CN114386797A - Internet of things card management and control method, system and device and storage medium - Google Patents
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Abstract
The invention discloses a method, a system, a device and a storage medium for managing and controlling an Internet of things card, wherein the method comprises the following steps: firstly, network card data of an Internet of things card is obtained; the network card data comprises account opening data; determining multiple risk indexes of the Internet of things card according to preset risk rules and network card data; distributing risk grade labels for the Internet of things card through multiple risk indexes based on an HEM algorithm; tracing the bound user of the Internet of things card according to the account opening data; according to the risk level label, alarming is carried out on the bound user; and managing and controlling the Internet of things card according to the risk level label. According to the embodiment of the application, a closed-loop management and control scheme from risk discovery to hierarchical disposal for the Internet of things is realized, a risk grade label is obtained by measuring multiple risk indexes through an HEM algorithm, the risk degree of the Internet of things is effectively quantized, the rapid positioning of the risk Internet of things is facilitated, and the response speed of the Internet of things when encountering the risk of the Internet of things is improved.
Description
Technical Field
The application relates to the technical field of Internet of things, in particular to a method, a system, a device and a storage medium for managing and controlling an Internet of things card.
Background
With the continuous development of the internet of things technology, the number of internet of things cards mainly used by enterprises in batches is getting bigger, and accordingly, the internet of things cards are a serious security threat. Besides common virus attacks such as botnets, trojans and worms, the communication process of the internet of things card is also easy to have dangerous situations such as information leakage and malicious interception of communication contents under service scenes such as cross-regional use. Therefore, how to effectively control the internet of things card is a problem that needs to be solved when the internet of things technology is rapidly developed.
Disclosure of Invention
The present application is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, the application provides a method, a system, a device and a storage medium for managing and controlling an internet of things card.
In a first aspect, an embodiment of the present application provides an internet of things card management and control method, including: acquiring network card data of the Internet of things card; the network card data comprises account opening data; determining multiple risk indexes of the Internet of things card according to preset risk rules and the network card data; distributing a risk grade label for the Internet of things card through a plurality of risk indexes based on an HEM algorithm; according to the account opening data, tracing the bound user of the Internet of things card; according to the risk level label, alarming is carried out on the bound user; and managing and controlling the Internet of things card according to the risk level label.
Optionally, the network card data further includes ticket data and service data, and the risk indicator may be divided into a network security risk, an information security risk and a service security risk; the network security risks comprise network intrusion and botnet crawl, the network intrusion comprises information leakage, weak passwords, remote control and information collection, and the botnet crawl comprises botnet, trojans, worms and viruses; wherein the information security risk comprises plaintext transmission, access overseas, bad information and access agent; wherein the business security risk comprises a use risk, a quality risk and a real-name risk, the use risk comprises a appropriation exception, an abuse exception and other exceptions, the quality risk comprises a three-rate exception, an arrearage exception and a business exception, and the real-name risk comprises a real-name risk and a function minimization risk.
Optionally, the allocating a risk level label to the internet of things card through the risk indicator based on the HEM algorithm includes: comparing the risk indexes pairwise to determine a plurality of first weight values; determining a judgment matrix according to the risk indicator and the first weight value; determining a relative weight coefficient according to the judgment matrix; performing mixed operation according to the relative weight coefficient and a preset membership matrix to obtain a fuzzy matrix and a fuzzy evaluation vector; determining a comprehensive evaluation value of the Internet of things card according to the relative weight coefficient, the fuzzy matrix and the fuzzy evaluation vector; and determining the risk grade label according to the comprehensive evaluation value.
Optionally, before the step of performing a mixing operation according to the relative weight coefficient and a preset membership matrix to obtain a fuzzy matrix and a fuzzy evaluation vector, the method further includes a step of performing consistency check on the relative weight coefficient, where the step includes: calculating to obtain the maximum eigenvector of the judgment matrix according to the relative weight coefficient; determining a consistency index of the judgment matrix according to the maximum eigenvector; determining consistency ratio according to the consistency index and a random consistency index obtained by table look-up; when the consistency ratio is less than 0.1, the consistency check passes.
Optionally, the method further comprises: when the consistency ratio is greater than or equal to 0.1, the consistency check fails; and readjusting the judgment matrix, determining a new relative weight coefficient according to the new judgment matrix, and performing the consistency check again.
Optionally, the risk level label includes ultra-high risk, medium-high risk, low risk and safety, and the alarming the bound user according to the risk level label includes: performing network page alarm on the bound user with the risk level label of low risk and safety; and carrying out short message alarm on the bound users with the risk level labels of ultra-high risk, high risk and medium-high risk.
Optionally, the managing and controlling the internet of things card according to the risk level tag includes: and performing at least one of shutdown, network access limitation and number limitation functions on the Internet of things card with the risk level labels of ultra-high risk, high risk and medium-high risk.
In a second aspect, an embodiment of the present application provides an internet of things card management and control system, including: the risk data management module is used for acquiring network card data of the Internet of things card; the network card data comprises account opening data; the risk monitoring and early warning module is used for determining a plurality of risk indexes of the Internet of things card according to preset risk rules and the network card data; the risk grading module is used for distributing risk grade labels for the internet of things card through a plurality of risk indexes based on an HEM algorithm; the source tracing management module is used for tracing the bound user of the Internet of things card according to the risk level label and the account opening data; the Internet of things card processing module is used for alarming the bound user according to the risk level label; and the Internet of things card is controlled according to the risk level label.
In a third aspect, an embodiment of the present application provides an internet of things card management and control device, including: at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor is caused to implement the internet of things card management method according to the first aspect.
In a fourth aspect, embodiments of the present application provide a computer storage medium, in which a processor-executable program is stored, and the processor-executable program, when executed by the processor, is configured to implement the method for managing and controlling an internet of things card according to the first aspect.
The beneficial effects of the embodiment of the application are as follows: firstly, network card data of an Internet of things card is obtained; the network card data comprises account opening data; determining multiple risk indexes of the Internet of things card according to preset risk rules and network card data; distributing risk grade labels for the Internet of things card through multiple risk indexes based on an HEM algorithm; tracing the bound user of the Internet of things card according to the account opening data; according to the risk level label, alarming is carried out on the bound user; and managing and controlling the Internet of things card according to the risk level label. According to the embodiment of the application, a closed-loop management and control scheme from risk discovery to hierarchical disposal for the Internet of things is realized, a risk grade label is obtained by measuring multiple risk indexes through an HEM algorithm, the risk degree of the Internet of things is effectively quantized, the rapid positioning of the risk Internet of things is facilitated, and the response speed of the Internet of things when encountering the risk of the Internet of things is improved.
Drawings
The accompanying drawings are included to provide a further understanding of the claimed subject matter and are incorporated in and constitute a part of this specification, illustrate embodiments of the subject matter and together with the description serve to explain the principles of the subject matter and not to limit the subject matter.
Fig. 1 is a schematic view of an internet of things card management and control system provided in an embodiment of the present application;
fig. 2 is a flowchart illustrating steps of a card management and control method for the internet of things according to an embodiment of the present disclosure;
fig. 3 is a flowchart of a step of assigning a risk level label to an internet of things card through multiple risk indicators according to an embodiment of the present application;
FIG. 4 is a flowchart of the steps provided by an embodiment of the present application for performing a consistency check on relative weighting coefficients;
fig. 5 is a schematic view of an internet of things card management and control device provided in the embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that although functional block divisions are provided in the system drawings and logical orders are shown in the flowcharts, in some cases, the steps shown and described may be performed in different orders than the block divisions in the systems or in the flowcharts. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
With the continuous development of the internet of things technology, the number of internet of things cards mainly used by enterprises in batches is getting bigger, and accordingly, the internet of things cards are a serious security threat. Besides common virus attacks such as botnets, trojans and worms, the communication process of the internet of things card is also easy to have dangerous situations such as information leakage and malicious interception of communication contents under service scenes such as cross-regional use. Therefore, how to effectively control the internet of things card is a problem that needs to be solved when the internet of things technology is rapidly developed.
Based on this, the application provides a method, a system, a device and a storage medium for managing and controlling an internet of things card, wherein the method comprises the following steps: firstly, network card data of an Internet of things card is obtained; the network card data comprises account opening data; determining multiple risk indexes of the Internet of things card according to preset risk rules and network card data; distributing risk grade labels for the Internet of things card through multiple risk indexes based on an HEM algorithm; tracing the bound user of the Internet of things card according to the account opening data; according to the risk level label, alarming is carried out on the bound user; and managing and controlling the Internet of things card according to the risk level label. According to the embodiment of the application, a closed-loop management and control scheme from risk discovery to hierarchical disposal for the Internet of things is realized, a risk grade label is obtained by measuring multiple risk indexes through an HEM algorithm, the risk degree of the Internet of things is effectively quantized, the rapid positioning of the risk Internet of things is facilitated, and the response speed of the Internet of things when encountering the risk of the Internet of things is improved.
The embodiments of the present application will be further explained with reference to the drawings.
Referring to fig. 1, fig. 1 is a schematic view of an internet of things card management and control system provided in an embodiment of the present application, where the system 100 includes a risk data management module 110, a risk monitoring and early warning module 120, a risk classification module 130, a traceability management module 140, and an internet of things card handling module 150.
The risk data management module is used for acquiring network card data of the Internet of things card; the network card data includes, but is not limited to, account opening data, call ticket data, and service data. In the embodiment of the application, the risk data management module filters, merges, compresses and classifies the acquired network card data by adopting a streaming extreme frame and a batch processing calculation scheme, and the network card data processed by the risk data management module is used as input data of the risk monitoring and early warning module.
The system comprises a risk monitoring and early warning module, a risk monitoring and early warning module and a risk analysis module, wherein the risk monitoring and early warning module is used for determining multiple risk indexes of an internet of things card according to preset risk rules and network card data; and the early warning unit is used for matching the network card data according to a preset risk rule so as to determine the value of a risk index which may influence the risk of the Internet of things network card.
And the risk grading module is used for distributing risk grade labels for the internet of things card through multiple risk indexes based on an HEM algorithm. The following description will be made for the process of the risk classification module classifying the internet of things card, and will not be described herein again.
And the source tracing management module is used for tracing the bound user of the Internet of things card according to the risk level label and the account opening data. In the above, the network card data includes account opening data of the internet of things card, and the source tracing management module determines the user associated with the current internet of things card according to the account opening data.
And the processing module of the internet of things card is used for alarming the bound user according to the risk level label and managing and controlling the internet of things card according to the risk level label.
The application of the method for controlling an internet of things card described in fig. 1 is also provided, and referring to fig. 2, fig. 2 is a flow chart of steps of the method for controlling an internet of things card provided in the embodiment of the application, and the method includes, but is not limited to, steps S200 to S250:
s200, network card data of the Internet of things card are obtained;
specifically, a large number of internet-of-things devices in the internet of things need to use an internet-of-things card, and account opening data of a user, namely information such as contact information and a residential address of the user, can be bound when the internet-of-things card opens an account; furthermore, in the using process of the internet of things equipment, the internet of things card is used for communication, so that call ticket data can be generated; in addition, the internet of things equipment can also generate service data in the process because the service needs other equipment to communicate, or heartbeat data and the like are uploaded to a background server. Therefore, in this step, the network card data may be acquired through the risk data management module, and the acquired network card data includes, but is not limited to, account opening data, ticket data, and service data.
S210, determining multiple risk indexes of the Internet of things card according to preset risk rules and network card data;
specifically, through a configuration unit in the risk monitoring and early warning module, a user can pre-configure risk rules which may affect the risk of the internet of things card according to needs, and determine different threshold values corresponding to the risk rules. Then, the early warning unit in the risk monitoring early warning module compares the network card data with the preset risk rules, and determines multiple risk indexes corresponding to the risk condition of the internet of things card in the network card data.
For example, the risk index is mainly affected by the network security risk, the information security risk, and the business security risk, and each of these items includes a primary index and a secondary index, and refer to table 1, where table 1 is the index details of the risk index provided in the embodiment of the present application.
TABLE 1
As shown in Table 1, with YiRepresenting risk index, i representing the order of the indices, YijRepresenting the primary index, j representing the order of the primary indexes, by YijkIndicating the secondary index, k indicating the order of the secondary indexes.It should be noted that the risk indicator shown in table 1 follows the overall integrity and relative independence principle, that is, the risk indicator shown in table 1 satisfies the following formula:
and determining the specific numerical value of each Internet of things card corresponding to each risk index in the table 1 according to the indexes in the table 1 and a preset risk rule.
S220, distributing risk grade labels for the Internet of things card through multiple risk indexes based on an HEM algorithm;
specifically, the HEM (hybrid evaluation method) algorithm in the present application refers to a method of determining a relative weight coefficient by combining a layer analysis method and then determining a risk level by combining a fuzzy comprehensive evaluation method. A risk classification module in the system distributes risk classification labels for the Internet of things card through multiple risk indexes based on an HEM algorithm.
Referring to fig. 3, fig. 3 is a flowchart illustrating a step of assigning a risk level label to an internet of things card through multiple risk indicators according to an embodiment of the present application, where the method includes, but is not limited to, steps S300 to S350:
s300, comparing the risk indexes pairwise to determine a plurality of first weight values;
specifically, since the risk indexes are judged more by combining the judgment matrix in the application, if the same comparison standard is used for all the indexes, the obtained result is often inaccurate. Therefore, the application proposes that the weight coefficients of the risk indexes are determined by using the judgment matrix, the judgment matrix does not compare all the risk indexes together, but compares every two risk indexes oppositely, and the difficulty of comparing the risk indexes with different properties is reduced as much as possible by using the relative weight coefficients, so that the accuracy of judging the weight of the risk indexes is improved.
Therefore, in this step, the risk indicators are compared pairwise to determine a plurality of first weight values. It should be noted that the first weight value is not necessarily fixed, and the first weight value is only a result obtained by comparing two risk indicators under the currently set risk rule.
S310, determining a judgment matrix according to the risk index and the first weight value;
specifically, assuming that there are n risk indicators, an n-order decision matrix may be constructed, and an n-order decision matrix may be constructed according to the risk indicators and the first weight values calculated in step S300. By fijTo represent the first weight value of the risk indicator, the characteristic of the judgment matrix is: 1) the values on both sides of the diagonal are reciprocal, i.e.
2) Any index is equally important compared to itself, with a value of 1, i.e. fii1. Therefore, an n-th order decision matrix as shown in table 2 below can be constructed. Referring to table 2, table 2 is a schematic table of the n-order decision matrix provided in the embodiment of the present application.
TABLE 2
Wherein, Y1...YnAnd the risk index is the risk index of the Internet of things card.
S320, determining a relative weight coefficient according to the judgment matrix;
specifically, according to the judgment matrix constructed in step S310, the relative weight coefficient corresponding to each index is calculated.
Firstly, each row of elements of the judgment matrix is normalized, and the result after normalization of the risk index Y is set as
The normalization process satisfies the following equation:
then, the normalized decision matrices are added in rows, and the relative weight coefficients are represented by W
Representing the result after addition, the following formula is satisfied:
finally, to
And (3) carrying out normalization to obtain a relative weight coefficient W, wherein the calculation formula is as follows:
W＝[w1,…wn]T,
and calculating to obtain a relative weight coefficient W according to the formula and the steps.
In the embodiment of the application, in order to verify whether the relative weight coefficient can effectively represent the weight of the risk indicator, a consistency indicator is also introduced to perform consistency check on the relative weight coefficient. Referring to fig. 4, fig. 4 is a flowchart illustrating steps of performing a consistency check on relative weight coefficients according to an embodiment of the present application, where the method includes, but is not limited to, steps S400 to S430:
s400, calculating to obtain the maximum eigenvector of the judgment matrix according to the relative weight coefficient;
specifically, it is assumed that the maximum eigenvector of the decision matrix is denoted as LmaxThen L ismaxL can be calculated according to the following formulamax。
S410, determining a consistency index of the judgment matrix according to the maximum eigenvector;
specifically, the consistency index is referred to as CI, and the calculation formula of CI is as follows:
CI＝(Lmax-n)/(n-1)
according to the formula, the consistency index CI can be obtained through calculation, and generally, when the CI is less than or equal to 0.1, the judgment matrix is considered to have consistency, namely, the consistency passes through the inspection.
S420, determining a consistency ratio according to the consistency index and a random consistency index obtained by table lookup;
specifically, since the judgment error of CI increases with the increase of n, that is, the reliability of CI value decreases on the basis of a larger n, the random consistency index RI is introduced to adjust CI. Referring to table 3, table 3 shows the correspondence between RI and the order n.
| n | 1 | 2 | 3 | 4 | 5 | ... |
| RI | 0 | 0 | 0.58 | 0.90 | 1.12 | ... |
TABLE 3
From the consistency index C and the random consistency index RI obtained by table lookup, a consistency ratio CR, which is CI/RI, can be calculated.
S430, when the consistency ratio is less than 0.1, the consistency check is passed;
specifically, when the consistency ratio is less than 0.1, that is, CR is less than or equal to 0.1, the consistency check is passed, and the current judgment matrix is reliable.
On the contrary, when the consistency ratio is greater than or equal to 0.1 and CR is greater than or equal to 0.1, the consistency check does not pass this time, and it is necessary to return to step S310 to re-determine the determination matrix and re-execute the following steps S320 and the consistency check shown in fig. 4.
Through steps S400-S430, the embodiment of the present application provides a step of performing a consistency check, and after step S320 is described, step S330 is described.
S330, performing mixed operation according to the relative weight coefficient and a preset membership matrix to obtain a fuzzy matrix and a fuzzy evaluation vector;
specifically, the complete classification of the internet of things card is completed according to the relative weight coefficient, and then the fuzzy comprehensive evaluation results of the primary index and the secondary index under the same category are obtained according to the fuzzy comprehensive evaluation algorithm.
For example, from Table 1 above, it can be seen that the cyber-security risk can be represented as Y1＝{Y11,Y12Is and Y is1Corresponding first level index network intrusion Y11Can be represented as Y11＝{Y111,Y112,Y113,Y114}，Y1Stiff wood worm Y as corresponding first-level index12Can be represented as Y12＝{Y121,Y122,Y123,Y124}. By analogy, all risk indicators in table 1 can be classified and represented.
Similarly, corresponding to the risk indicators, the relative weight coefficient W of each risk indicator can also be expressed correspondingly, for example, the relative weight coefficient of the cyber-security risk can be expressed as W1＝{W11,W12H and W1Relative weight coefficient W of corresponding first-level index network intrusion11Can be represented as W11＝{W111,W112,W113,W114}。
Constructing a membership matrix Ri，Ri＝{ri1,ri2,...rimWhere m denotes the order of the elements of the membership degree matrix. Such as R11Membership matrix, R, representing the risk of network intrusion111Membership matrix, r, representing the leakage of information from its subsetimAnd a fuzzy input value representing the influence of each sub-risk index on the risk level, wherein a specific numerical value can be input according to the actual condition.
And performing mixed operation on the relative weight coefficient sum and a preset membership matrix, wherein the specific formula is as follows:
Q′ij＝Wij*Rij
wherein, to Q'ijCarrying out normalization processing to obtain a membership matrix Qij. Membership evaluation value Q of network intrusion11Membership evaluation value Q of stiff wood creep12(ii) a Thereby obtaining a network security risk first-level fuzzy matrix Q1＝{Q11,Q12And fourthly, performing composite operation of the fuzzy matrix again and performing normalization processing to obtain a secondary fuzzy evaluation vector G1By analogy, a first-level fuzzy evaluation vector Q of information security is obtained2Second order fuzzy evaluation vector G of business risk3。
S340, determining a comprehensive evaluation value of the Internet of things card according to the relative weight coefficient, the fuzzy matrix and the fuzzy evaluation vector;
specifically, a comprehensive evaluation value C of the internet of things card is calculated according to the relative weight coefficient W, the fuzzy matrix Q and the fuzzy evaluation vector G, and the specific calculation formula is as follows:
C＝W*R*N＝W*{G1,Q1,G3}
wherein W denotes a relative weight coefficient, and N is (95,85,65,55, 45).
S350, determining a risk grade label according to the comprehensive evaluation value;
specifically, the risk level labels are classified into ultra-high risk, medium-high risk, low risk and safety according to the comprehensive evaluation value, and refer to table 4, where table 4 is a correspondence table between the risk level labels and the comprehensive evaluation value provided in the embodiment of the present application.
| Risk rating | VH | H | M | L | S |
| Comprehensive evaluation value | >90 | 80～90 | 60～80 | 50～60 | <50 |
TABLE 4
According to table 4, according to the comprehensive evaluation value of each internet of things card, a corresponding risk level label is allocated to the internet of things card.
Step S220 has already been described through steps S300-S350, and step S230 is described below.
S230, tracing the bound user of the Internet of things card according to account opening data;
specifically, after the risk level label of the internet of things card is determined, the source tracing management module may trace the source of the bound user of the internet of things card according to account opening data of the internet of things card if the bound user of the internet of things card needs to be notified.
S240, alarming is carried out on the bound user according to the risk level label;
specifically, the internet of things card handling module performs different types of alarms on bound users with different risk level tags, for example, a network page alarm is performed on bound users with low-risk and safe risk level tags; and carrying out short message alarm on bound users with the risk level labels of ultra-high risk, high risk and medium-high risk.
S250, managing and controlling the Internet of things card according to the risk level label;
specifically, the internet of things card handling module performs at least one of shutdown, network access limitation and number limitation functions on the internet of things cards with the risk level labels of being in ultra-high risk, high risk and medium-high risk.
Through steps S200 to S250, the embodiment of the present application provides a method for managing and controlling an internet of things card, which includes first obtaining network card data of the internet of things card; the network card data comprises account opening data; determining multiple risk indexes of the Internet of things card according to preset risk rules and network card data; distributing risk grade labels for the Internet of things card through multiple risk indexes based on an HEM algorithm; tracing the bound user of the Internet of things card according to the account opening data; according to the risk level label, alarming is carried out on the bound user; and managing and controlling the Internet of things card according to the risk level label. According to the embodiment of the application, a closed-loop management and control scheme from risk discovery to hierarchical disposal for the Internet of things is realized, a risk grade label is obtained by measuring multiple risk indexes through an HEM algorithm, the risk degree of the Internet of things is effectively quantized, the rapid positioning of the risk Internet of things is facilitated, and the response speed of the Internet of things when encountering the risk of the Internet of things is improved.
Referring to fig. 5, fig. 5 is a schematic diagram of an internet of things card management and control apparatus provided in the embodiment of the present application, where the apparatus 500 includes at least one processor 510, and further includes at least one memory 520 for storing at least one program; in fig. 5, a processor and a memory are taken as an example.
The processor and memory may be connected by a bus or other means, such as by a bus in FIG. 5.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
The embodiment of the application also discloses a computer storage medium, wherein a program executable by a processor is stored, and the program executable by the processor is used for realizing the method provided by the application when being executed by the processor.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
While the preferred embodiments of the present invention have been described, the present invention is not limited to the above embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and such equivalent modifications or substitutions are included in the scope of the present invention defined by the claims.
Claims (10)
1. An IOT card management and control method is characterized by comprising the following steps:
acquiring network card data of the Internet of things card; the network card data comprises account opening data;
determining multiple risk indexes of the Internet of things card according to preset risk rules and the network card data;
distributing a risk grade label for the Internet of things card through a plurality of risk indexes based on an HEM algorithm;
according to the account opening data, tracing the bound user of the Internet of things card;
according to the risk level label, alarming is carried out on the bound user;
and managing and controlling the Internet of things card according to the risk level label.
2. The IOT card management and control method according to claim 1, wherein the network card data further includes ticket data and service data, and the risk indicators can be divided into network security risks, information security risks and service security risks;
the network security risks comprise network intrusion and botnet crawl, the network intrusion comprises information leakage, weak passwords, remote control and information collection, and the botnet crawl comprises botnet, trojans, worms and viruses;
wherein the information security risk comprises plaintext transmission, access overseas, bad information and access agent;
wherein the business security risk comprises a use risk, a quality risk and a real-name risk, the use risk comprises a appropriation exception, an abuse exception and other exceptions, the quality risk comprises a three-rate exception, an arrearage exception and a business exception, and the real-name risk comprises a real-name risk and a function minimization risk.
3. The IOT card management and control method according to claim 1, wherein the assigning a risk level label to the IOT card through the risk indicator based on the HEM algorithm comprises:
comparing the risk indexes pairwise to determine a plurality of first weight values;
determining a judgment matrix according to the risk indicator and the first weight value;
determining a relative weight coefficient according to the judgment matrix;
performing mixed operation according to the relative weight coefficient and a preset membership matrix to obtain a fuzzy matrix and a fuzzy evaluation vector;
determining a comprehensive evaluation value of the Internet of things card according to the relative weight coefficient, the fuzzy matrix and the fuzzy evaluation vector;
and determining the risk grade label according to the comprehensive evaluation value.
4. The IOT card management and control method according to claim 3, wherein before the step of performing the hybrid operation according to the relative weight coefficients and the preset membership matrix to obtain the fuzzy matrix and the fuzzy evaluation vector, the method further comprises a step of performing consistency check on the relative weight coefficients, and the step comprises:
calculating to obtain the maximum eigenvector of the judgment matrix according to the relative weight coefficient;
determining a consistency index of the judgment matrix according to the maximum eigenvector;
determining consistency ratio according to the consistency index and a random consistency index obtained by table look-up;
when the consistency ratio is less than 0.1, the consistency check passes.
5. The IOT card management and control method according to claim 4, further comprising:
when the consistency ratio is greater than or equal to 0.1, the consistency check fails;
and readjusting the judgment matrix, determining a new relative weight coefficient according to the new judgment matrix, and performing the consistency check again.
6. The IOT card management and control method according to any one of claims 1-5, wherein the risk level labels comprise ultra-high risk, medium high risk, low risk and security, and the alarming the bound user according to the risk level labels comprises:
performing network page alarm on the bound user with the risk level label of low risk and safety;
and carrying out short message alarm on the bound users with the risk level labels of ultra-high risk, high risk and medium-high risk.
7. The internet of things card management and control method according to claim 6, wherein the managing and controlling the internet of things card according to the risk level label comprises:
and performing at least one of shutdown, network access limitation and number limitation functions on the Internet of things card with the risk level labels of ultra-high risk, high risk and medium-high risk.
8. The utility model provides a thing networking card management and control system which characterized in that includes:
the risk data management module is used for acquiring network card data of the Internet of things card; the network card data comprises account opening data;
the risk monitoring and early warning module is used for determining a plurality of risk indexes of the Internet of things card according to preset risk rules and the network card data;
the risk grading module is used for distributing risk grade labels for the internet of things card through a plurality of risk indexes based on an HEM algorithm;
the source tracing management module is used for tracing the bound user of the Internet of things card according to the risk level label and the account opening data;
the Internet of things card processing module is used for alarming the bound user according to the risk level label; and the Internet of things card is controlled according to the risk level label.
9. The utility model provides an thing networking card management and control device which characterized in that includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the internet of things card management method of any one of claims 1-7.
10. A computer storage medium having stored therein a program executable by a processor, wherein the program executable by the processor is configured to implement the internet of things card management method according to any one of claims 1 to 7 when executed by the processor.
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