CN116451134A - Vulnerability information classification method and device, storage medium and electronic equipment - Google Patents
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Abstract
The application discloses a method and a device for classifying vulnerability information, a storage medium and electronic equipment, and relates to the technical field of smart families, wherein the method for classifying vulnerability information comprises the following steps: establishing a weight matrix of vulnerability information in a plurality of error type dimensions, wherein an element x of the weight matrix ij The weight value is used for indicating the jth vulnerability information in the ith dimension, and j and i are positive integers; establishing a fuzzy similar matrix corresponding to the vulnerability information according to the weight matrix, wherein an element r in the fuzzy similar matrix ab The method comprises the steps of indicating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions, wherein a and b are positive integers; the vulnerability information is classified according to the fuzzy similarity matrix, and the problems that the classification, the grading, the quality measurement statistics of the vulnerability information lack comprehensive and relatively objective data evaluation and the like are solved by adopting the technical scheme.
Description
Technical Field
The application relates to the technical field of smart families, in particular to a method and a device for classifying vulnerability information, a storage medium and electronic equipment.
Background
With the rapid development of information technology, it is urgent to process more and more complex data in order to meet the industry demand of rapid development iteration. In the present internet data age, any application is not separated from the data.
The number of the total vulnerability information of the current quality department platform is huge, but the classification, the grading and the quality measurement statistics of the vulnerability information lack comprehensive and relatively objective data evaluation, and more are the classification and the grading of the vulnerability information which are subjective and artificial.
Aiming at the problems of lack of comprehensive and relatively objective data evaluation and the like of classification, rating and quality measurement statistics of vulnerability information in the related technology, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a method and a device for classifying vulnerability information, a storage medium and electronic equipment, and aims to at least solve the problems that in the related technology, the classification, the rating and the quality measurement statistics of the vulnerability information are lack of comprehensive and relatively objective data evaluation and the like.
According to an embodiment of the present application, there is provided a method for classifying vulnerability information, including: establishing a weight matrix of vulnerability information in a plurality of error type dimensions, wherein an element x of the weight matrix ij The weight value is used for indicating the jth vulnerability information in the ith dimension, and j and i are positive integers; establishing a fuzzy similar matrix corresponding to the vulnerability information according to the weight matrix, wherein an element r in the fuzzy similar matrix ab The method comprises the steps of indicating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions, wherein a and b are positive integers; and classifying the vulnerability information according to the fuzzy similarity matrix.
In an exemplary embodiment, classifying the vulnerability information according to the fuzzy similarity matrix includes: determining a transfer closure of the fuzzy similar matrix through a flattening method, and determining the transfer closure as a fuzzy equivalent matrix corresponding to the fuzzy similar matrix; determining a lambda value of the fuzzy equivalent matrix, and determining a lambda-truncated matrix corresponding to the fuzzy equivalent matrix according to the lambda value; and classifying the vulnerability information according to the lambda-cut matrix.
In an exemplary embodiment, determining a λ -truncated matrix corresponding to the fuzzy equivalence matrix according to the λ value includes: setting the value of an element larger than or equal to lambda in the fuzzy equivalent matrix to be 1, and setting the value of an element smaller than lambda in the fuzzy equivalent matrix to be 0, so as to obtain the lambda-cut matrix; classifying the vulnerability information according to the lambda-cut matrix, including: determining the value of any row of elements in the lambda-cut matrix; and under the condition that the values of other row elements in the lambda-cut matrix are the same as the values of any row element, determining that the vulnerability information corresponding to the other row elements and any row element is the vulnerability information of the same type.
In an exemplary embodiment, establishing a fuzzy similarity matrix corresponding to the vulnerability information according to the weight matrix includes: normalizing the data of the elements in the weight matrix by means of range conversion to obtain a normalized weight matrix, wherein the elements q of the normalized weight matrix ij A normalized weight value for indicating the jth vulnerability information in the ith dimension, j and i being positive integers; and establishing a fuzzy similar matrix corresponding to the vulnerability information according to the standardized weight matrix.
In an exemplary embodiment, establishing a fuzzy similarity matrix corresponding to the vulnerability information according to the standardized weight matrix includes: calculating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions according to the standardized weight matrix; determining the weight similarity as an element r of the fuzzy similarity matrix ab Is a numerical value of (2).
In one exampleIn an exemplary embodiment, calculating the weighted similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions according to the normalized weight matrix includes: determining the weight similarity p of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions through the following formula ab ：Where k is used to indicate the kth dimension and m is used to indicate the number of the plurality of dimensions.
In one exemplary embodiment, establishing a weight matrix of vulnerability information in a plurality of error type dimensions includes: determining the error type of the vulnerability information generated in the test process, wherein the error type at least comprises one of the following steps: a main flow class, a synchronous service class, an asynchronous service class, a concurrent service class and a page display class; and determining a weight value corresponding to the error type, and determining a weight matrix of the vulnerability information in a plurality of error type dimensions according to the weight value corresponding to the error type.
According to another embodiment of the present application, there is further provided a device for classifying vulnerability information, including: a first establishing module, configured to establish a weight matrix of vulnerability information in multiple error type dimensions, where an element x of the weight matrix ij The weight value is used for indicating the jth vulnerability information in the ith dimension, and j and i are positive integers; a second establishing module, configured to establish a fuzzy similarity matrix corresponding to the vulnerability information according to the weight matrix, where an element r in the fuzzy similarity matrix ab The method comprises the steps of indicating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions, wherein a and b are positive integers; and the classification module is used for classifying the vulnerability information according to the fuzzy similarity matrix.
According to yet another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium having a computer program stored therein, wherein the computer program is configured to perform the above-mentioned vulnerability information classification method when run.
According to still another aspect of the embodiments of the present application, there is further provided an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the above-mentioned method for classifying vulnerability information through the computer program.
In an embodiment of the present application, a weight matrix of vulnerability information in multiple error type dimensions is established, where an element x of the weight matrix ij The weight value is used for indicating the jth vulnerability information in the ith dimension, and j and i are positive integers; establishing a fuzzy similar matrix corresponding to the vulnerability information according to the weight matrix, wherein an element r in the fuzzy similar matrix ab The method comprises the steps of indicating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions, wherein a and b are positive integers; classifying the vulnerability information according to the fuzzy similarity matrix; by adopting the technical scheme, the problems of lack of comprehensive and relatively objective data evaluation and the like of classification, rating and quality measurement statistics of the vulnerability information are solved.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic diagram of a hardware environment of a method for classifying vulnerability information according to an embodiment of the present application;
FIG. 2 is a flow chart of a method of categorizing vulnerability information according to an embodiment of the present application;
FIG. 3 is a flow chart of a method of categorizing vulnerability information according to an alternative embodiment of the present application;
FIG. 4 is a block diagram (I) of a device for classifying vulnerability information according to an embodiment of the present application;
fig. 5 is a block diagram (ii) of a device for classifying vulnerability information according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to one aspect of the embodiment of the application, a method for classifying vulnerability information is provided. The method for classifying the vulnerability information is widely applied to full-house intelligent digital control application scenes such as intelligent Home (Smart Home), intelligent Home equipment ecology, intelligent Home (Intelligence House) ecology and the like. Alternatively, in the present embodiment, the above-described vulnerability information classification method may be applied to a hardware environment constituted by the terminal device 102 and the server 104 as shown in fig. 1. As shown in fig. 1, the server 104 is connected to the terminal device 102 through a network, and may be used to provide services (such as application services and the like) for a terminal or a client installed on the terminal, a database may be set on the server or independent of the server, for providing data storage services for the server 104, and cloud computing and/or edge computing services may be configured on the server or independent of the server, for providing data computing services for the server 104.
The network may include, but is not limited to, at least one of: wired network, wireless network. The wired network may include, but is not limited to, at least one of: a wide area network, a metropolitan area network, a local area network, and the wireless network may include, but is not limited to, at least one of: WIFI (Wireless Fidelity ), bluetooth. The terminal device 102 may not be limited to a PC, a mobile phone, a tablet computer, an intelligent air conditioner, an intelligent smoke machine, an intelligent refrigerator, an intelligent oven, an intelligent cooking range, an intelligent washing machine, an intelligent water heater, an intelligent washing device, an intelligent dish washer, an intelligent projection device, an intelligent television, an intelligent clothes hanger, an intelligent curtain, an intelligent video, an intelligent socket, an intelligent sound box, an intelligent fresh air device, an intelligent kitchen and toilet device, an intelligent bathroom device, an intelligent sweeping robot, an intelligent window cleaning robot, an intelligent mopping robot, an intelligent air purifying device, an intelligent steam box, an intelligent microwave oven, an intelligent kitchen appliance, an intelligent purifier, an intelligent water dispenser, an intelligent door lock, and the like.
In this embodiment, a method for classifying vulnerability information is provided and applied to a computer terminal, and fig. 2 is a flowchart of the method for classifying vulnerability information according to an embodiment of the present application, where the flowchart includes the following steps:
step S202, establishing a weight matrix of vulnerability information in a plurality of error type dimensions, wherein an element x of the weight matrix ij The weight value is used for indicating the jth vulnerability information in the ith dimension, and j and i are positive integers;
the number of vulnerability information is plural.
It should be noted that the plurality of error types include: main flow type, synchronous service type, asynchronous service type, concurrent service type, page presentation type.
For example, given a definition of vulnerability information in five dimension metrics, assume that the sum of all dimension weights of vulnerability information is 1:
the vulnerability information of the main flow type affects the main flow of the whole business test, the problem that other tests of the vulnerability information are meaningless to develop (such as a credit giving flow of opening a cut product in a scene payment bank, the subsequent borrowing and repayment cannot be developed after the credit giving failure) is solved, and the weight of the main flow type is defined to be 0.5.
The vulnerability information of the synchronous service type does not affect the conventional flow test of the whole business, but when the user inputs an abnormal parameter, the whole service is stopped abnormally, and the weight for defining the synchronous service type is 0.2.
Vulnerability information of an asynchronous service type does not affect the whole business process test, a user does not have abnormal perception in the whole business service process, but the user has problems in some non-real-time service experiences, and the weight of the asynchronous service type is 0.15.
The vulnerability information of the concurrent service type does not affect the whole business process test, when the user does not call the interface service in the final state in the process of processing in the whole business service process, the user initiates the same interface service call, does not intercept, and the weight of the definition of the concurrent service type is 0.1.
The vulnerability information of the page display type does not affect the whole business process test, the user experience of the page on the terminal is poor, and the weight for defining the page display type is 0.05 due to the page control and element display problem or ui design class problem.
It should be noted that, in the embodiment of the present invention, the weights corresponding to the error types are not limited.
It should be noted that one vulnerability information may correspond to one or more error types.
For example, in the case that the type of the vulnerability information is the main flow type and the asynchronous service type, the weighting matrix corresponding to the vulnerability information is [0.5,0,0.15,0,0 ]] T 。
Step S204, establishing a fuzzy similar matrix corresponding to the vulnerability information according to the weight matrix, wherein an element r in the fuzzy similar matrix ab The method comprises the steps of indicating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions, wherein a and b are positive integers;
and S206, classifying the vulnerability information according to the fuzzy similar matrix.
Through the steps, a weight matrix of the vulnerability information in a plurality of error type dimensions is established, wherein the element x of the weight matrix ij The weight value is used for indicating the jth vulnerability information in the ith dimension, and j and i are positive integers; establishing a fuzzy similar matrix corresponding to the vulnerability information according to the weight matrix, wherein an element r in the fuzzy similar matrix ab The method comprises the steps of indicating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions, wherein a and b are positive integers; the method and the device for classifying the vulnerability information according to the fuzzy similarity matrix solve the problems that in the related technology, classification, rating, quality measurement statistics of the vulnerability information lack comprehensive and relatively objective data evaluation and the like.
In many implementations of step S206, in one exemplary embodiment, this is achieved by: determining a transfer closure of the fuzzy similar matrix through a flattening method, and determining the transfer closure as a fuzzy equivalent matrix corresponding to the fuzzy similar matrix; determining a lambda value of the fuzzy equivalent matrix, and determining a lambda-truncated matrix corresponding to the fuzzy equivalent matrix according to the lambda value; and classifying the vulnerability information according to the lambda-cut matrix.
Optionally, determining the transitive closure of the fuzzy similarity matrix by a flattening method includes: determining the square of the matrix X to obtain the matrix X 2 The method comprises the steps of carrying out a first treatment on the surface of the Determining matrix X 2 Is equal to the matrix X, in the matrix X 2 In case of inequality with matrix X, matrix X is determined 2 To obtain a matrix X 4 The method comprises the steps of carrying out a first treatment on the surface of the Determination of X 4 And matrix X 2 Whether or not they are equal; at X 4 And matrix X 2 In the case of equality, determine X 4 A transitive closure for the matrix X; otherwise, continuing to execute the steps until the matrix is not changed any more.
For example, a matrixMatrix->Matrix arrayThus (S)>For the transfer closure of matrix X, i.eIs a fuzzy equivalent matrix of matrix X.
In an exemplary embodiment, determining a λ -truncated matrix corresponding to the fuzzy equivalence matrix according to the λ value includes: setting the value of an element larger than or equal to lambda in the fuzzy equivalent matrix to be 1, and setting the value of an element smaller than lambda in the fuzzy equivalent matrix to be 0, so as to obtain the lambda-cut matrix; classifying the vulnerability information according to the lambda-cut matrix, including: determining the value of any row of elements in the lambda-cut matrix; and under the condition that the values of other row elements in the lambda-cut matrix are the same as the values of any row element, determining that the vulnerability information corresponding to the other row elements and any row element is the vulnerability information of the same type.
By way of example only, the processing steps,λ=1, the λ -truncated matrix is +.>λ＝0.7When lambda-cut matrix is +.>
For example, in the lambda-cut matrixDividing the vulnerability information corresponding to the first row and the vulnerability information corresponding to the second row into vulnerability information of the same type; dividing vulnerability information corresponding to a third row into one type of vulnerability information; and dividing the vulnerability information corresponding to the fourth row into one type of vulnerability information.
In an exemplary embodiment, establishing a fuzzy similarity matrix corresponding to the vulnerability information according to the weight matrix includes: normalizing the data of the elements in the weight matrix by means of range conversion to obtain a normalized weight matrix, wherein the elements q of the normalized weight matrix ij A normalized weight value for indicating the jth vulnerability information in the ith dimension, j and i being positive integers; and establishing a fuzzy similar matrix corresponding to the vulnerability information according to the standardized weight matrix.
In particular, the method comprises the steps of,wherein x is ij Weight value, x representing jth vulnerability information in ith dimension min Is the minimum value in the original weight matrix; x is x max Is the maximum value in the original weight matrix.
In an exemplary embodiment, establishing a fuzzy similarity matrix corresponding to the vulnerability information according to the standardized weight matrix includes: calculating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions according to the standardized weight matrix; determining the weight similarity as an element r of the fuzzy similarity matrix ab Is a numerical value of (2).
Specifically, the weight similarity p of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions is determined by the following formula ab ：Where k is used to indicate the kth dimension and m is used to indicate the number of the plurality of dimensions.
Wherein q ak ∧q bk For indicating q ak 、q bk Is the minimum value of (a).
In one exemplary embodiment, establishing a weight matrix of vulnerability information in a plurality of error type dimensions includes: determining the error type of the vulnerability information generated in the test process, wherein the error type at least comprises one of the following steps: a main flow class, a synchronous service class, an asynchronous service class, a concurrent service class and a page display class; and determining a weight value corresponding to the error type, and determining a weight matrix of the vulnerability information in a plurality of error type dimensions according to the weight value corresponding to the error type.
For example, as shown in Table 1,
TABLE 1
| Dimension weight x ij \bug | Bug0 | Bug1 | Bug2 | Bug3 | Bug4 |
| Main flow class weight | 0.5 | 0 | 0.5 | 0 | 0.5 |
| Synchronizing service weights | 0.2 | 0.2 | 0 | 0.2 | 0 |
| Asynchronous service weight | 0 | 0.15 | 0.15 | 0 | 0.15 |
| Concurrency class service weight | 0 | 0.1 | 0 | 0.1 | 0 |
| Page presentation class weights | 0.05 | 0.05 | 0 | 0.05 | 0 |
The weight matrix corresponding to the above table 1 is
In order to better understand the process of the above-mentioned classification method of the vulnerability information, the following description is given with reference to the implementation method flow of the classification of the vulnerability information in the alternative embodiment, but the implementation method flow is not limited to the technical solution of the embodiment of the present application.
In the embodiment, the fuzzy theory classifier classifies the vulnerability information, and the basic idea of the fuzzy set is to activate the absolute membership in the set, and the membership of the element X to the set A is not 0 or 1, but 0, 1.
Fuzzy sets and membership functions:
one fuzzy set A on the universe X is called X's membership to A of the fuzzy set. The fuzzy set A is completely characterized by membership functions when mu A When = {0,1}, a degenerates into one common set. Fuzzy sets are sets used to express the concept of ambiguity, also called fuzzy sets, fuzzy subsets. A common collection refers to the totality of objects having certain attributes.
The determination method of the membership function comprises the following steps: the basic idea of fuzzy mathematics is the idea of membership. The key to establishing a mathematical model by applying the fuzzy mathematical method is to establish a membership function conforming to reality. How to determine membership functions of a fuzzy set has heretofore been an unsolved problem. The membership function method comprises the following steps:
the fuzzy statistical method comprises the following steps: the fuzzy statistical method is an objective method and is mainly determined according to the objective existence of membership on the basis of a fuzzy statistical test.
The assignment method comprises the following steps: the assignment method is a subjective method, and is mainly a method for determining some fuzzy set membership functions according to practical experience of people.
Other methods: in practical applications, the method used to determine membership functions of fuzzy sets is varied and is mainly determined according to the practical significance of the problem. For example, in economic management and social management, the membership of fuzzy sets can be obtained by means of the existing 'objective scale'.
Fig. 3 is a flowchart of a method for classifying vulnerability information according to an alternative embodiment of the present application, as shown in fig. 3, specifically including the following steps:
assuming that 5 bugs are classified by using Fuzzy Logic, the weights of the dimensions of each bug are as shown in the following table 2, and the Fuzzy similarity matrix is calculated on the original dimension data, so that the classification is completed:
TABLE 2
Step S301: dimension weight data is standardized;
by using the range conversion formulaProcessing the weight data, wherein x ij The weight value representing the jth bug in the ith dimension is not processed because the original weight data is already normalized. X is x min Is x ij Is the minimum value of (a); x is x max Is x ij Is the maximum value of (a).
Step S302: establishing a fuzzy similarity matrix by an arithmetic mean value method;
calculating a fuzzy similarity matrix R, establishing a similarity relation matrix of 5 dimension weights among the bugs according to the standardized numerical value, and calculating R by adopting an arithmetic average value ij The reference formula is as follows:
wherein r is ab The amount indicating the degree of similarity of the weight of the a-th bug and the b-th bug in 5 dimensions.
Fuzzy similarity matrix
Step S303: solving a transfer closure by adopting fuzzy matrix synthesis operation, namely a fuzzy equivalent matrix;
the method for transmitting the closure is that the fuzzy matrix repeatedly performs self-multiplication, and the closure is transmitted when the result t (R) is not changed, because the fuzzy matrix data is not standardized, the normalization is performed on the calculation result of the self-multiplication value.
Fuzzy equivalence matrix
Step S304: and clustering the vulnerability information according to the fuzzy equivalence matrix.
The classification is mainly carried out according to different lambda, and the formula is as follows:
such as 0.5<λ<When=1, the truncated matrix is:at this time, the types are classified into 5 types, and each bug is a separate type.
Such as 0.2<And when lambda is less than or equal to 0.5, the truncated matrix is as follows:at this time, it is classified into 3 categories, namely, bug0, bug2 and bug4, bug1 alone 1 category, bug3 alone 1 category.
According to the embodiment of the invention, a weight matrix of vulnerability information in a plurality of error type dimensions is established; establishing a fuzzy similar matrix corresponding to the vulnerability information according to the weight matrix; the method and the device for classifying the vulnerability information according to the fuzzy similarity matrix solve the problems that in the related technology, classification, rating, quality measurement statistics of the vulnerability information lack comprehensive and relatively objective data evaluation and the like.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method of the embodiments of the present application.
The embodiment of the invention provides a device for classifying vulnerability information, and fig. 4 is a structural block diagram (one) of the device for classifying vulnerability information according to the embodiment of the application; as shown in fig. 4, includes:
a first establishing module 42 for establishing a weight matrix of vulnerability information in multiple error type dimensions, wherein an element x of the weight matrix ij The weight value is used for indicating the jth vulnerability information in the ith dimension, and j and i are positive integers;
a second establishing module 44, configured to establish a fuzzy similarity matrix corresponding to the vulnerability information according to the weight matrix, where an element r in the fuzzy similarity matrix ab The method comprises the steps of indicating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions, wherein a and b are positive integers;
and the classification module 46 is configured to classify the vulnerability information according to the fuzzy similarity matrix.
By the device, a weight matrix of vulnerability information in a plurality of error type dimensions is established, wherein the element x of the weight matrix ij The weight value is used for indicating the jth vulnerability information in the ith dimension, and j and i are positive integers; establishing a fuzzy similar matrix corresponding to the vulnerability information according to the weight matrix, wherein an element r in the fuzzy similar matrix ab For indicating that the a-th vulnerability information and the b-th vulnerability information are in multiple dimensionsThe weight similarity of a and b are positive integers; the method and the device for classifying the vulnerability information according to the fuzzy similarity matrix solve the problems that in the related technology, classification, rating, quality measurement statistics of the vulnerability information lack comprehensive and relatively objective data evaluation and the like.
In an exemplary embodiment, the classification module 46 is configured to determine, by a flattening method, a transitive closure of the fuzzy similarity matrix, and determine the transitive closure as a fuzzy equivalent matrix corresponding to the fuzzy similarity matrix; determining a lambda value of the fuzzy equivalent matrix, and determining a lambda-truncated matrix corresponding to the fuzzy equivalent matrix according to the lambda value; and classifying the vulnerability information according to the lambda-cut matrix.
Optionally, a classification module 46 is used to determine the square of the matrix X to obtain the matrix X 2 The method comprises the steps of carrying out a first treatment on the surface of the Determining matrix X 2 Is equal to the matrix X, in the matrix X 2 In case of inequality with matrix X, matrix X is determined 2 To obtain a matrix X 4 The method comprises the steps of carrying out a first treatment on the surface of the Determination of X 4 And matrix X 2 Whether or not they are equal; at X 4 And matrix X 2 In the case of equality, determine X 4 A transitive closure for the matrix X; otherwise, continuing to execute the steps until the matrix is not changed any more.
For example, a matrixMatrix->Matrix arrayThus (S)>For the transfer closure of matrix X, i.eIs a fuzzy equivalent matrix of matrix X.
In an exemplary embodiment, the classification module 46 is configured to set a value of an element greater than or equal to λ in the fuzzy equivalent matrix to 1, and set a value of an element less than λ in the fuzzy equivalent matrix to 0, so as to obtain the λ -truncated matrix; classifying the vulnerability information according to the lambda-cut matrix, including: determining the value of any row of elements in the lambda-cut matrix; and under the condition that the values of other row elements in the lambda-cut matrix are the same as the values of any row element, determining that the vulnerability information corresponding to the other row elements and any row element is the vulnerability information of the same type.
By way of example only, the processing steps,λ=1, the λ -truncated matrix is +.>Lambda=0.7, lambda-cut matrix is +.>
For example, in the lambda-cut matrixDividing the vulnerability information corresponding to the first row and the vulnerability information corresponding to the second row into vulnerability information of the same type; dividing vulnerability information corresponding to a third row into one type of vulnerability information; and dividing the vulnerability information corresponding to the fourth row into one type of vulnerability information.
In an exemplary embodiment, fig. 5 is a block diagram (ii) of a device for classifying vulnerability information according to an embodiment of the present application; as shown in fig. 5, the apparatus further includes: a conversion module 52 for normalizing the data of the elements in the weight matrix by a range conversion method to obtain a normalized weight matrix, wherein the elements of the normalized weight matrixElement q ij A normalized weight value for indicating the jth vulnerability information in the ith dimension, j and i being positive integers; a second establishing module 44, configured to establish a fuzzy similar matrix corresponding to the vulnerability information according to the normalized weight matrix.
In particular, the method comprises the steps of,wherein x is ij Weight value, x representing jth vulnerability information in ith dimension min Is the minimum value in the original weight matrix; x is x max Is the maximum value in the original weight matrix.
In an exemplary embodiment, the second establishing module 44 is configured to calculate, according to the normalized weight matrix, a weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions; determining the weight similarity as an element r of the fuzzy similarity matrix ab Is a numerical value of (2).
Specifically, the weight similarity p of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions is determined by the following formula ab ：Where k is used to indicate the kth dimension and m is used to indicate the number of the plurality of dimensions.
Wherein q ak ∧q bk For indicating q ak 、q bk Is the minimum value of (a).
In an exemplary embodiment, the first establishing module 42 is configured to determine an error type of the vulnerability information generated during the testing process, where the error type includes at least one of the following: a main flow class, a synchronous service class, an asynchronous service class, a concurrent service class and a page display class; and determining a weight value corresponding to the error type, and determining a weight matrix of the vulnerability information in a plurality of error type dimensions according to the weight value corresponding to the error type.
For example, as shown in Table 1,
TABLE 1
/>
The weight matrix corresponding to the above table 1 is
Embodiments of the present application also provide a storage medium including a stored program, wherein the program performs the method of any one of the above when run.
Alternatively, in the present embodiment, the above-described storage medium may be configured to store program code for performing the steps of:
s1, establishing a weight matrix of vulnerability information in a plurality of error type dimensions, wherein an element x of the weight matrix ij The weight value is used for indicating the jth vulnerability information in the ith dimension, and j and i are positive integers;
s2, establishing a fuzzy similarity matrix corresponding to the vulnerability information according to the weight matrix, wherein an element r in the fuzzy similarity matrix ab The method comprises the steps of indicating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions, wherein a and b are positive integers;
and S3, classifying the vulnerability information according to the fuzzy similarity matrix.
Embodiments of the present application also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.
Alternatively, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:
s1, establishing vulnerability information in multiple error type dimensionsA weight matrix, wherein an element x of the weight matrix ij The weight value is used for indicating the jth vulnerability information in the ith dimension, and j and i are positive integers;
s2, establishing a fuzzy similarity matrix corresponding to the vulnerability information according to the weight matrix, wherein an element r in the fuzzy similarity matrix ab The method comprises the steps of indicating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions, wherein a and b are positive integers;
and S3, classifying the vulnerability information according to the fuzzy similarity matrix.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Alternatively, specific examples in this embodiment may refer to examples described in the foregoing embodiments and optional implementations, and this embodiment is not described herein.
It will be appreciated by those skilled in the art that the modules or steps of the application described above may be implemented in a general purpose computing device, they may be centralized on a single computing device, or distributed across a network of computing devices, or they may alternatively be implemented in program code executable by computing devices, such that they may be stored in a memory device for execution by the computing devices and, in some cases, the steps shown or described may be performed in a different order than what is shown or described, or they may be implemented as individual integrated circuit modules, or as individual integrated circuit modules. Thus, the present application is not limited to any specific combination of hardware and software.
The foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.
Claims (10)
1. A method for classifying vulnerability information, comprising:
establishing a weight matrix of vulnerability information in a plurality of error type dimensions, wherein an element x of the weight matrix ij The weight value is used for indicating the jth vulnerability information in the ith dimension, and j and i are positive integers;
establishing a fuzzy similar matrix corresponding to the vulnerability information according to the weight matrix, wherein an element r in the fuzzy similar matrix ab The method comprises the steps of indicating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions, wherein a and b are positive integers;
and classifying the vulnerability information according to the fuzzy similarity matrix.
2. The method of classifying vulnerability information according to claim 1, wherein classifying the vulnerability information according to the fuzzy similarity matrix comprises:
determining a transfer closure of the fuzzy similar matrix through a flattening method, and determining the transfer closure as a fuzzy equivalent matrix corresponding to the fuzzy similar matrix;
determining a lambda value of the fuzzy equivalent matrix, and determining a lambda-truncated matrix corresponding to the fuzzy equivalent matrix according to the lambda value;
and classifying the vulnerability information according to the lambda-cut matrix.
3. The method for classifying vulnerability information according to claim 2, wherein determining a λ -truncated matrix corresponding to the fuzzy equivalence matrix according to the λ value comprises:
setting the value of an element larger than or equal to lambda in the fuzzy equivalent matrix to be 1, and setting the value of an element smaller than lambda in the fuzzy equivalent matrix to be 0, so as to obtain the lambda-cut matrix;
classifying the vulnerability information according to the lambda-cut matrix, including:
determining the value of any row of elements in the lambda-cut matrix; and under the condition that the values of other row elements in the lambda-cut matrix are the same as the values of any row element, determining that the vulnerability information corresponding to the other row elements and any row element is the vulnerability information of the same type.
4. The method for classifying vulnerability information according to claim 1, wherein establishing a fuzzy similarity matrix corresponding to the vulnerability information according to the weight matrix comprises:
normalizing the data of the elements in the weight matrix by means of range conversion to obtain a normalized weight matrix, wherein the elements q of the normalized weight matrix ij A normalized weight value for indicating the jth vulnerability information in the ith dimension, j and i being positive integers;
and establishing a fuzzy similar matrix corresponding to the vulnerability information according to the standardized weight matrix.
5. The method for classifying vulnerability information according to claim 4, wherein establishing a fuzzy similarity matrix corresponding to the vulnerability information according to the standardized weight matrix comprises:
calculating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions according to the standardized weight matrix;
determining the weight similarity as an element r of the fuzzy similarity matrix ab Is a numerical value of (2).
6. The method of claim 4, wherein calculating the weighted similarity of the a-th vulnerability information and the b-th vulnerability information in a plurality of dimensions according to the normalized weight matrix comprises:
determining the weight similarity p of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions through the following formula ab ：
Where k is used to indicate the kth dimension and m is used to indicate the number of the plurality of dimensions.
7. The method of classifying vulnerability information of claim 1, wherein establishing a weight matrix of vulnerability information in a plurality of error type dimensions comprises:
determining the error type of the vulnerability information generated in the test process, wherein the error type at least comprises one of the following steps: a main flow class, a synchronous service class, an asynchronous service class, a concurrent service class and a page display class;
and determining a weight value corresponding to the error type, and determining a weight matrix of the vulnerability information in a plurality of error type dimensions according to the weight value corresponding to the error type.
8. A device for classifying vulnerability information, comprising:
a first establishing module, configured to establish a weight matrix of vulnerability information in multiple error type dimensions, where an element x of the weight matrix ij The weight value is used for indicating the jth vulnerability information in the ith dimension, and j and i are positive integers;
a second establishing module, configured to establish a fuzzy similarity matrix corresponding to the vulnerability information according to the weight matrix, where an element r in the fuzzy similarity matrix ab The method comprises the steps of indicating the weight similarity of the a-th vulnerability information and the b-th vulnerability information in multiple dimensions, wherein a and b are positive integers;
and the classification module is used for classifying the vulnerability information according to the fuzzy similarity matrix.
9. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored program, wherein the program when run performs the method of any of the preceding claims 1 to 7.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method according to any of the claims 1 to 7 by means of the computer program.
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