CN113204746B - Identity recognition method and device, storage medium and electronic equipment - Google Patents
Identity recognition method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The specification discloses an identity recognition method, an identity recognition device, a storage medium and electronic equipment, wherein when a user starts a specified client, each operation of the user in the specified client is determined, the operation corresponding to each operation of the user is an operation behavior sequence, the corresponding behavior characteristics are obtained through a conversion model, and whether the user is a legal user is judged according to the behavior characteristics of the user and the standard behavior characteristics corresponding to the legal user. The method is not limited by electronic equipment, and does not need the user to perform extra operations such as storing biological characteristics and the like, so that the user can complete identity recognition without any awareness under the condition that the privacy of the user is not violated, the operation convenience is improved, and the identity recognition efficiency is further improved.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to an identity recognition method, an identity recognition apparatus, a storage medium, and an electronic device.
Background
With the development of computer technology, the use of electronic devices such as mobile phones becomes very common, and application software with various functions is produced accordingly. Many application software can acquire a large amount of personal information of a user when the user registers for use, and in order to guarantee the safety of the personal information of the user, when the client is used by other people, the illegal user who uses the client in time can be identified.
In the prior art, the mode for identity recognition is mainly as follows: and performing identification according to the biological characteristics of the user.
However, the above identification method has certain requirements on hardware devices, which not only requires high cost, but also requires additional operations by the user, resulting in low operational convenience and invasion of user privacy.
Disclosure of Invention
The present specification provides an identity recognition method and apparatus, which partially solve the above problems in the prior art.
The technical scheme adopted by the specification is as follows:
specifically, the present specification provides an identity recognition method, including:
determining an operation behavior sequence corresponding to each operation currently executed by a user to be identified as a behavior sequence to be identified;
inputting the behavior sequence to be recognized into a conversion model to obtain behavior characteristics to be recognized, corresponding to the behavior sequence to be recognized and output by the conversion model;
acquiring each standard behavior feature which is stored in advance, wherein the standard behavior feature is a behavior feature corresponding to a standard behavior sequence which is stored in advance;
determining a standard point corresponding to each standard behavior feature and a to-be-identified point corresponding to the to-be-identified behavior feature in a preset coordinate system;
and judging whether the point to be identified is an outlier or not according to each standard point and the point to be identified, and identifying the identity of the user to be identified according to a judgment result.
Optionally, determining an operation behavior sequence corresponding to each operation currently executed by the user to be identified specifically includes:
when a specified client is started, determining an operation behavior sequence corresponding to each operation executed by a user to be identified on the specified client.
Optionally, determining an operation behavior sequence corresponding to each operation currently executed by the user to be identified specifically includes:
determining each behavior data corresponding to each operation currently executed by a user to be identified;
for each behavior data, converting the behavior data into a corresponding attribute value according to a preset behavior conversion rule;
and sequencing the attribute values corresponding to the behavior data according to the sequence of the execution time of each operation currently executed by the user to be identified to obtain an operation behavior sequence.
Optionally, each standard behavior feature stored in advance specifically includes:
pre-acquiring various historical operation sets which are stored and executed by legal users in history;
determining an operation behavior sequence corresponding to each operation contained in each historical operation set as a standard behavior sequence aiming at each historical operation set;
and inputting the standard behavior sequence into the conversion model aiming at each standard behavior sequence to obtain the standard behavior characteristics corresponding to the standard behavior sequence output by the conversion model.
Optionally, obtaining each stored historical operation set executed by the valid user in history specifically includes:
determining each starting time of the appointed client in history;
for each starting time, determining a closing time which is after the starting time and corresponds to the designated client and has the shortest time interval with the starting time, and taking a time period from the starting time to the determined closing time as a use time period of the designated client;
and acquiring and saving a set formed by the operations executed by the legal user in the use time period as each historical operation set corresponding to the use time period.
Optionally, the identity recognition of the user to be recognized according to the determination result specifically includes:
if the point to be identified is an outlier, determining that the user to be identified is an illegal user;
and if the point to be identified is not the outlier, determining that the user to be identified is a legal user.
Optionally, determining whether the point to be identified is an outlier specifically includes:
determining a standard point in each standard point as a designated standard point;
according to the sequence of the distances from the specified standard points to the specified standard points from near to far, obtaining a specified number of standard points as a first group of standard points, and determining the average distance between the first group of standard points and the specified standard points as a standard average distance; acquiring the specified number of standard points as a second group of standard points according to the sequence of the distances from the points to be identified from near to far;
if the ratio of the average distance between the second set of standard points and the point to be identified to the standard average distance is greater than a specified first threshold, the point to be identified is an outlier;
and if the ratio of the average distance between the second set of standard points and the point to be identified to the standard average distance is within a specified first threshold, the point to be identified is not an outlier.
This specification provides an identification apparatus comprising:
the monitoring module is used for determining an operation behavior sequence corresponding to each operation currently executed by the user to be identified as the behavior sequence to be identified;
the conversion module is used for inputting the behavior sequence to be recognized into a conversion model to obtain the behavior characteristics to be recognized corresponding to the behavior sequence to be recognized output by the conversion model;
the standard module is used for acquiring each pre-stored standard behavior characteristic, and the standard behavior characteristic is a behavior characteristic corresponding to a pre-stored standard behavior sequence;
the mapping module is used for determining a standard point corresponding to each standard behavior characteristic and a point to be identified corresponding to the behavior characteristic to be identified in a preset coordinate system;
and the identification module is used for judging whether the point to be identified is an outlier or not according to each standard point and the point to be identified and identifying the identity of the user to be identified according to the judgment result.
The present specification provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described identification method.
The present specification provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the above-mentioned identification method when executing the program.
The technical scheme adopted by the specification can achieve the following beneficial effects:
in the method, when a user starts a specified client, each operation of the user in the specified client is monitored, the operation of the user is corresponding to an operation behavior sequence, corresponding behavior characteristics are obtained through a conversion model, and whether the user is a legal user is judged according to the behavior characteristics of the user and standard behavior characteristics corresponding to the legal user.
Compared with the prior art, the method does not need to use a biological feature recognition function, so the method is not limited by electronic equipment, does not need a user to execute additional operations like storing own biological features in the client, and performs identity recognition when the user normally uses the client. The client side stores the operation of normally using the client side by the user as the standard behavior characteristic for identity recognition later when the user uses the client side every time, so the method can not invade the privacy of the user, enables the user to finish identity recognition without awareness, improves the operation convenience and improves the identity recognition efficiency.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification and are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description serve to explain the specification and not to limit the specification in a non-limiting sense. In the drawings:
fig. 1 is a schematic flow chart of an identity recognition method in the present specification;
FIG. 2 is a schematic view of an identification device provided herein;
fig. 3 is a schematic diagram of an electronic device corresponding to fig. 1 provided in the present specification.
Detailed Description
To make the objects, technical solutions and advantages of the present specification clearer and more complete, the technical solutions of the present specification will be described in detail and completely with reference to the specific embodiments of the present specification and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.
The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of an identity recognition method in this specification, which specifically includes the following steps:
s100: and determining an operation behavior sequence corresponding to each operation currently executed by the user to be identified as the behavior sequence to be identified.
When a user first uses the client, the client may ask the user to register an account, fill out personal information, and save it. With the increase of the number of times that a user uses the client, personal information of an account holder (namely, a legal user) stored in the client is increased, and in order to ensure the security of the personal information, the user is subjected to identity recognition every time the user uses the client. The client may be installed in various electronic devices, the electronic devices may be devices such as a mobile phone and a tablet computer, and are not limited in this specification, and the client may be a takeaway application or other applications such as a payment application, and is not limited in this specification.
In this specification, since it is impossible to determine whether or not the user who uses the client this time is a legitimate user, the user is referred to as a user to be identified.
When the user to be identified starts the client, each operation currently executed by the user to be identified at the client can be determined through monitoring and the like, wherein for each operation, the operation comprises behavior data of the user to be identified, namely the time point when the user to be identified executes the operation and the behavior description corresponding to the operation. The behavior description is a description of an operation of the user to be recognized in the client, and may be a type of an operation performed by the user, such as a click operation, or may be a position of the operation, such as a coordinate of the click operation. And then, determining an operation behavior sequence corresponding to each operation according to the sequence of the execution time of each operation executed by the determined user to be identified, and taking the operation behavior sequence as the behavior sequence to be identified.
It is noted that each operation performed by a certain user to be identified is a sequence of actions.
S102: and inputting the behavior sequence to be recognized into a conversion model to obtain the behavior characteristics to be recognized corresponding to the behavior sequence to be recognized output by the conversion model.
And inputting the behavior sequence to be recognized into the conversion model to obtain the behavior characteristic to be recognized corresponding to the behavior sequence to be recognized. The conversion model has the function of enabling the behavior sequence to be recognized to have the characteristics of dense dimensionality and constant length, and the behavior sequence to be recognized with the characteristics is called as the behavior characteristic to be recognized.
Specifically, the conversion model can adopt a long-short term memory artificial neural network model, so that the behavior sequence to be recognized has the characteristics of dense dimensionality and constant length.
In addition, the behavior sequence to be identified has the characteristics of dense dimensionality and constant length, and the following method can be adopted:
firstly, the behavior sequence to be recognized is subjected to dimensionality reduction, and then the dimensionality-reduced behavior sequence to be recognized is normalized.
The purpose of enabling the behavior sequence to be recognized to have the characteristics is to facilitate the subsequent steps of the identity recognition method, and further improve the identity recognition efficiency.
S104: and acquiring each pre-stored standard behavior characteristic, wherein the standard behavior characteristic is a behavior characteristic corresponding to the pre-stored standard behavior sequence.
In this specification, the client uses the standard behavior feature of the legitimate user as the standard of the identity recognition, so that each standard behavior feature stored in advance is to be obtained, where the standard behavior feature is the behavior feature corresponding to the standard behavior sequence stored in advance, where the standard behavior feature is the corresponding output obtained after the standard behavior sequence is input into the conversion model, and the standard behavior sequence is each historical operation set executed historically by the legitimate user.
In daily life, a legal user can generate a standard behavior characteristic every time the client is used, and the standard behavior characteristic is used for identifying the identity of the user to be identified, so that the identity identification can be completed without invading the privacy of the user.
Specifically, in the embodiment of the present specification, the generation of each behavior standard feature stored in advance adopts the following method:
and acquiring various historical operation sets which are historically executed by the legal user, wherein the historical operation sets are sets which are formed by operations executed by the legal user in the use time period, starting from the start time in history by the client, determining the closing time which is after the start time and corresponds to the closing client with the shortest time interval of the start time for each start time, and taking the starting time to the determined closing time as the use time period of the client. For example, in afternoon of 1 month and 1 month in 2021, a legitimate user starts a client at three o 'clock, and after performing an operation of taking out at a point, the client is closed at three and a half hours, and a series of operations of taking out at a point performed by the legitimate user during the time period from the start time of the three o' clock client to the closing time of the three and a half minutes is referred to as a history operation set.
It should be noted that the monitored operations performed by the user to be identified are all operations from the start of the client to the time before the client is closed, because if the user to be identified closes the client, there is no meaning for the identification of the user to be identified. And when the standard behavior characteristics are saved in advance, all the monitored operations executed by the legal user are all the operations from the starting of the client to the closing of the client.
And acquiring each stored historical operation set, and determining an operation behavior sequence corresponding to each operation contained in each historical operation set as a standard behavior sequence aiming at each historical operation set. And inputting the standard behavior sequence into the conversion model aiming at each standard behavior sequence to obtain the standard behavior characteristics corresponding to the standard behavior sequence output by the conversion model.
It is also noted that the historical set of operations is in a one-to-one correspondence with the standard behavior characteristics. Each operation history set of a legal user from starting the appointed client to closing the appointed client corresponds to a standard behavior sequence, and a corresponding standard behavior characteristic can be determined for each standard behavior sequence through the processing of a conversion model.
Following the above example, the operation behavior sequence corresponding to the historical operation set of the legal user in the time period from the three o' clock client starting time to the three-point half-client closing time in 1 st afternoon in 2021 year is taken as the standard behavior sequence. And inputting the standard behavior sequence into a conversion model to obtain a standard behavior characteristic corresponding to a time period from three points to three and a half points of a legal user in 1 month and 1 afternoon in 2021.
Historically, each time a legitimate user starts to shut down a client once, a historical sequence of operations is generated and the client will convert it to its corresponding standard behavior signature and save it. Since a valid user uses the client more than once, the client stores standard behavior characteristics of many valid users. When the client acquires the pre-stored standard behavior characteristics, a plurality of standard behavior characteristics corresponding to different time periods can be acquired.
S106: and determining a standard point corresponding to each standard behavior characteristic and a to-be-identified point corresponding to the to-be-identified behavior characteristic in a preset coordinate system.
In this specification, by comparing the behavior feature to be identified with the standard behavior feature, it can be determined whether the user to be identified is an illegal user.
Specifically, whether the difference between the characteristic to be recognized and the standard behavior characteristic is larger than a specified second threshold value or not can be judged by calculating the similarity between the characteristic to be recognized and the standard behavior characteristic, and then whether the user to be recognized is an illegal user or not can be judged. If the difference between the features to be identified and the standard behavior features is larger than a specified second threshold value, the user to be identified is an illegal user; and if the difference between the characteristic to be identified and the standard behavior characteristic is within a specified second threshold value, determining that the user to be identified is a legal user.
In the embodiment of the present specification, the similarity calculation method may adopt cosine distance, specifically, any one standard behavior feature is selected, the behavior feature to be identified and the standard behavior feature are mapped to a high-dimensional space to obtain a corresponding vector to be identified and a corresponding standard vector, a cosine value between the vector to be identified and the selected standard vector is calculated, if the cosine value is closer to 1 and the included angle tends to 0, the similarity between the behavior feature to be identified and the standard behavior feature is high, and it may be determined that the user to be identified is a legal user; if the cosine value is close to 0 and the included angle tends to 90 degrees, the similarity between the behavior characteristic to be identified and the standard behavior characteristic is low, and the user to be identified can be determined to be an illegal user.
The similarity calculation method can also adopt a Euclidean distance, specifically, one standard behavior feature is arbitrarily selected from all standard behavior features, the Euclidean distance between the standard behavior feature and the behavior feature to be recognized is calculated, if the calculated Euclidean distance is smaller, the similarity between the behavior feature to be recognized and the standard behavior feature is larger, and the user to be recognized can be determined to be a legal user; if the calculated Euclidean distance is larger, the similarity between the behavior characteristics to be identified and the standard behavior characteristics is smaller, and the fact that the user to be identified is an illegal user can be determined.
Further, when the user to be identified is determined to be an illegal user, the client immediately limits each operation after the illegal user, each operation of the illegal user in the client is not executed, and the login of the current account is forcibly quitted to protect the safety of account information. If the user to be identified is determined to be a legal user, continuously monitoring each operation of the user until the client is closed, converting each operation from the client to the client which is monitored to be started to the client to be closed into a standard behavior characteristic according to the method, and storing the standard behavior characteristic in the client for the next identification.
In one or more embodiments provided in this specification, in addition to a method for calculating similarity between behavior features to be recognized and each standard behavior feature, a method for identifying an identity of a user to be recognized may also be used to obtain a feature point corresponding to the behavior feature by mapping the behavior features to be recognized and the standard behavior features to a preset coordinate system, and identify the user to be recognized by determining whether the feature point corresponding to the behavior feature to be recognized is an outlier.
Specifically, in a preset coordinate system, aiming at each standard behavior feature, mapping the standard behavior feature to a coordinate point in the coordinate system as a standard point corresponding to the standard behavior feature; and mapping the behavior characteristics to be identified to coordinate points in the coordinate system to serve as the points to be identified corresponding to the behavior characteristics to be identified. And judging whether the user to be identified is a legal user or not by judging whether the point to be identified is an outlier or not according to each standard point and the point to be identified.
S108: and judging whether the point to be identified is an outlier or not according to each standard point and the point to be identified, and identifying the identity of the user to be identified according to a judgment result.
For each standard point and point to be identified determined in step S106, it is only necessary to determine whether the point to be identified is an outlier, and according to the determination result, it is possible to determine whether the user to be identified is a valid user.
Specifically, the following method is adopted for judging whether the point to be identified is an outlier or not:
firstly, randomly determining a standard point as a designated standard point in each standard point;
then, according to the sequence of the distances from the specified standard points to the specified standard points from near to far, obtaining a specified number of standard points as a first group of standard points, and determining the average distance between the first group of standard points and the specified standard points as a standard average distance; and acquiring the specified number of standard points as a second group of standard points according to the sequence of the distances from the points to be identified from near to far.
If the ratio of the average distance between the second set of standard points and the point to be identified to the standard average distance is greater than a specified first threshold, the point to be identified is an outlier;
and if the ratio of the average distance between the second set of standard points and the point to be identified to the standard average distance is within a specified first threshold value, the point to be identified is not an outlier.
For example, one standard point is determined to be a designated standard point from among the standard points, 5 standard points are obtained according to the sequence of the distances from the designated standard point to the designated standard point from the near to the far, and the average distance from the 5 standard points to the designated standard point is calculated to be used as the standard average distance; then according to the sequence of the distances from the points to be identified from near to far, 5 standard points are also obtained, the average distance from the 5 standard points to the points to be identified is also calculated and is called as the average distance to be identified, and if the ratio of the average distance to be identified to the standard average distance is within a specified threshold value, the points to be identified are not outliers; if the ratio of the average distance to be identified to the standard average distance is greater than a specified threshold, then the point to be identified is an outlier.
It should be noted that, in the method for determining whether the point to be identified is an outlier, since 5 standard points are obtained in the order from near to far when the average distance from the specified standard point is calculated, and 5 standard points are obtained in the order from near to far when the average distance from the point to be identified is calculated, the number of the obtained standards is the same, and the method for obtaining 5 standard points is also the same, so that the fairness of comparison is ensured. In addition, in a plurality of standard points, the point to be identified only needs to be compared with any one standard point, and whether the point to be identified is an outlier can be judged without comparing the point to be identified with all the standard points one by one, so that the efficiency of identity identification is improved.
It should be added that, in step S100, each behavior data includes both the behavior description of the corresponding operation and the time point when the operation occurs, and for the electronic device, even if the time interval between the occurrence of the behavior data corresponding to the two operations is very short, the two sets of completely different data are also used, and it is very complicated to directly process the behavior data, which results in low identification efficiency. For convenience of subsequent processing, each behavior data may be converted into a corresponding numerical value as an attribute value corresponding to the behavior data.
Specifically, after the behavior data are acquired, the behavior data may be converted into corresponding attribute values according to a preset behavior conversion rule for each behavior data by using a coding method. And then, arranging the attribute values according to the sequence of the monitored execution time of each operation to obtain an operation behavior sequence as a behavior sequence to be identified, wherein the behavior sequence to be identified after being processed by the conversion rule is easier to be processed subsequently.
For example, the payment elapsed time >0.1s is coded as 1, the time duration for browsing the page >20s is coded as 2, and the change setting item is coded as M.
Based on the same idea, the identity recognition method provided for one or more embodiments of the present specification further provides a corresponding identity recognition apparatus, as shown in fig. 2.
Fig. 2 is a schematic diagram of an identity recognition apparatus provided in this specification, specifically including:
the system comprises a monitoring module 201, a conversion module 202, a standard module 203, a mapping module 204 and an identification module 205, wherein:
the monitoring module 201 is configured to determine an operation behavior sequence corresponding to each operation currently executed by a user to be identified, as the behavior sequence to be identified;
the conversion module 202 is configured to input the behavior sequence to be recognized into a conversion model, so as to obtain a behavior feature to be recognized, which is output by the conversion model and corresponds to the behavior sequence to be recognized;
the standard module 203 is configured to obtain each pre-stored standard behavior feature, where the standard behavior feature is a behavior feature corresponding to a pre-stored standard behavior sequence;
the mapping module 204 is configured to determine, in a preset coordinate system, a standard point corresponding to each standard behavior feature and a to-be-identified point corresponding to the to-be-identified behavior feature;
and the identifying module 205 is configured to determine whether the point to be identified is an outlier according to each standard point and the point to be identified, and perform identity identification on the user to be identified according to a determination result.
Optionally, the monitoring module 201 is specifically configured to, when a specified client is started, determine an operation behavior sequence corresponding to each operation executed by a user to be identified on the specified client.
Optionally, the monitoring module 201 is specifically configured to determine each behavior data corresponding to each operation currently executed by the user to be identified; for each behavior data, converting the behavior data into a corresponding attribute value according to a preset behavior conversion rule; and sequencing the attribute values corresponding to the behavior data according to the sequence of the execution time of each operation currently executed by the user to be identified to obtain an operation behavior sequence.
Optionally, the standard module 203 is specifically configured to obtain, in advance, each stored historical operation set that is executed by a valid user in history; determining an operation behavior sequence corresponding to each operation contained in each historical operation set as a standard behavior sequence aiming at each historical operation set; and inputting the standard behavior sequence into the conversion model aiming at each standard behavior sequence to obtain the standard behavior characteristics corresponding to the standard behavior sequence output by the conversion model.
Optionally, the standard module 203 is specifically configured to determine each starting time of the specified client in history; for each starting time, determining a closing time which is after the starting time and corresponds to the designated client and has the shortest time interval with the starting time, and taking a time period from the starting time to the determined closing time as a use time period of the designated client; and acquiring and saving a set formed by operations executed by the legal user in the use time period as each historical operation set corresponding to the use time period.
Optionally, the identifying module 205 is specifically configured to determine that the user to be identified is an illegal user if the point to be identified is an outlier; and if the point to be identified is not the outlier, determining that the user to be identified is a legal user.
Optionally, the identification module 205 is specifically configured to determine one standard point from among the standard points as a designated standard point; acquiring a specified number of standard points as a first group of standard points according to the sequence of the distances from the specified standard points to the specified standard points from near to far, and determining the average distance between the first group of standard points and the specified standard points as a standard average distance; acquiring the standard points of the specified number as a second group of standard points according to the sequence of the distances from the points to be identified from near to far; if the ratio of the average distance between the second set of standard points and the point to be identified to the standard average distance is greater than a specified first threshold, the point to be identified is an outlier; and if the ratio of the average distance between the second set of standard points and the point to be identified to the standard average distance is within a specified first threshold, the point to be identified is not an outlier.
The present specification also provides a computer-readable storage medium storing a computer program, which can be used to execute the identity recognition method provided in fig. 1 above.
This specification also provides a schematic block diagram of the electronic device shown in fig. 3. As shown in fig. 3, the drone includes, at the hardware level, a processor, an internal bus, a network interface, a memory, and a non-volatile memory, although it may also include hardware needed for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs the computer program to implement the identification method described in fig. 1. Of course, besides the software implementation, the present specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may be hardware or logic devices.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the various elements may be implemented in the same one or more pieces of software and/or hardware in the practice of this description.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the system embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.
The above description is only an example of the present disclosure, and is not intended to limit the present disclosure. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.
Claims (9)
1. An identity recognition method, comprising:
determining an operation behavior sequence corresponding to each operation currently executed by a user to be identified as a behavior sequence to be identified;
inputting the behavior sequence to be recognized into a conversion model to obtain the behavior characteristics to be recognized corresponding to the behavior sequence to be recognized output by the conversion model;
acquiring each standard behavior feature which is stored in advance, wherein the standard behavior feature is a behavior feature corresponding to a standard behavior sequence which is stored in advance;
determining a standard point corresponding to each standard behavior feature and a to-be-identified point corresponding to the to-be-identified behavior feature in a preset coordinate system;
determining a standard point in each standard point as a designated standard point, acquiring a designated number of standard points according to the sequence of the distance from the designated standard point to the designated standard point from near to far as a first group of standard points, and determining the average distance between the first group of standard points and the designated standard point as a standard average distance; acquiring the specified number of standard points as a second group of standard points according to the sequence of the distances from the points to be identified from near to far;
judging whether the point to be identified is an outlier, if the ratio of the average distance between the second group of standard points and the point to be identified to the standard average distance is greater than a specified first threshold value, the point to be identified is the outlier; if the ratio of the average distance between the second set of standard points and the point to be identified to the standard average distance is within a specified first threshold, the point to be identified is not an outlier;
and identifying the identity of the user to be identified according to the judgment result.
2. The method according to claim 1, wherein determining the operation behavior sequence corresponding to each operation currently executed by the user to be identified specifically comprises:
when a specified client is started, determining an operation behavior sequence corresponding to each operation executed by a user to be identified on the specified client.
3. The method according to claim 1, wherein determining the operation behavior sequence corresponding to each operation currently executed by the user to be identified specifically includes:
determining each behavior data corresponding to each operation currently executed by a user to be identified;
for each behavior data, converting the behavior data into corresponding attribute values according to the preset behavior conversion rule;
and sequencing the attribute values corresponding to the behavior data according to the sequence of the execution time of each operation currently executed by the user to be identified to obtain an operation behavior sequence.
4. The method of claim 1, wherein each of the pre-stored standard behavior characteristics specifically includes:
pre-acquiring various historical operation sets which are stored and executed by legal users in history;
determining an operation behavior sequence corresponding to each operation contained in each historical operation set as a standard behavior sequence aiming at each historical operation set;
and inputting the standard behavior sequence into the conversion model aiming at each standard behavior sequence to obtain the standard behavior characteristics corresponding to the standard behavior sequence output by the conversion model.
5. The method of claim 4, wherein obtaining each stored historical operation set historically executed by the legitimate user comprises:
determining each starting time of a specified client in history;
for each starting time, determining a closing time which is after the starting time and corresponds to the designated client and has the shortest time interval with the starting time, and taking a time period from the starting time to the determined closing time as a use time period of the designated client;
and acquiring and saving a set formed by operations executed by the legal user in the use time period as each historical operation set corresponding to the use time period.
6. The method according to claim 1, wherein the identifying the user to be identified according to the determination result specifically comprises:
if the point to be identified is an outlier, determining that the user to be identified is an illegal user;
and if the point to be identified is not the outlier, determining that the user to be identified is a legal user.
7. An identification device, comprising:
the monitoring module is used for determining an operation behavior sequence corresponding to each operation currently executed by the user to be identified as the behavior sequence to be identified;
the conversion module is used for inputting the behavior sequence to be recognized into a conversion model to obtain the behavior characteristics to be recognized corresponding to the behavior sequence to be recognized output by the conversion model;
the standard module is used for acquiring each standard behavior characteristic which is stored in advance, wherein the standard behavior characteristic is a behavior characteristic corresponding to a standard behavior sequence which is stored in advance;
the mapping module is used for determining a standard point corresponding to each standard behavior characteristic and a point to be identified corresponding to the behavior characteristic to be identified in a preset coordinate system;
the identification module is used for determining one standard point from the standard points as a designated standard point, acquiring a designated number of standard points according to the sequence of the distances from the designated standard point to the designated standard point from near to far, using the standard points as a first group of standard points, and determining the average distance between the first group of standard points and the designated standard point as a standard average distance; acquiring the specified number of standard points as a second group of standard points according to the sequence of the distances from the points to be identified from near to far; judging whether the point to be identified is an outlier, if the ratio of the average distance between the second group of standard points and the point to be identified to the standard average distance is greater than a specified first threshold value, the point to be identified is the outlier; if the ratio of the average distance between the second set of standard points and the point to be identified to the standard average distance is within a specified first threshold, the point to be identified is not an outlier; and identifying the identity of the user to be identified according to the judgment result.
8. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1 to 6.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 6 when executing the program.
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| US11218493B2 (en) * | 2019-05-31 | 2022-01-04 | Advanced New Technologies Co., Ltd. | Identity verification |
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