CN117411922A - Requester identification method, requester identification device, requester identification equipment and storage medium - Google Patents
Requester identification method, requester identification device, requester identification equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a storage medium for identifying a requesting party, belonging to the technical field of networks, wherein the method comprises the following steps: acquiring session data to be identified; extracting features of the conversation data to be identified to obtain conversation features to be identified; based on the session binding relation, determining a requester corresponding to the session data to be identified according to the request address in the session data to be identified and the session characteristics to be identified. According to the method and the device, based on the session binding relation and the session characteristics to be identified, the session data to be identified is automatically analyzed, so that a requester corresponding to the session data to be identified is identified, manual participation is not needed in the whole process, the API access monitoring cost is reduced, and the API access monitoring quality is improved.
Description
Technical Field
The present invention relates to the field of network technologies, and in particular, to a method, an apparatus, a device, and a storage medium for identifying a requester.
Background
In order to meet the requirements of API (application programming interface ) for safety governance, enterprises are urgently required to achieve fine management and control of user dimensions.
However, at present, enterprises mainly rely on historical experience rules, manually configured user name extraction rules and the like to realize fine management and control on user dimensions, so that the enterprise is high in cost and easy to make mistakes.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for identifying a requester, which are used for reducing the API access monitoring cost and improving the API access monitoring quality.
According to an aspect of the present invention, there is provided a requester identifying method including:
acquiring session data to be identified;
extracting features of the conversation data to be identified to obtain conversation features to be identified;
based on the session binding relation, determining a requester corresponding to the session data to be identified according to the request address in the session data to be identified and the session characteristics to be identified.
According to another aspect of the present invention, there is provided a requester identifying apparatus including:
the session data acquisition module to be identified is used for acquiring the session data to be identified;
the conversation characteristic determining module to be identified is used for extracting the characteristics of the conversation data to be identified to obtain the conversation characteristics to be identified;
the request party determining module is used for determining a request party corresponding to the session data to be identified according to the request address in the session data to be identified and the session characteristics to be identified based on the session binding relation.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the requestor identification method of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to perform the requestor identifying method of any one of the embodiments of the present invention.
According to the technical scheme, the session data to be identified are obtained; extracting features of the conversation data to be identified to obtain conversation features to be identified; based on the session binding relation, determining a requester corresponding to the session data to be identified according to the request address in the session data to be identified and the session characteristics to be identified. According to the technical scheme, based on the session binding relation and the session characteristics to be identified, the session data to be identified is automatically analyzed, so that a requester corresponding to the session data to be identified is identified, manual participation is not needed in the whole process, the API access monitoring cost is reduced, and the API access monitoring quality is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for identifying a requestor according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a method for identifying a requester according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a requester identifying device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a requester identifying method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It is noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of the present invention and in the foregoing figures, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
In addition, it should be noted that, in the technical scheme of the invention, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the session data to be identified and the session training data and the like all conform to the regulations of the related laws and regulations and do not violate the popular regulations of the public order.
Example 1
Fig. 1 is a flowchart of a method for identifying a requester, which is provided in an embodiment of the present invention, and the method may be implemented by a requester identifying device, which may be implemented in a form of hardware and/or software, and may be configured in an electronic device. As shown in fig. 1, the method includes:
s101, acquiring session data to be identified.
The session data to be identified refers to the flow of an API (application programming interface ) which is new in the service system and needs to be identified; wherein, the API traffic refers to web traffic based on http protocol. Optionally, the session data to be identified may include session request data to be identified and session response data to be identified; the session request data to be identified includes, but is not limited to, a requester identifier, a request mode, a request address and a request protocol. Wherein the request address may be the URL (Uniform Resource Locator ) of the request. Session response data to be identified includes, but is not limited to, response protocol, response status code, and response content. The response protocol is consistent with the request protocol and is an http protocol. The response status code is used to characterize the response status of the service system to the session request of the requester, for example, if the response status code is 404, it characterizes that the resource requested by the requester does not exist. The response content refers to the processing result of the service system on the session request of the requester.
Specifically, for each service system, session data to be identified of the service system is obtained from an API of the service system through a traffic mirroring technique.
S102, extracting features of the conversation data to be identified to obtain conversation features to be identified.
The session feature to be identified refers to a data feature of the session data to be identified.
Specifically, for each service system, feature extraction can be performed on session data to be identified of the service system based on a feature extraction model, so as to obtain session features to be identified. The feature extraction model may be preset according to actual service requirements, for example, the feature extraction model may be a feature extraction model based on a decision tree, which is not specifically limited in the embodiment of the present invention.
Optionally, for each service system, cluster analysis may be performed on the session data to be identified to obtain at least one behavioral session cluster; respectively determining behavior characteristics of at least one behavior session cluster; determining at least one session response data to be identified from the session data to be identified according to the behavior characteristics of the at least one behavior session cluster; and carrying out semantic analysis on at least one conversation response data to be identified to obtain conversation characteristics to be identified.
The behavior session cluster refers to a data set obtained by clustering analysis of session data to be identified. It should be noted that only one behavior feature can be extracted from one behavior session cluster.
Specifically, for each service system, clustering analysis can be performed on session data to be identified of the service system based on a clustering algorithm to obtain at least one behavior session cluster; the clustering algorithm may be preset according to actual service requirements, for example, the clustering algorithm may be a k-means clustering algorithm, for example, the clustering algorithm may be a hierarchical clustering algorithm, which is not limited in the embodiment of the present invention. Then, for each behavior session cluster, extracting the characteristics of the behavior session cluster to obtain the behavior characteristics of the behavior session cluster; matching the behavior characteristics with the data characteristics of the session response data to be identified in the session data to be identified, and taking the session response data to be identified, of which the data characteristics are consistent with the behavior characteristics, as the session response data to be identified corresponding to the behavior characteristics; and carrying out semantic analysis on the obtained at least one conversation response data to be identified, thereby obtaining the conversation characteristics to be identified.
It can be understood that the session data to be identified are subjected to cluster analysis, so that session data with similar behaviors are respectively concentrated in one behavior session cluster, the data characteristics of the same session behavior are enriched, the behavior characteristics of each behavior session cluster determined later are more accurate, and the determined session characteristics to be identified are more accurate.
S103, based on the session binding relation, determining a requester corresponding to the session data to be identified according to the request address in the session data to be identified and the session characteristics to be identified.
The session binding relationship refers to the binding relationship among the session characteristics of the service system, the login interface and the requester. It should be noted that, one service system corresponds to one session feature, one service system corresponds to one login interface, and one login interface corresponds to at least one requester.
Specifically, a request address and a session feature to be identified in the session data to be identified are used as indexes, a login interface with the login address identical to the request address is extracted from a session binding relationship, session features with the session feature identical to the session feature to be identified are extracted from the session binding relationship, and then a requester of the session data to be identified is further determined from the session binding relationship according to the login interface and the session features.
According to the technical scheme, the session data to be identified are obtained; extracting features of the conversation data to be identified to obtain conversation features to be identified; based on the session binding relation, determining a requester corresponding to the session data to be identified according to the request address in the session data to be identified and the session characteristics to be identified. According to the technical scheme, based on the session binding relation and the session characteristics to be identified, the session data to be identified is automatically analyzed, so that a requester corresponding to the session data to be identified is identified, manual participation is not needed in the whole process, the API access monitoring cost is reduced, and the API access monitoring quality is improved.
Example two
Fig. 2 is a flowchart of a method for identifying a requester according to a second embodiment of the present invention, where an alternative implementation manner of determining a session binding relationship is provided based on the foregoing embodiment. In the embodiments of the present invention, parts not described in detail may be referred to for related expressions of other embodiments. As shown in fig. 2, the method includes:
s201, classifying the session training data to obtain an application programming interface API session cluster of at least one service system.
The session training data refers to historical web traffic based on an http protocol; optionally, the session training data includes session request data and session response data; the session request data refers to data generated when the request direction service system sends a session request; optionally, the session request data includes, but is not limited to, a request mode, a request address, and a request protocol. Wherein the request address may be the URL (Uniform Resource Locator ) of the request. Correspondingly, the session response data refers to data fed back to the requester by the service system in response to the request of the requester; optionally, the session response data includes, but is not limited to, a response protocol, a response status code, and response content. The response protocol is consistent with the request protocol and is an http protocol. The response status code is used to characterize the response status of the service system to the session request of the requester, for example, if the response status code is 200, it indicates that the service system responded to the session request of the requester. The response content refers to the processing result of the service system on the session request of the requester. An API session cluster refers to a data set consisting of at least one piece of session data.
Specifically, the session training data may be classified according to at least one system domain name in the session training data, to obtain an API session cluster of at least one service system.
The system domain name refers to a domain name of a service system and is used for positioning the service system. It should be noted that, a system domain name corresponds to a service system.
More specifically, with the system domain name as an index, extracting a non-duplicate system domain name from the session training data; and classifying the session data in the session training data according to the extracted system domain name to obtain an Application Programming Interface (API) session cluster of at least one service system.
It can be appreciated that the session training data is divided into a plurality of session clusters according to the system domain name of the service system, so as to facilitate the subsequent clearer analysis of the session characteristics of each service system.
S202, according to the API session cluster of the service system, determining the session characteristics of the service system.
Wherein session features refer to features used to characterize a session; alternatively, the session feature may be one of Cookie, session and Token, etc.
Specifically, cluster analysis can be performed on an API session cluster of the service system to obtain at least one behavior API session cluster; respectively extracting features of at least one behavior API session cluster to obtain key behavior features of the at least one behavior API session cluster; according to the key behavior characteristics of at least one behavior API session cluster, carrying out data positioning on session training data, and determining at least one response data; and carrying out semantic analysis on at least one response data to obtain session characteristics of the service system.
The behavior API session cluster refers to a data set obtained by clustering analysis of the API session cluster of the service system. It should be noted that only one key behavior feature can be extracted from one behavior API session cluster.
More specifically, for each API session cluster of the service system, cluster analysis may be performed on the API session cluster of the service system based on a clustering algorithm to obtain at least one behavioural API session cluster; the clustering algorithm may be preset according to actual service requirements, for example, the clustering algorithm may be a k-means clustering algorithm, for example, the clustering algorithm may be a hierarchical clustering algorithm, which is not limited in the embodiment of the present invention. Then, for each behavior API session cluster, extracting the characteristics of the behavior API session cluster to obtain the key behavior characteristics of the behavior API session cluster; for the key behavior characteristics of each behavior API session cluster, matching the key behavior characteristics with the data characteristics of session response data in session training data, and taking the session response data with the data characteristics consistent with the key behavior characteristics as response data corresponding to the key behavior characteristics, thereby obtaining response data corresponding to each key behavior characteristic in the same way; and carrying out semantic analysis on the obtained at least one response data, thereby obtaining the session characteristics of the service system.
It can be understood that by performing cluster analysis on the API session cluster of each service system, session data with similar behaviors in each service system can be aggregated together, so that key behavior features can be extracted from the API session cluster of each service system more quickly, and further, the determined session features of each service system are more accurate.
S203, determining a login interface corresponding to the service system from a login interface library according to the request address in the API session cluster of the service system.
The login interface library is a database for storing login interface data; wherein the login interface data includes, but is not limited to, a login interface name, a login interface identification, and a login address.
Specifically, for each service system, the request address in the API session cluster of the service system is used as an index, and a login interface with the login address identical to the request address is extracted from a login interface library and used as a login interface corresponding to the service system.
S204, determining a requester for logging in the service system according to the historical login parameters of the login interface.
The history login parameters refer to login data of a history moment login interface; optionally, the historical login parameters include, but are not limited to, a requestor, a login password, and a login verification code.
Specifically, for the login interface corresponding to each service system, a requester for historical login to the service system is extracted from the historical login parameters of the login interface.
S205, constructing a session binding relation among the session characteristics, the login interface and the requester according to the session characteristics, the login interface and the requester of the service system.
Specifically, for each service system, a session feature of the service system, a session binding relationship between a login interface and a requester are established, that is, a one-to-one relationship between the session feature of the service system and the login interface is established, and a one-to-many relationship between the login interface of the service system and the requester is established. It should be noted that, one service system corresponds to one session feature, one service system corresponds to one login interface, and one login interface corresponds to at least one requester.
S206, obtaining the session data to be identified.
S207, extracting features of the conversation data to be identified to obtain conversation features to be identified.
S208, based on the session binding relation, determining a requester corresponding to the session data to be identified according to the request address in the session data to be identified and the session characteristics to be identified.
According to the technical scheme provided by the embodiment of the invention, the session data (namely the session training data) of the historical access service systems are analyzed, so that the session binding relation among the session characteristics, the login interfaces and the requesters in each service system is established, the subsequent automatic identification of the requesters accessing each service system based on the determined session binding relation is facilitated, manual participation is not needed in the whole process, the cost is greatly reduced, the problem of low quality of identifying the requesters based on manual extraction rules is solved, and the accuracy of identifying the requesters is improved.
Example III
Fig. 3 is a schematic structural diagram of a requester identifying device according to a third embodiment of the present invention, where the embodiment is applicable to a case of monitoring API access of an enterprise system, and the device may be implemented in a form of hardware and/or software and may be configured in an electronic device. As shown in fig. 3, the apparatus includes:
a to-be-identified session data obtaining module 301, configured to obtain to-be-identified session data;
the to-be-identified session feature determining module 302 is configured to perform feature extraction on to-be-identified session data to obtain to-be-identified session features;
the requester determining module 303 is configured to determine, based on the session binding relationship, a requester corresponding to the session data to be identified according to the request address in the session data to be identified and the session feature to be identified.
According to the technical scheme, the session data to be identified are obtained; extracting features of the conversation data to be identified to obtain conversation features to be identified; based on the session binding relation, determining a requester corresponding to the session data to be identified according to the request address in the session data to be identified and the session characteristics to be identified. According to the technical scheme, based on the session binding relation and the session characteristics to be identified, the session data to be identified is automatically analyzed, so that a requester corresponding to the session data to be identified is identified, manual participation is not needed in the whole process, the API access monitoring cost is reduced, and the API access monitoring quality is improved.
Optionally, the session feature determining module to be identified 302 is specifically configured to:
performing cluster analysis on the session data to be identified to obtain at least one behavior session cluster;
respectively determining behavior characteristics of at least one behavior session cluster;
determining at least one session response data to be identified from the session data to be identified according to the behavior characteristics of the at least one behavior session cluster;
and carrying out semantic analysis on at least one conversation response data to be identified to obtain conversation characteristics to be identified.
Optionally, the apparatus further comprises:
the API session cluster determining module is used for classifying the session training data to obtain an API session cluster of the application programming interface of at least one service system;
the session feature determining module is used for determining the session feature of the service system according to the API session cluster of the service system;
the login interface determining module is used for determining a login interface corresponding to the service system from a login interface library according to a request address in an API session cluster of the service system;
the logged-in requesting party determining module is used for determining the historical login parameters of the login interface and determining a requesting party of the historical login service system;
and the session binding relation determining module is used for constructing session binding relation among the session characteristics, the login interface and the requester according to the session characteristics, the login interface and the requester of the service system.
Optionally, the API session cluster determining module is specifically configured to:
and classifying the session training data according to at least one system domain name in the session training data to obtain an Application Programming Interface (API) session cluster of at least one service system.
Optionally, the session feature determining module is specifically configured to:
performing cluster analysis on the API session cluster of the service system to obtain at least one behavior API session cluster;
respectively extracting features of at least one behavior API session cluster to obtain key behavior features of the at least one behavior API session cluster;
according to the key behavior characteristics of at least one behavior API session cluster, carrying out data positioning on session training data, and determining at least one response data;
and carrying out semantic analysis on at least one response data to obtain session characteristics of the service system.
Optionally, the session training data refers to historical web traffic based on an http protocol.
The requester identifying device provided by the embodiment of the invention can execute the requester identifying method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the requester identifying methods.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM12 and the RAM13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the requester identification method.
In some embodiments, the requestor identification method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM12 and/or the communication unit 19. When the computer program is loaded into RAM13 and executed by processor 11, one or more of the steps of the requester identification method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the requestor identification method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of requestor identification, comprising:
acquiring session data to be identified;
extracting features of the session data to be identified to obtain session features to be identified;
based on the session binding relation, determining a requester corresponding to the session data to be identified according to the request address in the session data to be identified and the session characteristics to be identified.
2. The method according to claim 1, wherein the feature extraction of the session data to be identified to obtain session features to be identified includes:
performing cluster analysis on the session data to be identified to obtain at least one behavior session cluster;
determining behavior characteristics of the at least one behavior session cluster respectively;
determining at least one session response data to be identified from the session data to be identified according to the behavior characteristics of the at least one behavior session cluster;
and carrying out semantic analysis on the at least one conversation response data to be identified to obtain conversation characteristics to be identified.
3. The method according to claim 1, wherein the method further comprises:
classifying the session training data to obtain an API session cluster of at least one service system;
determining the session characteristics of the service system according to the API session cluster of the service system;
determining a login interface corresponding to the service system from a login interface library according to a request address in an API session cluster of the service system;
determining a requester for historic login of the service system according to the historic login parameters of the login interface;
and constructing a session binding relation among the session characteristics, the login interface and the requester according to the session characteristics, the login interface and the requester of the service system.
4. A method according to claim 3, wherein said classifying session training data to obtain at least one API session cluster for a service system comprises:
classifying the session training data according to at least one system domain name in the session training data to obtain an Application Programming Interface (API) session cluster of at least one service system.
5. A method according to claim 3, wherein said determining session features of said business system based on API session clusters of said business system comprises:
performing cluster analysis on the API session cluster of the service system to obtain at least one behavior API session cluster;
respectively extracting features of the at least one behavior API session cluster to obtain key behavior features of the at least one behavior API session cluster;
according to the key behavior characteristics of the at least one behavior API session cluster, carrying out data positioning on the session training data, and determining at least one response data;
and carrying out semantic analysis on the at least one response data to obtain session characteristics of the service system.
6. The method according to any of claims 3-5, wherein the session training data refers to historical web traffic based on an http protocol.
7. A requester identifying device, comprising:
the session data acquisition module to be identified is used for acquiring the session data to be identified;
the conversation characteristic to be identified determining module is used for extracting the characteristics of the conversation data to be identified to obtain conversation characteristics to be identified;
and the requester determining module is used for determining a requester corresponding to the session data to be identified according to the request address in the session data to be identified and the session characteristics to be identified based on the session binding relation.
8. The apparatus of claim 7, wherein the session feature determination module is specifically configured to:
performing cluster analysis on the session data to be identified to obtain at least one behavior session cluster;
determining behavior characteristics of the at least one behavior session cluster respectively;
determining at least one session response data to be identified from the session data to be identified according to the behavior characteristics of the at least one behavior session cluster;
and carrying out semantic analysis on the at least one conversation response data to be identified to obtain conversation characteristics to be identified.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the requestor identification method of any one of claims 1-6.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the requester identification method of any one of claims 1-6.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311604377.3A CN117411922A (en) | 2023-11-28 | 2023-11-28 | Requester identification method, requester identification device, requester identification equipment and storage medium |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311604377.3A CN117411922A (en) | 2023-11-28 | 2023-11-28 | Requester identification method, requester identification device, requester identification equipment and storage medium |
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| CN117411922A true CN117411922A (en) | 2024-01-16 |
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| CN202311604377.3A Pending CN117411922A (en) | 2023-11-28 | 2023-11-28 | Requester identification method, requester identification device, requester identification equipment and storage medium |
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| Country | Link |
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| CN (1) | CN117411922A (en) |
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