Method, device and medium for extracting fingerprint features of WEB home page

Abstract

The present application discloses a method, device, and medium for extracting fingerprint features of a web homepage, which relates to the field of front-end technology. The method obtains the request response body and response header information of a web application address, obtains the same row in a test web application based on the request response body and response header information, obtains the credibility of the row, combines the features of the row based on the credibility, forms feature rules, and outputs the feature rules. It can be seen that this method can quickly extract the fingerprint rules of the web application homepage by obtaining the same row from at least two web application addresses, avoiding the problem of not being able to identify the feature rules when the keyword is modified or deleted by the user, and avoiding the situation where the keyword does not exist on the homepage, avoiding the situation of not being able to identify, making feature extraction simpler and faster.

Description

Technical Field

[0001] The present application relates to the field of front-end technology, and in particular to a method, device and medium for extracting fingerprint features of a WEB homepage. Background Art

[0002] When conducting a network penetration test, information collection is the most important step. Website fingerprinting tools can match the returned packet content with a fingerprint signature library. A successful match can determine the website's fingerprint. The core of website fingerprinting tools is the fingerprint signature rule library, but collecting and extracting fingerprint signatures is a complex task. Currently, fingerprint signature extraction methods pre-define keywords for web applications, such as the product's Chinese and English names, the manufacturer's Chinese and English names, and the website address. These keywords are then searched for on the homepage and static files. If any of these keywords are found, they are identified as fingerprint signatures.

[0003] Because other users may modify or delete keywords when deploying them to their own environments, this can lead to problems with recognition when using this rule. Furthermore, these keywords often do not appear in homepage content, making fingerprint feature extraction even more cumbersome. Given these technical issues, finding a fingerprint feature extraction method is an urgent problem for those skilled in the art. Summary of the Invention

[0004] The purpose of this application is to provide a method, device and medium for extracting fingerprint features of WEB homepages.

[0005] To solve the above technical problems, the present application provides a method for extracting fingerprint features of a web homepage, comprising:

[0006] Obtaining a request response body and response header information of a web application address, wherein the web application address is at least two;

[0007] Obtain the same row in the test WEB application according to the request response body and the response header information;

[0008] Get the credibility of the row;

[0009] Combining features of the rows according to the credibility and forming feature rules;

[0010] The feature rules are output.

[0011] Preferably, the outputting the feature rule includes:

[0012] Retrieving the feature rule to obtain a set of titles and search results corresponding to the feature rule;

[0013] Determining the similarity of the titles of each group according to the search results;

[0014] Determining the priority of the feature rule according to the similarity;

[0015] The feature rules are output according to the priorities.

[0016] Preferably, obtaining the credibility of the row includes:

[0017] Acquire a feature library, wherein the feature library is pre-set according to preset features;

[0018] Determining whether a weight factor for characterizing the credibility of the row exists in the row;

[0019] If so, the credibility corresponding to the weight factor in the feature library is used as the credibility of the row.

[0020] Preferably, combining features of the rows according to the credibility and forming feature rules includes:

[0021] If the credibility is within a first preset range, taking the row corresponding to the credibility as a feature rule;

[0022] If the credibility is within a second preset range, combining two rows corresponding to the credibility into one feature rule according to the logic;

[0023] If the credibility is within a third preset range, all rows corresponding to the credibility within the third preset range are combined into one feature rule;

[0024] The lower limit value of the first preset range is equal to the upper limit value of the second preset range, and the lower limit value of the second preset range is equal to the upper limit value of the third preset range.

[0025] Preferably, determining the priority of the feature rule according to the similarity includes:

[0026] determining whether there is a similarity within a fourth preset range;

[0027] If yes, obtaining the number of titles with similarities within the fourth preset range and the number of titles with similarities outside the fourth preset range, and determining a ratio between the two;

[0028] If the ratio is within a fifth preset range, the feature rule corresponding to the title is determined to be a credible feature rule;

[0029] If the ratio is within the sixth preset range or the seventh preset range, the feature rule corresponding to the title is determined to be a generally credible feature rule;

[0030] Among them, the upper limit value of the fourth preset range is equal to the upper limit value of the first preset range, and the lower limit value of the fourth preset range is greater than the lower limit value of the first preset range; the lower limit value of the fifth preset range is equal to the lower limit value of the first preset range, and the upper limit value of the fifth preset range is greater than the upper limit value of the fourth preset range; the upper limit value of the sixth preset range is equal to the lower limit value of the fifth preset range, and the lower limit value of the sixth preset range is greater than the upper limit value of the second preset range; the lower limit value of the seventh preset range is equal to the upper limit value of the fifth preset range.

[0031] Preferably, if there is no similarity within the fourth preset range, the method further includes:

[0032] Obtain the number of titles corresponding to similarities within the seventh preset range and the number of titles corresponding to similarities outside the seventh preset range, and determine the ratio between the two. If the ratio between the two is within the fifth preset range, then the feature rules corresponding to the titles are determined to be credible feature rules; if the ratio between the two is outside the fifth preset range, then the feature rules corresponding to the titles are determined to be uncredible feature rules.

[0033] Preferably, after obtaining the credibility of the row, the method further includes:

[0034] Delete rows with confidence levels below a preset threshold.

[0035] To solve the above technical problems, the present application also provides a device for extracting fingerprint features of a web homepage, comprising:

[0036] A first acquisition module is used to obtain the request response body and response header information of the WEB application address, wherein the WEB application address is at least two;

[0037] A second acquisition module is used to acquire the same row in the test WEB application according to the request response body and the response header information;

[0038] A third acquisition module, configured to acquire the credibility of the row;

[0039] a combining module, configured to combine features of the rows according to the credibility and form feature rules;

[0040] An output module is used to output the feature rules.

[0041] In order to solve the above technical problems, the present application also provides a device for extracting fingerprint features of a WEB homepage, comprising a memory for storing a computer program;

[0042] The processor is configured to implement the steps of the method for extracting fingerprint features of a web homepage as described above when executing the computer program.

[0043] To solve the above technical problems, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for extracting fingerprint features of a WEB homepage as described above are implemented.

[0044] The present application provides a method for extracting fingerprint features of a web homepage. The method obtains the request response body and response header information of a web application address, obtains the same rows in a test web application based on the request response body and response header information, obtains the credibility of the rows, combines the features of the rows based on the credibility, forms feature rules, and outputs the feature rules. As can be seen, this method can quickly extract the fingerprint rules of the web application homepage by obtaining the same rows from at least two web application addresses, avoiding the problem of not being able to recognize the feature rules when the keywords are modified or deleted by the user, and also avoids the situation where the keywords do not exist on the homepage, thus avoiding the occurrence of unrecognized situations, making feature extraction simpler and faster.

[0045] On this basis, the present application also provides a device and medium for extracting fingerprint features of a WEB homepage, which has the same beneficial effects as the method for extracting fingerprint features of a WEB homepage. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0047] Figure 1 A flowchart of a method for extracting fingerprint features of a web homepage provided in an embodiment of the present application;

[0048] Figure 2 A flowchart of another method for extracting fingerprint features of a web homepage provided in an embodiment of the present application;

[0049] Figure 3 A structural diagram of a device for extracting fingerprint features of a web homepage provided in an embodiment of the present application;

[0050] Figure 4 This is a structural diagram of a device for extracting fingerprint features of a web homepage provided in another embodiment of the present application. DETAILED DESCRIPTION

[0051] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0052] The core of this application is to provide a method, device and medium for extracting fingerprint features of WEB homepages.

[0053] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0054] Figure 1 This is a flowchart of a method for extracting fingerprint features of a web homepage provided in an embodiment of the present application, such as Figure 1 As shown, the method for extracting fingerprint features of a WEB homepage includes the following steps.

[0055] S10: Obtain the request response body and response header information of the WEB application address.

[0056] S11: Get the same line in the test WEB application based on the request response body and response header information.

[0057] S12: Get the credibility of the row.

[0058] S13: Combine the features of the rows according to the credibility and form feature rules.

[0059] S14: Output feature rules.

[0060] It is understandable that when conducting a network penetration test, information collection is the most important step. Website fingerprint recognition tools can match the return package content with the fingerprint feature library. If the match is successful, the fingerprint of the website can be determined. The core of the website fingerprint recognition tool is the fingerprint feature rule library, but the collection and extraction of fingerprint features is a very tedious task. The current method of extracting fingerprint features is to set the keywords of the WEB application in advance, such as the Chinese and English names of the product, the Chinese and English names of the manufacturer, the website address, etc., and search the homepage, static files, etc. If they exist, they are determined to be fingerprint features. Other users will modify or delete the keywords when deploying to their own environment, which leads to the problem of non-recognition when using the rule. On the other hand, these keywords will not appear in the homepage content in most cases, and the extraction of fingerprint recognition features will be more tedious at this time. Therefore, a method for extracting fingerprint features of the homepage is proposed.

[0061] As described in step S10, the request response body and response header information of the WEB application address are obtained, wherein there are at least two WEB application addresses. This embodiment does not limit the number of WEB application addresses, and it only needs to be greater than or equal to two. By selecting at least two test WEB application addresses, it is not necessary to pre-set keywords, and feature rules can be obtained through the same lines, which reduces the tediousness of feature extraction, thereby avoiding the situation where the keywords do not exist in the homepage, and also avoids the situation where the keywords are modified or deleted and cannot be identified after feature extraction. In addition, if the homepage response code is a 302 jump, the content obtained is the content after the jump. If the response code is 200 and the homepage content is small, and there is a JS jump link, the homepage content is also the content after the connection jump. If the homepage response code is other cases, the content of the homepage is obtained.

[0062] In addition, as mentioned in step S11, the same rows in the test WEB application are obtained based on the request response body and response header information. Since the number of WEB application addresses is greater than two, the same rows may exist in the test WEB application. Specifically, if data A and data B both contain data a, then data a is the same row obtained.

[0063] In step S12, it is mentioned that the credibility of the row is obtained. This embodiment does not limit how to obtain the credibility of the row. A database can be pre-set. If the information contained in the currently obtained row is the same as the information contained in the database, the credibility of the row can be determined based on the same information. This embodiment does not limit this. In step S13, it is mentioned that the rows are characterized and combined according to the credibility and feature rules are formed. This embodiment does not limit this either. It can be based on the range of credibility. For example, if the credibility is 90%, the row corresponding to the credibility can be directly used as a feature rule. If the credibility is below 90%, every two rows can be combined into a feature rule. This embodiment does not limit this. It only needs to meet the requirement of combining the features of the rows according to the credibility and forming the feature rules. In step S13, it is mentioned that the feature rules are output. This embodiment does not limit how to output the feature rules. The feature rules can be further verified and the verified feature rules can be output, or the feature rules and the number of feature rules can be output together.

[0064] This embodiment provides a method for extracting fingerprint features from a web homepage. The method obtains the request response body and response header information of a web application address, retrieves identical rows in a test web application based on the request response body and response header information, obtains the row's credibility, combines the rows' features based on the credibility, forms feature rules, and outputs the feature rules. As can be seen, this method can quickly extract the fingerprint rules for the web application homepage by using at least two web application addresses and obtaining identical rows. This avoids the problem of feature rules not being recognized when keywords are modified or deleted by the user, and also avoids situations where keywords do not exist on the homepage, thus preventing recognition failures. This makes feature extraction simpler and faster.

[0065] Based on the above embodiment, how to output feature rules is described. The specific steps of outputting feature rules are as follows.

[0066] Retrieve the feature rule to obtain a set of titles and retrieval results corresponding to the feature rule;

[0067] Determine the similarity of the titles of each group based on the search results;

[0068] Determine the priority of feature rules based on similarity;

[0069] Output feature rules according to priority.

[0070] On this basis, the feature rules are searched to obtain a set of titles corresponding to the feature rules and search results. This embodiment provides a preferred search method, which is to search through a cyberspace search API. The titles corresponding to the feature rules can be understood as, for example, if the feature rule is "mobile phone", then the corresponding titles can be various mobile phone brands. The similarity of the titles of each group is determined based on the search results. For example, if two feature rules are mobile phones and tablets, then the corresponding titles may be similar, both of which are mobile phone brands. The similarity of the titles can be determined based on the number of similar titles. When there are multiple feature rules, the similarity can be determined. The higher the similarity, the higher the priority. Finally, the feature rules can be output based on the priority.

[0071] The method for outputting feature rules provided in this embodiment retrieves feature rules to obtain a set of titles corresponding to the feature rules and search results, determines the similarity of titles in each group based on the search results, prioritizes the feature rules based on the similarity, and outputs the feature rules based on the priority. As can be seen, this method allows for setting the priority of feature rules, allowing users to clearly understand the importance of feature rules when outputting them.

[0072] Based on the above embodiment, how to obtain the credibility of a row is described. The specific steps for obtaining the credibility of a row are as follows.

[0073] Obtain a feature library, which is pre-set based on preset features;

[0074] Determine whether there is a weight factor in the row that is used to characterize the credibility of the row;

[0075] If so, the credibility of the corresponding weight factor in the feature library is used as the credibility of the row.

[0076] This embodiment does not limit the number of feature libraries; it can be one or more. If there are multiple feature libraries, the feature libraries can be specifically divided into an important tag library, an important keyword feature library, an unimportant tag library, and an exclusion tag feature library. It is understood that the credibility of the important tag library is above 60%, the credibility of the important keyword library is above 80%, and the credibility of the unimportant tag library is below 50%. The exclusion tag feature library means that if a row contains one or more of the exclusion tag libraries, the row is untrustworthy and can be directly excluded.

[0077] Specifically, in the important tag library, <title>” represents a credibility of 80%, "<meta name” represents a credibility of 60%, "<content” represents a credibility of 70%, etc. It can be understood that when a tag representing credibility appears in a line, the credibility of the line can be set according to the credibility corresponding to the tag. When two or more tags appear in a line, the lowest credibility is selected as the credibility of the line. In addition, in the important keyword feature library, "cms” represents a credibility of 90%, "system” represents a credibility of 95%, "power by” represents a credibility of 99%, etc. In the unimportant tag library, "< / title> " represents a credibility of 30%, " represents a credibility of 40%, " represents a credibility of 20%, etc. The label feature library mainly includes the following labels, <a href＝" ">”"<! --footer end-->”"<! --home-->”, etc. In other words, the credibility of the row is set by comparing the label in the row with the label in the feature library.

[0078] The method for obtaining row credibility provided in this embodiment obtains a feature library that is pre-configured based on preset features. The method then determines whether a weight factor representing the row credibility exists in the row. If so, the credibility corresponding to the weight factor in the feature library is used as the row credibility. This method reduces the difficulty of setting credibility by comparing the label in the feature library with the label in the row. If the labels are identical, the row credibility is set based on the label's credibility.

[0079] Based on the above embodiment, how to combine features of rows according to credibility to form feature rules is described. The specific steps are as follows.

[0080] If the credibility is within the first preset range, the row corresponding to the credibility is used as a feature rule;

[0081] If the credibility is within the second preset range, the rows corresponding to the credibility of the logical pair are combined into a feature rule in pairs;

[0082] If the credibility is within the third preset range, all rows corresponding to the credibility within the third preset range are combined into one feature rule;

[0083] The lower limit value of the first preset range is equal to the upper limit value of the second preset range, and the lower limit value of the second preset range is equal to the upper limit value of the third preset range.

[0084] This embodiment does not limit the first preset range, the second preset range, and the third preset range. As a preferred implementation, the first preset range is selected to be above 90%, the second preset range is between 60% and 90%, and the third preset range is below 60%. It can be understood that there are many rows, and therefore many credibility levels. The rows corresponding to the credibility levels in the first preset range are used as a feature rule. That is, if there are three credibility levels in the first preset range, the rows corresponding to these three credibility levels are used as three feature rules. In addition, the rows corresponding to the credibility levels in the second preset range are combined into a feature rule in pairs according to the AND logic. Specifically, if there are four rows with credibility levels in the second preset range, and they are a, b, c, and d, respectively, then every two of them form a feature rule, that is, ab, ac, ad, bc, bd, and cd.

[0085] If there is a credibility within the third preset range, all rows corresponding to the credibility within the third preset range are combined into one feature rule. That is, no matter how many rows have credibility within the third preset range, only one feature rule will be formed in the end.

[0086] The method provided in this embodiment combines features of rows based on credibility to form a feature rule. If the credibility is within a first preset range, the rows corresponding to the credibility are used as a feature rule. If the credibility is within a second preset range, the rows corresponding to the logical credibility are combined into a feature rule based on two pairs. If the credibility is within a third preset range, all rows corresponding to the credibility within the third preset range are combined into a feature rule. It can be seen that this method combines feature rules based on the level of credibility, making the method of determining feature rules simpler.

[0087] Based on the above embodiment, how to determine the priority of feature rules according to similarity is described. The specific steps of determining the priority of feature rules according to similarity are as follows.

[0088] determining whether there is a similarity within a fourth preset range;

[0089] If so, obtaining the number of titles corresponding to the similarity within the fourth preset range and the number of titles corresponding to the similarity outside the fourth preset range, and determining a ratio between the two;

[0090] If the ratio is within the fifth preset range, the feature rule corresponding to the title is determined to be a credible feature rule;

[0091] If the ratio is within the sixth preset range or the seventh preset range, the feature rule corresponding to the title is determined to be a generally credible feature rule;

[0092] Among them, the upper limit value of the fourth preset range is equal to the upper limit value of the first preset range, and the lower limit value of the fourth preset range is greater than the lower limit value of the first preset range; the lower limit value of the fifth preset range is equal to the lower limit value of the first preset range, and the upper limit value of the fifth preset range is greater than the upper limit value of the fourth preset range; the upper limit value of the sixth preset range is equal to the lower limit value of the fifth preset range, and the lower limit value of the sixth preset range is greater than the upper limit value of the second preset range; the lower limit value of the seventh preset range is equal to the upper limit value of the fifth preset range.

[0093] This embodiment does not limit the specific ranges of the fourth preset range, the fifth preset range, the sixth preset range, and the seventh preset range. However, as a preferred embodiment, the fourth preset range is set to be greater than 95%, the fifth preset range is set to be 0.9-1.1, the sixth preset range is set to be 0.8-0.9, and the seventh preset range is set to be 1.1-1.2. This embodiment does not limit how to obtain the similarity of the titles. It is determined whether there is a similarity within the fourth preset range. If so, the number of titles corresponding to the similarity within the fourth preset range and the number of titles corresponding to the similarity outside the fourth preset range are obtained, and the ratio of the two is determined. When the ratio is between 0.9-1.1, the feature rule corresponding to the title is determined to be a credible feature rule. When the ratio is between 0.8-0.9 or 1.1-1.2, the feature rule corresponding to the title is determined to be a generally credible rule. When it is within the remaining ranges, the feature rule corresponding to the title is determined to be an untrustworthy rule.

[0094] The method for determining the priority of feature rules according to similarity provided in this embodiment is by judging whether there is a similarity within a fourth preset range;

[0095] If so, obtain the number of titles with similarities that are within the fourth preset range and the number of titles with similarities that are outside the fourth preset range, and determine the ratio between the two; if the ratio is within the fifth preset range, then the feature rule corresponding to the title is determined to be a credible feature rule; if the ratio is within the sixth preset range or the seventh preset range, then the feature rule corresponding to the title is determined to be a generally credible feature rule. It can be seen that this method determines whether the feature rule corresponding to the current title is credible through similarity and quantity, and sets a priority for the feature rule. This is equivalent to verifying whether the extracted feature rule is reliable. If it is unreliable, it is determined to be untrustworthy. This method improves the accuracy of feature rule extraction.

[0096] In a specific embodiment, when the credibility of a row is too low, the current row can be determined to be untrustworthy. Considering this situation, this embodiment proposes another method for extracting fingerprint features of a web homepage. Figure 2 This is a flow chart of another method for extracting fingerprint features of a web homepage provided in an embodiment of the present application, such as Figure 2 As shown, based on step S12, the following further includes:

[0097] S15: Delete rows whose credibility is lower than a preset threshold.

[0098] It is understood that when the credibility of a row is lower than a preset threshold, the behavior can be determined to be untrustworthy and the row can be directly deleted. This embodiment does not limit the value of the preset threshold, and can be 50% or 30%. The preset threshold can be selected according to specific implementation conditions.

[0099] This embodiment provides that rows with a credibility lower than a preset threshold are deleted, thereby avoiding the subsequent extraction of features of the row and making the extraction result more accurate.

[0100] On the basis of the above embodiment, if there is no similarity within the fourth preset range, the number of titles corresponding to the similarity within the seventh preset range and the number of titles corresponding to the similarity outside the seventh preset range are obtained, and the ratio of the two is determined. If the ratio of the two is within the fifth preset range, the feature rule corresponding to the title is determined to be a credible feature rule; if the ratio of the two is outside the fifth preset range, the feature rule corresponding to the title is determined to be an untrustworthy feature rule.

[0101] In the above embodiments, a method for extracting fingerprint features from a web homepage is described in detail. This application also provides corresponding embodiments of a device for extracting fingerprint features from a web homepage. It should be noted that this application describes the embodiments of the device from two perspectives: one based on functional modules and the other based on hardware.

[0102] Figure 3 The embodiment of the present application provides a device for extracting fingerprint features of a web homepage, such as Figure 3 As shown, the device for extracting fingerprint features of a WEB homepage includes:

[0103] A first acquisition module 15 is configured to acquire a request response body and response header information of a WEB application address, wherein there are at least two WEB application addresses;

[0104] The second acquisition module 16 is used to obtain the same line in the test WEB application according to the request response body and response header information;

[0105] The third acquisition module 17 is used to obtain the credibility of the row;

[0106] A combination module 18, configured to combine features of rows according to credibility and form feature rules;

[0107] The output module 19 is used to output feature rules.

[0108] The present embodiment provides a device for extracting fingerprint features of a web homepage, comprising a first acquisition module, a second acquisition module, a third acquisition module, a combination module, and an output module, for executing the steps of a method for extracting fingerprint features of a web homepage. The method obtains the request response body and response header information of a web application address, obtains the same row in a test web application based on the request response body and response header information, obtains the credibility of the row, combines the rows based on the credibility, forms a feature rule, and outputs the feature rule. It can be seen that this method can quickly extract the fingerprint rule of the web application homepage by using at least two web application addresses and obtaining the same row, avoiding the problem of not being able to recognize the feature rule when the keyword is modified or deleted by the user, and also avoids the situation where the keyword does not exist on the homepage, avoiding the situation where the keyword cannot be recognized, making feature extraction simpler and faster.

[0109] Figure 4 This is a structural diagram of a device for extracting fingerprint features of a web homepage provided in another embodiment of the present application, such as Figure 4 As shown, the device for extracting fingerprint features of a WEB homepage includes: a memory 20 for storing a computer program;

[0110] The processor 21 is configured to implement the steps of the method for extracting fingerprint features of a web homepage as mentioned in the above embodiment when executing a computer program.

[0111] The device for extracting fingerprint features of a web homepage provided in this embodiment may include, but is not limited to, a smart phone, a tablet computer, a laptop computer, or a desktop computer.

[0112] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of a digital signal processor (DSP), a field programmable gate array (FPGA), and a programmable logic array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a graphics processing unit (GPU), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an artificial intelligence (AI) processor, which is used to process computing operations related to machine learning.

[0113] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory, and non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 20 is at least used to store the following computer program 201, wherein, after the computer program is loaded and executed by the processor 21, it can implement the relevant steps of the method for extracting the fingerprint features of the WEB homepage disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include but is not limited to data of the method for extracting the fingerprint features of the WEB homepage, etc.

[0114] In some embodiments, the device for extracting fingerprint features of a WEB homepage may further include a display screen 22 , an input and output interface 23 , a communication interface 24 , a power supply 25 , and a communication bus 26 .

[0115] Those skilled in the art will understand that Figure 4 The structure shown in the figure does not constitute a limitation on the device for extracting fingerprint features of a WEB homepage, and may include more or fewer components than shown in the figure.

[0116] The device for extracting fingerprint features of a web homepage provided in this embodiment includes a memory and a processor. The memory is used to store a program for extracting fingerprint features of a web homepage, and the processor is used to execute the program for extracting fingerprint features of a web homepage. The device obtains the request response body and response header information of a web application address, obtains the same row in a test web application based on the request response body and response header information, obtains the credibility of the row, combines the rows based on the credibility, forms a feature rule, and outputs the feature rule. It can be seen that this method can quickly extract the fingerprint rule of the web application homepage by obtaining the same row through at least two web application addresses, avoids the problem of not being able to identify the feature rule when the keyword is modified or deleted by the user, and avoids the situation where the keyword does not exist in the homepage, avoids the situation where the keyword cannot be identified, and makes feature extraction simpler and faster.

[0117] Finally, the present application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiment.

[0118] It is understandable that if the method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and executes all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0119] This embodiment provides a computer-readable storage medium that stores a program in the computer-readable storage medium, obtains the request response body and response header information of the web application address when executing the program, obtains the same row in the test web application based on the request response body and response header information, obtains the credibility of the row, combines the features of the row based on the credibility and forms a feature rule, and outputs the feature rule. It can be seen that this method can quickly extract the fingerprint rule of the web application homepage by obtaining the same row from at least two web application addresses, avoiding the problem of not being able to recognize the feature rule when the keyword is modified or deleted by the user, and also avoids the situation where the keyword does not exist on the homepage, avoiding the situation of not being able to recognize the keyword, making feature extraction simpler and faster.

[0120] The above is a detailed introduction to the method, device and medium for extracting the fingerprint features of the WEB homepage provided by this application. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to this application, and these improvements and modifications also fall within the scope of protection of the claims of this application.

[0121] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprise," "include," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

Claims

1. A method for extracting fingerprint features of a WEB homepage, characterized in that: include: Obtaining a request response body and response header information of a WEB application address, wherein the WEB application address is at least two; Obtain the same row in the test WEB application according to the request response body and the response header information; Obtaining the credibility of the row; wherein obtaining the credibility of the row includes: obtaining a feature library, the feature library being pre-set according to preset features; determining whether a weight factor for characterizing the credibility of the row exists in the row; and if so, using the credibility corresponding to the weight factor in the feature library as the credibility of the row; Combining features of the rows according to the credibility and forming a feature rule; combining features of the rows according to the credibility and forming a feature rule includes: if the credibility is within a first preset range, taking the rows corresponding to the credibility as one of the feature rules; if the credibility is within a second preset range, combining the rows corresponding to the credibility into one of the feature rules according to logic; if the credibility is within a third preset range, combining all rows corresponding to the credibility within the third preset range into one of the feature rules; wherein the lower limit value of the first preset range is equal to the upper limit value of the second preset range, and the lower limit value of the second preset range is equal to the upper limit value of the third preset range; The feature rules are output.

2. The method for extracting fingerprint features of a WEB homepage according to claim 1, characterized in that: Outputting the feature rules includes: Retrieving the feature rule to obtain a set of titles and search results corresponding to the feature rule; Determining the similarity of the titles of each group according to the search results; Determining the priority of the feature rule according to the similarity; The feature rules are output according to the priorities.

3. The method for extracting fingerprint features of a WEB homepage according to claim 2, characterized in that: Determining the priority of the feature rule according to the similarity includes: determining whether there is a similarity within a fourth preset range; If yes, obtaining the number of titles with similarities within the fourth preset range and the number of titles with similarities outside the fourth preset range, and determining a ratio between the two; If the ratio is within a fifth preset range, the feature rule corresponding to the title is determined to be a credible feature rule; If the ratio is within the sixth preset range or the seventh preset range, the feature rule corresponding to the title is determined to be a generally credible feature rule; Among them, the upper limit value of the fourth preset range is equal to the upper limit value of the first preset range, and the lower limit value of the fourth preset range is greater than the lower limit value of the first preset range; the lower limit value of the fifth preset range is equal to the lower limit value of the first preset range, and the upper limit value of the fifth preset range is greater than the upper limit value of the fourth preset range; the upper limit value of the sixth preset range is equal to the lower limit value of the fifth preset range, and the lower limit value of the sixth preset range is greater than the upper limit value of the second preset range; the lower limit value of the seventh preset range is equal to the upper limit value of the fifth preset range.

4. The method for extracting fingerprint features of a WEB homepage according to claim 3, characterized in that: If the similarity does not fall within the fourth preset range, the method further includes: Obtain the number of titles corresponding to similarities within the seventh preset range and the number of titles corresponding to similarities outside the seventh preset range, and determine the ratio between the two. If the ratio between the two is within the fifth preset range, then the feature rules corresponding to the titles are determined to be credible feature rules; if the ratio between the two is outside the fifth preset range, then the feature rules corresponding to the titles are determined to be uncredible feature rules.

5. The method for extracting fingerprint features of a WEB homepage according to claim 1, characterized in that: After obtaining the credibility of the row, the method further includes: Delete rows with confidence levels below a preset threshold.

6. A device for extracting fingerprint features of a web homepage, characterized in that: include: A first acquisition module is used to obtain the request response body and response header information of the WEB application address, wherein the WEB application address is at least two; A second acquisition module is used to acquire the same row in the test WEB application according to the request response body and the response header information; A third acquisition module is configured to acquire the credibility of the row; wherein acquiring the credibility of the row comprises: acquiring a feature library, the feature library being pre-set according to preset features; determining whether a weight factor for characterizing the credibility of the row exists in the row; and if so, using the credibility corresponding to the weight factor in the feature library as the credibility of the row; A combination module, configured to perform feature combination on the rows according to the credibility and form a feature rule; the feature combination on the rows according to the credibility and forming a feature rule includes: if the credibility is within a first preset range, taking the rows corresponding to the credibility as one of the feature rules; if the credibility is within a second preset range, combining the rows corresponding to the credibility into one of the feature rules according to logic; if the credibility is within a third preset range, combining all rows corresponding to the credibility within the third preset range into one of the feature rules; wherein the lower limit value of the first preset range is equal to the upper limit value of the second preset range, and the lower limit value of the second preset range is equal to the upper limit value of the third preset range; An output module is used to output the feature rules.

7. A device for extracting fingerprint features of a web homepage, characterized in that: including a memory for storing a computer program; A processor, configured to implement the steps of the method for extracting fingerprint features of a web homepage as claimed in any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for extracting fingerprint features of a web homepage according to any one of claims 1 to 5.

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