CN112084439B - A method, device, equipment and storage medium for identifying variables in a URL - Google Patents

Abstract

The invention relates to a method, a device, equipment and a storage medium for identifying variables in a URL. The method comprises the following steps: acquiring access path data of a website to be identified; preprocessing access path data to obtain a path relation data set and a hierarchical threshold set; identifying quantitative data and suspected variable data in the access path data according to the path relation data set and the hierarchical threshold set; verifying the suspected variable data to obtain variable data; and integrating and outputting the quantitative data and the variable data. By adopting the method, the variable identification can be automatically carried out on the access path data, and the variable identification efficiency is greatly improved.

Description

A method, device, equipment and storage medium for identifying variables in a URL

Technical field

The invention relates to the technical field of website testing and protection, and in particular to a method, device, equipment and storage medium for identifying variables in a URL.

Background technique

With the popularity of website applications, more and more websites are used in all walks of life. Before the website is put into use, in order to ensure that the website can operate normally as expected, a penetration test needs to be conducted on the website. Submitting attack code via parameters is a common means of pen testing or scanning a website.

There are usually three types of parameter transfer forms. The first one is called Query String, and the transfer method is through URL. This method is commonly used to obtain data, such as obtaining details of an article. The second method is to pass it through a form. The front end assembles the content filled in by the user according to the requirements and puts it in the payload part of the request packet. This method is commonly used to send data to the backend, such as sending article-related content to the backend when creating an article. The third type is included in the URL. This type of method is common in RESTful APIs or pseudo-static routing, and may occur when obtaining data from the backend or sending data to the backend. Other existing methods are usually manual tagging, usually processing the entire URL as a complete variable. For example, when the website uses a RESTful API or the address is pseudo-static, sometimes the parameters are submitted as part of the access path and cannot be as clear as a form. Know which part is the parameter. At the same time, when identifying request parameters, existing technologies usually give up parsing the variable part of the address, or use manual definition and configuration to mark them.

Contents of the invention

In view of this, the purpose of the present invention is to overcome the shortcomings of the existing technology and provide a method, device, equipment and storage medium for identifying variables in a URL.

In order to achieve the above objectives, the present invention adopts the following technical solutions:

A way to identify variables in a URL, including:

Obtain the access path data of the website to be identified;

Preprocess the access path data to obtain a path relationship data set and a hierarchical threshold set;

Identify quantitative data and suspected variable data within the access path data according to the path relationship data set and the hierarchical threshold set;

Verify the suspected variable data to obtain variable data;

The quantitative data and the variable data are integrated and output.

Optionally, preprocess the access path data to obtain a path relationship data set and a hierarchical threshold set, including:

Divide the access path data according to preset rules to obtain multiple path segment nodes;

A node data structure is generated according to the path segment node combined with the path relationship; the path relationship is obtained by the path segment node according to the access path data;

The path relationship data set and the hierarchical threshold set are obtained according to the node data structure.

Optionally, the path relationship data set includes: the number of child nodes, the number of references, and the number of sibling nodes;

The hierarchical threshold set includes: child node number threshold, referenced number threshold and reverse reference coefficient threshold;

Obtaining the path relationship data set and the hierarchical threshold set according to the node data structure include:

Count the number of child nodes, parent nodes and sibling nodes of each path segment node;

Calculate the number of references and the average number of references of the path segment node according to the number of parent nodes;

Calculate the weighting coefficient of the number of child nodes at each level in the node data structure according to the number of child nodes;

Calculate the child node number threshold according to the child node number weighting coefficient;

Calculate the citation number weighting coefficient of nodes at each level according to the average number of citations;

Calculate the citation count threshold according to the citation count weighting coefficient;

Calculate the back reference coefficient according to the number of parent nodes and the number of sibling nodes;

Calculate the mean value of the back citation coefficient according to the back citation coefficient;

The back-citation coefficient threshold is calculated using the average back-citation coefficient.

Optionally, identifying quantitative data and suspected variable data in the access path data based on the path relationship data set and the hierarchical threshold set includes:

Determine whether the number of child nodes of any of the path segment nodes is greater than the child node number threshold;

If so, determine that the path segment node is quantitative data;

Otherwise, determine whether the number of citations is greater than the threshold number of citations;

If so, determine that the path segment node is quantitative data;

Otherwise, determine whether the number of references is greater than the number of sibling nodes;

If so, determine that the path segment node is quantitative data;

Otherwise, determine whether the back reference coefficient threshold divided by the number of child nodes is greater than the threshold of the number of child nodes;

If so, determine that the path segment node is quantitative data;

Otherwise, it is determined that the path segment node is the suspected variable data.

Optional, also includes:

The suspected variable data is marked with preset wildcards and combined with the quantitative data to generate a tree path structure.

Optionally, the suspected variable data is verified to obtain variable data, including:

Traverse the subordinate nodes of the suspected variable data and the subordinate nodes of the quantitative data in the tree path structure;

Determine whether there is a quantitative node that satisfies the first preset condition among the lower-level nodes of the quantitative data; the quantitative node is the same as the lower-level node of the suspected variable data;

If it exists, determine that the suspected variable data is quantitative data;

Otherwise, determine whether the suspected variable data has a sibling node that meets the second preset condition; the sibling node is an end node, and the sibling node is quantitative data;

If it exists, determine that the suspected variable data is quantitative data;

Otherwise, it is determined that the suspected variable data is variable data.

Optionally, the preset rule is: use "/" as the dividing point.

A device for identifying variables in a URL, including:

The access path acquisition module is used to obtain the access path data of the website to be identified;

A preprocessing module, used to preprocess the access path data to obtain a path relationship data set and a hierarchical threshold set;

A quantitative identification module, configured to identify quantitative data and suspected variable data in the access path data according to the path relationship data set and the hierarchical threshold set;

The suspected variable verification module is used to verify the suspected variable data and obtain the variable data;

The result integration output module is used to integrate and output the quantitative data and the variable data.

A device that identifies variables in URLs, including:

A processor, and memory coupled to the processor;

The memory is used to store a computer program, and the computer program is at least used to perform the above-mentioned method of identifying variables in a URL;

The processor is used to call and execute the computer program in the memory.

A storage medium that stores a computer program. When the computer program is executed by a processor, each step of the method for identifying variables in a URL is implemented as described above.

The technical solution provided by this application can include the following beneficial effects:

This application discloses a method for identifying variables in a URL, which includes: obtaining the access path data of the website to be identified; preprocessing the access path data to obtain a path relationship data set and a hierarchical threshold set; based on the path relationship data set and hierarchical threshold value Collect and identify quantitative data and suspected variable data in the access path data; verify the suspected variable data to obtain variable data; integrate and output the quantitative data and variable data. In the above method, the website access path data is preprocessed, and then the quantitative data and suspected variable data in the path are identified, and then the suspected variable data is verified to determine the final variable data, and then the quantitative data and variable data are integrated to obtain the final identification results. The above method can automatically analyze and calculate the variable data part in the access path through access data, which greatly improves the efficiency of variable identification.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without exerting creative efforts.

Figure 1 is a flow chart of a method for identifying variables in a URL provided by an embodiment of the present invention;

Figure 2 is a flow chart of a preprocessing method provided by an embodiment of the present invention;

Figure 3 is a flow chart of a quantitative data identification method provided by an embodiment of the present invention;

Figure 4 is a flow chart of a method for verifying suspected variables provided by an embodiment of the present invention;

Figure 5 is a module diagram of a device for identifying variables in a URL provided by an embodiment of the present invention;

Figure 6 is a structural diagram of a device for identifying variables in a URL provided by an embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below. Obviously, the described embodiments are only some of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other implementations obtained by those of ordinary skill in the art without any creative work fall within the scope of protection of the present invention.

Figure 1 is a flow chart of a method for identifying variables in a URL provided by an embodiment of the present invention. Referring to Figure 1, a method for identifying variables in a URL includes:

Step 101: Obtain the access path data of the website to be identified. The access path data in this application uses log data.

Step 102: Preprocess the access path data to obtain a path relationship data set and a hierarchical threshold set. In this step, after the access path data that needs to be analyzed is loaded into the system corresponding to this embodiment, the data is preprocessed in advance, and then data that can be used for subsequent identification operations is obtained.

Step 103: Identify the quantitative data and suspected variable data in the access path data according to the path relationship data set and the hierarchical threshold set.

Step 104: Verify the suspected variable data to obtain the variable data.

Step 105: Integrate the quantitative data and variable data and output.

In the above method, by analyzing the access path data, the variable data part and the quantitative data part in the path are identified, and the automatic identification function of the variable part in the network path is realized, which greatly improves the efficiency of variable identification, thereby improving the efficiency of penetration testing. efficiency.

In more detail, on the basis of the above embodiments, this application also discloses step 102, that is, the implementation process of preprocessing access path data to obtain a path relationship data set and a hierarchical threshold set, as follows:

Figure 2 is a flow chart of a preprocessing method provided by an embodiment of the present invention. Referring to Figure 2, the access path data is preprocessed to obtain a path relationship data set and a hierarchical threshold set, including:

Step 201: Divide the access path data according to preset rules to obtain multiple path segment nodes. In this application, "/" is used to divide the path into multiple path segments based on the characteristics of the URL, and the individual path segments are collectively referred to as path segment nodes.

Step 202: Generate a node data structure based on the path segment nodes combined with path relationships; the path relationships are obtained by the path segment nodes based on the access path data. Generate a node data structure for each path segment node according to the original path relationship. When the paths of the original data in the log are /a/b/c, /a/b/e, and /a/d/c, the node The data structure is as follows:

.

Step 203: Obtain the path relationship data set and the hierarchical threshold set according to the node data structure. Among them, the path relationship data set includes: the number of child nodes, the number of citations, and the number of sibling nodes; the hierarchical threshold set includes: the threshold of the number of child nodes, the number of citations, and the backreference coefficient threshold.

The specific process of step 203 is as follows: Count the number of child nodes, number of parent nodes, and number of sibling nodes of each path segment node. For example: In the node data structure mentioned above, the child nodes of b include c and e, so the number of child nodes of b is 2. There are two types of parent nodes of node c: b and d, so the number of parent nodes of c is 2. The sibling node of the e node is c, and the number of sibling nodes is 1. Similarly, the sibling node of c is e, and the number of sibling nodes is 1.

Calculate the number of references and the average number of references of the path segment node according to the number of parent nodes. Taking the node data structure as an example, e is referenced once by b, and the number of references is 1, and c is referenced once by b and d, a total of 2 times, and the number of references is 2. At a depth of level 3, b The average number of citations at the sub-node level of is (1+2)/2=1.5, and the average number of citations at the sub-node level of d is 2/1=2.

Calculate the weighting coefficient of the number of child nodes at each level in the node data structure according to the number of child nodes; calculate the threshold value of the number of child nodes according to the weighting coefficient of the number of child nodes; the threshold value of the number of child nodes is based on the weighting coefficient of the number of child nodes And the degree of dispersion between superimposed data is obtained.

Calculate the citation number weighting coefficient of nodes at each level according to the average number of citations; calculate the citation number threshold according to the citation number weighting coefficient; the citation number weighting coefficient is based on the citation number of each path segment node at this level It is calculated by combining the dispersion between each data.

Calculate the back reference coefficient according to the number of parent nodes and the number of sibling nodes; calculate the average back reference coefficient according to the back reference coefficient; calculate the back reference coefficient threshold using the average back reference coefficient .

It should be noted that the thresholds involved in the above embodiments can be replaced by values obtained by a calculation method of dividing the entire set, such as the mean, median, (maximum + minimum)/2 values, etc. The specific form does not vary. limit. The weighting coefficient can be replaced by a value that expresses the discrete relationship between the entire set of data, such as standard deviation, variance, etc. The specific expression form is not limited.

In more detail, on the basis of the above embodiments, this application also discloses step 103, that is, the implementation process of identifying quantitative data and suspected variable data in the access path data according to the path relationship data set and the hierarchical threshold set, specifically as follows:

Figure 3 is a flow chart of a method for quantitative data identification provided by an embodiment of the present invention. Identifying quantitative data and suspected variable data in the access path data according to the path relationship data set and the hierarchical threshold set includes:

Step 301: Determine whether the number of child nodes of any path segment node is greater than the child node number threshold;

Step 302: If the number of child nodes is greater than the child node number threshold, determine that the path segment node is quantitative data;

Step 303: If the number of child nodes is not greater than the threshold of the number of child nodes, determine whether the number of references is greater than the threshold of the number of references; if so, execute step 302;

Step 304: If the number of citations is not greater than the citation number threshold, determine whether the number of citations is greater than the number of sibling nodes; if so, perform step 302;

Step 305: If the number of references is not greater than the number of sibling nodes, determine whether the reverse reference coefficient threshold divided by the number of child nodes is greater than the number of child nodes threshold; if so, perform step 302;

Step 306: The back reference coefficient threshold divided by the number of child nodes is not greater than the threshold of the number of child nodes, and the path segment node is determined to be the suspected variable data.

Furthermore, based on the above embodiment, the method further includes: using preset wildcards to mark the suspected variable data, and combining the quantitative data to generate a tree path structure. According to the judgment results, the variable nodes are generalized into "&" and merged and output into the data structure. If b and d are judged to be suspected variables in the example, and the other judgments are quantitative, the output tree path structure is:

.

At the same time, further, on the basis of the above embodiments, this application also discloses the implementation process of step 104, which is as follows:

Figure 4 is a flow chart of a method for verifying suspected variables provided by an embodiment of the present invention. Referring to Figure 4, the suspected variable data is verified and the variable data is obtained, including:

Step 401: Traverse the subordinate nodes of the suspected variable data and the subordinate nodes of the quantitative data in the tree path structure.

Step 402: Determine whether there is a quantitative node that satisfies the first preset condition among the lower-level nodes of the quantitative data; the quantitative node is the same as the lower-level node of the suspected variable data. Take the following data structure as an example: The original parent node of e node is b.

Step 403: If it exists, determine that the suspected variable data is quantitative data. f and b, which are identified as quantitative nodes, have the same child node e. At this stage, the variable b will be restored to quantitative data. The corrected output result is: .

Step 404: Otherwise, determine whether the suspected variable data has a sibling node that meets the second preset condition; the sibling node is an end node, and the sibling node is quantitative data. If it exists, execute step 403;

For example: . After quantitative data identification processing, g was identified as a suspected variable, and the data structure is/> .

Since g is an end node and its sibling node h is also an end node and is identified as quantitative, g is reduced to quantitative data at this stage.

Step 405: Otherwise, determine that the suspected variable data is variable data.

In the above embodiment, the variable part in the path can be automatically analyzed and calculated based on the log data. Compared with manual marking, the workload can be reduced and no knowledge of the website is required and no developer assistance is required. The automated calculation can be carried out according to the target being analyzed. Updates are automatically updated, increasing real-time performance. At the same time, this method solves the problem of being unable to effectively identify variables in the URL and provides accurate learning features for machine learning. After identifying the variables, /a/1/c/ and /a/2/c can be merged into /a/?/c, which merges the originally scattered weights together, which helps improve the accuracy of machine learning.

Corresponding to the method for identifying variables in a URL provided by embodiments of the present invention, embodiments of the present invention also provide a device for identifying variables in a URL. See examples below.

Figure 5 is a module diagram of a device for identifying variables in a URL provided by an embodiment of the present invention. A device for identifying variables in a URL, including:

The access path acquisition module 501 is used to obtain the access path data of the website to be identified;

The preprocessing module 502 is used to preprocess the access path data to obtain a path relationship data set and a hierarchical threshold set;

Quantitative identification module 503, configured to identify quantitative data and suspected variable data in the access path data according to the path relationship data set and the hierarchical threshold set;

The suspected variable verification module 504 is used to verify the suspected variable data to obtain variable data;

The result integration output module 505 is used to integrate and output the quantitative data and the variable data.

In more detail, the preprocessing module 502 is specifically configured to: segment the access path data according to preset rules to obtain multiple path segment nodes; generate a node data structure based on the path segment nodes combined with path relationships; the path relationships It is obtained from the path segment node according to the access path data; the path relationship data set and the hierarchical threshold set are obtained according to the node data structure.

The quantitative identification module 503 is specifically used to: determine whether the number of child nodes of any of the path segment nodes is greater than the child node number threshold; if so, determine that the path segment node is quantitative data; otherwise, determine that the referenced Whether the number is greater than the cited number threshold; if so, determine that the path segment node is quantitative data; otherwise, determine whether the cited number is greater than the number of sibling nodes; if so, determine that the path segment node is quantitative data; Otherwise, it is determined whether the back reference coefficient threshold divided by the number of child nodes is greater than the threshold of the number of child nodes; if so, it is determined that the path segment node is quantitative data; otherwise, it is determined that the path segment node is the suspected variable. data.

The suspected variable verification module 504 is specifically configured to: traverse the subordinate nodes of the suspected variable data and the subordinate nodes of the quantitative data in the tree path structure; determine whether there is a subordinate node of the quantitative data that satisfies the first requirement. A quantitative node with preset conditions; the quantitative node is the same as the subordinate node of the suspected variable data; if it exists, determine that the suspected variable data is quantitative data; otherwise, determine whether the suspected variable data exists and satisfies the second preset The sibling node of the condition; the sibling node is an end node, and the sibling node is quantitative data; if it exists, it is determined that the suspected variable data is quantitative data; otherwise, it is determined that the suspected variable data is variable data.

Furthermore, based on the above embodiments, the device in this application also includes:

A wildcard marking module is used to mark the suspected variable data using preset wildcards and combine it with the quantitative data to generate a tree path structure.

The above identification device can be used to automatically identify variables in the access path, which greatly improves the efficiency of variable identification. At the same time, the identified scattered variables are integrated to help improve the accuracy of machine learning.

In order to more clearly introduce the hardware system that implements the embodiment of the present invention, corresponding to the method for identifying variables in the URL provided by the embodiment of the present invention, the embodiment of the present invention also provides a device for identifying variables in the URL. See examples below.

Figure 6 is a structural diagram of a device for identifying variables in a URL provided by an embodiment of the present invention. See Figure 6, a device that identifies variables in a URL, including:

Processor 601, and memory 602 connected to the processor 601;

The memory 602 is used to store a computer program, which is at least used to perform the above-mentioned method of identifying variables in a URL;

The processor 601 is used to call and execute the computer program in the memory 602 .

Based on the above embodiments, a storage medium is also disclosed. The storage medium stores a computer program. When the computer program is executed by a processor, each step of the method for identifying variables in a URL is implemented as described above.

It can be understood that the same or similar parts in the above-mentioned embodiments can be referred to each other, and the content that is not described in detail in some embodiments can be referred to the same or similar content in other embodiments.

It should be noted that in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise stated, the meaning of "plurality" means at least two.

Any process or method descriptions in flowcharts or otherwise described herein may be understood to represent modules, segments, or portions of code that include one or more executable instructions for implementing the specified logical functions or steps of the process. , and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed out of the order shown or discussed, including in a substantially simultaneous manner or in the reverse order, depending on the functionality involved, which shall It should be understood by those skilled in the art to which embodiments of the present invention belong.

It should be understood that various parts of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if it is implemented in hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: a logic gate circuit with a logic gate circuit for implementing a logic function on a data signal. Discrete logic circuits, application specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

Those of ordinary skill in the art can understand that all or part of the steps involved in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. The program can be stored in a computer-readable storage medium. When executed, one of the steps of the method embodiment or a combination thereof is included.

In addition, each functional unit in various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software function modules. If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

The storage media mentioned above can be read-only memory, magnetic disks or optical disks, etc.

In the description of this specification, reference to the terms "one embodiment," "some embodiments," "an example," "specific examples," or "some examples" or the like means that specific features are described in connection with the embodiment or example. , structures, materials or features are included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Although the embodiments of the present invention have been shown and described above, it can be understood that the above-mentioned embodiments are illustrative and should not be construed as limitations of the present invention. Those of ordinary skill in the art can make modifications to the above-mentioned embodiments within the scope of the present invention. The embodiments are subject to changes, modifications, substitutions and variations.

Claims (8)

1. A method of identifying a variable in a URL, comprising:

acquiring access path data of a website to be identified;

preprocessing the access path data to obtain a path relation data set and a hierarchical threshold set;

identifying quantitative data and suspected variable data in the access path data according to the path relation data set and the hierarchical threshold set;

verifying the suspected variable data to obtain variable data;

integrating and outputting the quantitative data and the variable data;

the preprocessing the access path data to obtain a path relation data set and a hierarchical threshold set, including:

dividing the access path data according to a preset rule to obtain a plurality of path segment nodes;

generating a node data structure according to the path segment nodes and the path relation; the path relation is obtained by the path segment node according to the access path data;

obtaining the path relation data set and the hierarchical threshold set according to the node data structure;

the path relation dataset comprises: number of child nodes, number of referenced and number of sibling nodes;

the hierarchical threshold set includes: a child node number threshold, a referenced number threshold, and a reverse reference coefficient threshold;

the obtaining the path relation data set and the hierarchical threshold set according to the node data structure includes:

counting the number of child nodes, the number of father nodes and the number of brother nodes of each path segment node;

calculating the referenced number and the mean value of the referenced number of the path segment nodes according to the parent node number;

calculating the weight coefficient of the number of the child nodes of each level in the node data structure according to the number of the child nodes;

calculating the threshold value of the number of the child nodes according to the weighting coefficient of the number of the child nodes;

calculating the weighted coefficient of the referenced number of each level node according to the referenced number average value;

calculating the threshold value of the referenced number according to the weighted coefficient of the referenced number;

calculating an inverse reference coefficient according to the number of father nodes and the number of brother nodes;

calculating the average value of the reverse reference coefficients according to the reverse reference coefficients;

and calculating the threshold value of the back reference coefficient by using the average value of the back reference coefficient.

2. The method of claim 1, wherein the identifying quantitative data and suspected variable data within the access path data from the path relationship data set and the hierarchical threshold set comprises:

judging whether the number of the child nodes of any path segment node is larger than the threshold value of the number of the child nodes;

if the number of the child nodes is larger than the threshold value of the number of the child nodes, judging that the path segment nodes are quantitative data;

otherwise, judging whether the referenced number is larger than the referenced number threshold;

if the referenced number is larger than the referenced number threshold, judging that the path segment node is quantitative data;

otherwise, judging whether the referenced number is larger than the number of the brother nodes;

if the number of the cited nodes is larger than the number of the brother nodes, judging that the path segment nodes are quantitative data;

otherwise, judging whether the inverse reference coefficient threshold divided by the number of the child nodes is larger than the threshold of the number of the child nodes or not;

if the inverse reference coefficient threshold divided by the number of the child nodes is larger than the threshold of the number of the child nodes, judging that the path segment nodes are quantitative data;

otherwise, judging the path segment node as the suspected variable data.

3. The method as recited in claim 1, further comprising:

and marking the suspected variable data by using a preset wildcard, and generating a tree-type path structure by combining the quantitative data.

4. A method according to claim 3, wherein the verifying the suspected variable data to obtain variable data comprises:

traversing the lower nodes of the suspected variable data and the lower nodes of the quantitative data in the tree path structure;

judging whether a quantitative node meeting a first preset condition exists in lower nodes of the quantitative data; the quantitative node is the same as a subordinate node of the suspected variable data;

if a quantitative node meeting a first preset condition exists in the lower nodes of the quantitative data, judging that the suspected variable data is quantitative data;

otherwise, judging whether the suspected variable data has brother nodes meeting a second preset condition or not; the sibling node is an end node, and the sibling node is quantitative data;

if the suspected variable data have brother nodes meeting a second preset condition, judging the suspected variable data to be quantitative data;

otherwise, judging the suspected variable data as variable data.

5. The method according to claim 1, wherein the preset rule is: with "/" as the segmentation point.

6. An apparatus for identifying a variable in a URL, comprising:

the access path acquisition module is used for acquiring access path data of the website to be identified;

the preprocessing module is used for preprocessing the access path data to obtain a path relation data set and a hierarchical threshold set;

the preprocessing the access path data to obtain a path relation data set and a hierarchical threshold set, including:

dividing the access path data according to a preset rule to obtain a plurality of path segment nodes;

generating a node data structure according to the path segment nodes and the path relation; the path relation is obtained by the path segment node according to the access path data;

obtaining the path relation data set and the hierarchical threshold set according to the node data structure;

the path relation dataset comprises: number of child nodes, number of referenced and number of sibling nodes;

the hierarchical threshold set includes: a child node number threshold, a referenced number threshold, and a reverse reference coefficient threshold;

the obtaining the path relation data set and the hierarchical threshold set according to the node data structure includes:

counting the number of child nodes, the number of father nodes and the number of brother nodes of each path segment node;

calculating the referenced number and the mean value of the referenced number of the path segment nodes according to the parent node number;

calculating the weight coefficient of the number of the child nodes of each level in the node data structure according to the number of the child nodes;

calculating the threshold value of the number of the child nodes according to the weighting coefficient of the number of the child nodes;

calculating the weighted coefficient of the referenced number of each level node according to the referenced number average value;

calculating the threshold value of the referenced number according to the weighted coefficient of the referenced number;

calculating an inverse reference coefficient according to the number of father nodes and the number of brother nodes;

calculating the average value of the reverse reference coefficients according to the reverse reference coefficients;

calculating the threshold value of the back reference coefficient by utilizing the average value of the back reference coefficient;

the quantitative identification module is used for identifying quantitative data and suspected variable data in the access path data according to the path relation data set and the hierarchical threshold set;

the suspected variable verification module is used for verifying the suspected variable data to obtain variable data;

and the result integration output module is used for integrating and outputting the quantitative data and the variable data.

7. An apparatus for identifying a variable in a URL, comprising:

a processor, and a memory coupled to the processor;

the memory is used for storing a computer program at least for executing the method of identifying variables in a URL according to any one of claims 1-5;

the processor is configured to invoke and execute the computer program in the memory.

8. A storage medium storing a computer program which, when executed by a processor, performs the steps of the method of identifying a variable in a URL as claimed in any one of claims 1 to 5.