service interaction report comprises:
performing first attack characteristic mining operation on a first attack characteristic vector expression of the target network service interaction report to obtain a third attack characteristic vector expression corresponding to the first attack characteristic vector expression;
performing third vector expression adjustment operation on the third attack feature vector expressions to obtain a plurality of third attack feature vector expression sets, wherein the queue numbers of the third attack feature vector expressions in the third attack feature vector expression sets are consistent with the queue numbers of the corresponding target network service interaction reports;
performing second attack feature mining operation on a third attack feature vector expression in the third attack feature vector expression set to obtain a fourth attack feature vector expression set, wherein the fourth attack feature vector expression set comprises a fourth attack feature vector expression corresponding to the third attack feature vector expression;
performing fourth vector expression adjustment operation on the fourth attack feature vector expression set to obtain a third attack feature vector expression list corresponding to the target network service interaction report;
and performing third attack feature mining operation on fourth attack feature vector expressions in the third attack feature vector expression list to obtain risk trend vector expressions corresponding to the target network service interaction report.
6. The method of claim 1, wherein determining a plurality of target network traffic interaction reports among a plurality of network traffic interaction reports of a network traffic interaction log that triggers cyber attack risk detection comprises:
sampling a plurality of network service interaction reports of a network service interaction log triggering network attack risk detection to obtain a plurality of candidate network service interaction reports;
and processing the candidate network service interaction reports to obtain the target network service interaction reports.
7. The method of claim 1, wherein the data analysis method applied to network attack recognition is implemented by a network attack recognition mechanism, and the method further comprises:
determining an example network service interaction report from a plurality of network service interaction reports of an example network service interaction log;
loading the example network service interaction report to the network attack recognition mechanism to obtain a deployment list corresponding to the prediction wind control of the example network service interaction log;
determining the mechanism loss of the network attack identification mechanism based on the priori knowledge of the predicted wind control corresponding deployment list and the example network service interaction log;
optimizing the network attack recognition mechanism based on the mechanism loss.
8. The method of claim 7, wherein optimizing the cyber attack recognition mechanism based on the mechanism loss comprises: and optimizing the network attack recognition mechanism based on the mechanism loss to obtain the optimized network attack recognition mechanism.
9. An electronic device comprising a memory, a processor, and a network module; wherein the memory, the processor, and the network module are electrically connected directly or indirectly; the processor implements the method of any one of claims 1-8 by reading the computer program from the memory and running it.
10. A readable storage medium, characterized in that a program is stored thereon, which program, when being executed by a processor, carries out the method of any one of claims 1-8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114866344A (en) * | 2022-07-05 | 2022-08-05 | 佛山市承林科技有限公司 | Information system data security protection method and system and cloud platform |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114866344A (en) * | 2022-07-05 | 2022-08-05 | 佛山市承林科技有限公司 | Information system data security protection method and system and cloud platform |
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