CN116932964A - Web application API (application program interface) mining method and system based on page state similarity analysis - Google Patents
Web application API (application program interface) mining method and system based on page state similarity analysis Download PDFInfo
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Abstract
The application provides a Web application API (application program interface) mining method, a system, electronic equipment and a medium based on Web page state similarity analysis. The method and the device can comprehensively, accurately and efficiently identify the API of the Web application.
Description
Technical Field
The application relates to the field of network security, in particular to a Web application API (application program interface) mining method and system based on page state similarity analysis.
Background
As Web applications become more and more widely used, their inherent data and functional value makes them targets for attackers. Modern Web applications have rich interactivity, providing users with APIs (application programming interfaces) for various data interactions. Various data interaction APIs provide convenience for users and simultaneously lead Web applications to face greater security threats. Client and server vulnerabilities represented by XSS (cross site scripting attack), SQL injection, and remote code execution make attacks on Web applications increasingly aggressive. From the perspective of security analysts, the more data interaction APIs are found in the Web application, the more likely vulnerabilities are found in the Web application. It is therefore important to identify the APIs of Web applications as comprehensively as possible.
In existing Web application API identification research efforts, one class is based on the user manual of a Web application, and one class is to identify APIs by dynamically traversing Web pages (e.g., crawlers). Research based on Web application user manuals typically uses methods such as rule matching to extract APIs from the user manual or machine learning methods to generate a large number of test cases to guess the possible APIs. These methods have limited ability to identify APIs that are not present in the user manual, and many Web applications do not provide user manuals. By dynamically traversing web pages to identify APIs, limitations based on user manuals may be eliminated. However, pages of modern Web applications are typically highly complex user interfaces implemented by languages such as JavaScript, HTML, CSS. The high complexity of Web pages and the similarity between Web pages present a significant challenge for crawlers to identify Web application APIs. When the existing Web page similarity analysis method is applied to an API recognition scene, the negative influence caused by similar Web pages cannot be effectively eliminated.
Therefore, the existing method cannot efficiently, accurately and comprehensively identify the Web application API, so that a comprehensive, accurate and efficient method capable of accurately analyzing the similarity of the Web page without a user manual is needed to identify the Web application API.
Disclosure of Invention
In order to solve the problems, reduce the negative influence of page similarity on the efficiency and accuracy of Web application API identification, and improve the accuracy and comprehensiveness of Web application API identification, the application provides a method for identifying APIs by dynamically traversing the Web application based on state perception of Web page state similarity analysis, which can effectively improve the quantity, accuracy and efficiency of Web application API identification.
The application discloses a Web application API (application program interface) mining method based on page state similarity analysis, which comprises the following steps:
step S1, acquiring a target Web application webpage, and extracting key elements in the webpage, wherein the key elements comprise: URL links in the web page, forms in the web page, javaScript events in the web page;
s2, constructing a comprehensive webpage model by using the acquired local URL links, the forms and the JavaScript events in the current webpage, representing the state of the webpage, and setting the state as < URL_link, form and JavaScript_events >;
s3, respectively vectorizing three elements in the constructed comprehensive webpage model;
step S4, based on the three vectorized elements, respectively calculating cosine similarity of the same kind of elements contained in different states of the page according to element characteristics of the three vectorized elements;
step S5, respectively setting different weights according to the influence degree of each type of elements on the page state change, and calculating the overall similarity of the current state and the rest state;
and S6, judging whether the overall similarity is smaller than a specified threshold value, if so, obtaining a new page state, and performing API mining under the page state.
According to the method of the first aspect of the application, the URL links comprise absolute URL links and relative URL links, for which data cleansing is set, excluding non-local URLs and resource file URLs.
According to the method of the first aspect of the present application, the obtaining the form includes: extracting key data in the form and describing the form by using a uniform format.
According to the method of the first aspect of the present application, the cosine similarity in step S4 is calculated as follows:
wherein ,X、Yfor the same type element vector in two page states, the vector value, X, of the data representing any type element type in the local URL link, the form and the JavaScript event in two different states i 、Y i Representing the ith component in the element vector of the class, and n represents the number of components of the data vector of the corresponding type element in each page state.
According to the method of the first aspect of the present application, in step S5, different weights are set according to the influence degree of each type of element on the page content change, and the overall similarity between the current page state to be analyzed and the rest of analyzed page states is calculated, where the calculation formula is as follows:
wherein ；
Wherein A is the page state to be analyzed, B is the page state which has been analyzed,、/>、/>related weight coefficients representing three types of elements of URL links in the web page, forms in the web page and JavaScript events in the web page,、/>、/>and representing the results of cosine similarity calculation of URL links in two different webpage states, forms of the webpages and JavaScript events in the webpages respectively.
According to the method of the first aspect of the present application, the step S6 of performing API mining includes: and extracting all URLs conforming to RESTful API, SOAP API and JSON-RPC API specifications in the URL link of the current page state, and GET request URL containing request parameters and all form requests in the page state.
The second aspect of the application discloses a Web application API mining system for realizing page state similarity analysis, which is used for realizing the Web application API mining method based on the page state similarity analysis according to the first aspect.
The third aspect of the application discloses an electronic device, which comprises a memory and a processor, wherein the processor is used for executing a program stored in the memory to realize the Web application API discovery method based on the page state similarity analysis according to the first aspect.
A fourth aspect of the present application discloses a computer-readable storage medium storing a computer program for implementing the Web application API mining method based on page status similarity analysis according to the first aspect.
The Web application API mining method and system based on page state similarity analysis provided by the application mainly realize the following effects:
the method can be used for analyzing and judging the similarity of the states of the Web application pages, and accurately and effectively reducing the influence of the similar pages in the traversal process;
by utilizing the method, different states of the Web page can be efficiently and accurately mined, APIs in the pages in different states are identified, and the coverage rate of the API identification can be improved;
the method can effectively improve the number and coverage of the Web application API identification and make up for the defect that the dynamic traversal of the Web application API identification is incomplete.
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In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the following description will briefly explain the drawings needed in the embodiments or the prior art description, and it is obvious that the drawings in the following description are some embodiments of the application and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is an overall flow of Web application API discovery based on state similarity analysis in accordance with the present application;
FIG. 2 is a key data structure in an extraction form according to the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
FIG. 1 illustrates the overall flow of the present application, as shown, primarily involving web page state representation, state similarity analysis, and API discovery. Through Web page state representation, state similarity analysis is more accurate, and based on state similarity page analysis, web application program pages can be covered as much as possible, so that APIs (application program interfaces) can be identified and extracted. The application provides a Web application API (application program interface) mining method based on state similarity analysis, which comprises the following steps:
step S1, acquiring a target Web application webpage, and extracting key elements in the webpage, wherein the key elements comprise: URL links in the web pages, form submissions in the web pages, execution of JavaScript events in the web pages;
s2, constructing a comprehensive webpage model by utilizing the local URL link, the Form and the JavaScript event in the current webpage obtained in the step S1, wherein the state of the characterization webpage is specifically expressed as < URL_link, form and JavaScript_events >;
the application utilizes the local URL link, the Form and the JavaScript event in the current webpage to construct the comprehensive webpage model, which not only characterizes the state of the webpage, but also is specifically expressed as < URL_link, form, javaScript_events >. For the three elements in the webpage, the elements are traversed according to a state sensing mode during webpage mining, and compared with the existing webpage mining mode, the construction of the model can enable the mined webpage to be more comprehensive.
Wherein the URL links mainly comprise non-resource file local URLs capable of jumping web pages, including absolute URL links and relative URL links.
The main factors that cause the Web application page content to change are as follows:
the first factor is the URL links in the web page. There are many tags in the Web application front end source that can be used to contain local URLs, such as < a >, < link >, < iframe > and < script >, and the href and src fields in these tags typically contain URL links that can be used for page switching, and once these URLs for page or function switching are accessed, the content of the Web page changes.
The second element is form submission in the web page. Forms are one of the main ways in which users interact with Web applications, mainly using HTTP POST request methods. Taking an administrator login interface in a Web application as an example. When the administrator enters the correct account password into the form and submits the data, the Web application will authenticate the user and redirect it to the administrator interface, resulting in a change in the page.
The third element is execution of a JavaScript event in the web page. Modern web application front-end interfaces provide a rich interactive experience for users using various JavaScript events, such as mouse clicks, keyboard inputs, and touch events. Using these events, the web page may dynamically present content, update functionality and data in real-time, and provide various interactive elements, such as sliders, popups, and menus. The triggering of these JavaScript events often results in changes in the content of the page. For example, introducing new functionality may introduce new URL addresses or data interaction APIs.
Therefore, the application constructs a comprehensive webpage model by utilizing the local URL links, the forms and the JavaScript events in the current webpage, which represents the state of the webpage and is specifically expressed as < URL_link, form, javaScript_events >. For each class of elements, each state includes a different element value.
Wherein the URL links mainly include absolute URL links and relative URL links. Absolute URL (Uniform Resource Locator) is a complete Web address specifying the protocol, domain name and path of the resource. It provides the exact location of the resource on the Internet, including the server and directory where the resource resides. Instead, the relative URL provides a partial Web address, pointing to the resource relative to the URL of the current Web page. Relative URLs are typically used to link to resources within the same web site, they are short and easy to manage. The present application matches and extracts all URLs from the < a >, < link >, < iframe > and < script > tags. For absolute URLs, the application performs data cleansing to exclude non-local URLs and resource file URLs.
When a form exists in the web page, the key data in the form is extracted and the form is described using a uniform format, as shown in fig. 2. Specifically, the application extracts the data interaction method (mainly comprising GET and POST), the URL of the action field, and the id field value and value field value in the < input > tag in the form.
To fully acquire all event information on the current page, the present application uses the getEventListeners (document) command in the Google Chrome to acquire all event information on the current page, including event information triggered by JavaScript code embedded in HTML code, such as "onclick" and "onload" events in the < button > and < body > tags. By acquiring the event information, the method and the device can accurately identify and describe the event on the page.
According to the application, by extracting the local URL links, the forms and the JavaScript events in the Web page, the analysis and the storage of useless data in the Web page can be reduced, and the page state of the Web application program can be more accurately represented.
S3, vectorizing three elements in the webpage model obtained in the step S2;
taking URL links as an example, vectorization is performed according to the number of occurrences of one URL link, for example, URL link "https:// sample.domain/path/. The criteria for vectorization may be determined as desired.
Step S4, calculating cosine similarity based on the vectorized three elements obtained in the step S3;
when URL link jump, form submission or JavaScript event triggering actions occur in the page, the page is modeled again and similarity analysis of the page states is performed so as to judge whether the page is a new page state or not, and whether further API mining and page traversing are needed based on the page or not is judged. According to the application, the URL link, the form and the JavaScript event in the webpage model are subjected to cosine similarity calculation with the corresponding type elements in the previous webpage state, so that the cosine similarity of the URL link, the form and the JavaScript event is calculated respectively, and weights are distributed according to the influence degree of the three types of elements on the change of the webpage content, thereby calculating the overall similarity.
When URL link jump, form submission or JavaScript event triggering actions occur in the page, the page is modeled again and similarity analysis of the page states is performed so as to judge whether the page is a new page state or not, and whether further API mining and page traversing are needed based on the page or not is judged. According to the application, the URL link, the form and the JavaScript event in the webpage model are subjected to cosine similarity calculation with the corresponding type elements in the previous webpage state, so that the cosine similarity of the URL link, the form and the JavaScript event is calculated respectively, and weights are distributed according to the influence degree of the three types of elements on the change of the webpage content, thereby calculating the overall similarity.
Cosine similarity under different states of each class of elements is calculated respectively, and a cosine similarity calculation formula is as follows:
wherein ,X、Yfor the same type element vector in two page states, the vector value, X, of the data representing any type element type in the local URL link, the form and the JavaScript event in two different states i 、Y i Representing the ith component in the element vector of the class, and n represents the number of components of the data vector of the corresponding type element in each page state.
And S5, calculating the overall similarity according to the weight of each class of elements obtained in the step S4. The method comprises the following steps:
after the cosine similarity among different states of each class of elements is calculated, the overall similarity is calculated according to the weight of each class of elements, and the calculation formula is as follows:
wherein 。
Wherein A is the page state to be analyzed, B is the page state which has been analyzed,、/>、/>related weight coefficients representing three types of elements of URL links in the web page, forms in the web page and JavaScript events in the web page,、/>、/>and representing the results of cosine similarity calculation of URL links in two different webpage states, forms of the webpages and JavaScript events in the webpages respectively.
And S6, setting a threshold value X, and when the overall similarity is smaller than the threshold value, indicating that the current webpage model is in a new webpage state, and performing API mining under the state.
When the overall similarity is less than the threshold, then the token may perform a exploration traversal of the new web page state and a mining of the API based on the page state. In a new page state, the application discovers the Web application API, mainly extracts all URLs conforming to RESTful API, SOAP API and JSON-RPC API specifications in the URL link of the current page state, and conventionally comprises the GET request URL link of the request parameter and all form requests in the page state. By performing API mining analysis on page states used by the Web application and performing deduplication on the same data on the collected APIs, APIs in the target Web application are mined as much as possible.
In summary, the application is based on a brand new Web page state representation and page state similarity analysis method, so that the Web page analysis efficiency and the Web page coverage rate are improved, the data interaction points existing in the target Web application system are more comprehensively and efficiently discovered, the negative influence of the similarity page on page traversal is reduced, the Web application page state coverage rate and the Web page traversal efficiency are improved, and the Web application API coverage rate is improved. The method and the device realize efficient, accurate and high-coverage discovery of the data interaction points in the Web application, namely maximize the number of the data interaction points of the discovered Web application and prevent the crawler from falling into infinite circulation.
Note that the technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be regarded as the scope of the description. The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (9)
1. A Web application API (application program interface) mining method based on page state similarity analysis is characterized by comprising the following steps:
step S1, acquiring a target Web application webpage, and extracting key elements in the webpage, wherein the key elements comprise: URL links in the web page, forms in the web page, javaScript events in the web page;
s2, constructing a comprehensive webpage model by using the acquired local URL links, the forms and the JavaScript events in the current webpage, representing the state of the webpage, and setting the state as < URL_link, form and JavaScript_events >;
s3, respectively vectorizing three elements in the constructed comprehensive webpage model;
step S4, based on the three vectorized elements, respectively calculating cosine similarity of the same kind of elements contained in different states of the page according to element characteristics of the three vectorized elements;
step S5, respectively setting different weights according to the influence degree of each type of elements on the page state change, and calculating the overall similarity of the current state and the rest state;
and S6, judging whether the overall similarity is smaller than a specified threshold value, if so, obtaining a new page state, and performing API mining under the page state.
2. The Web application API mining method based on page status similarity analysis as recited in claim 1, wherein said URL links include absolute URL links and relative URL links, and for absolute URL, data cleansing is set to exclude non-local URL and resource file URL.
3. The Web application API mining method based on page state similarity analysis of claim 1, wherein obtaining the form comprises: extracting key data in the form and describing the form by using a uniform format.
4. The Web application API mining method based on page status similarity analysis according to claim 1, wherein the cosine similarity calculation formula in step S4 is as follows:
，
wherein ,X、Yfor the same type element vector in two page states, the vector value, X, of the data representing any type element type in the local URL link, the form and the JavaScript event in two different states i 、Y i Representing the ith component in the element vector of the class, and n represents the number of components of the data vector of the corresponding type element in each page state.
5. The Web application API mining method based on page state similarity analysis according to claim 4, wherein in step S5, different weights are set according to the influence degree of each type of element on the page content change, and the overall similarity between the current page state to be analyzed and the rest of analyzed page states is calculated according to the following calculation formula:
，
wherein The method comprises the steps of carrying out a first treatment on the surface of the Wherein A is the page status to be analyzed, B is the page status already analyzed, and ++>、/>、/>Related weight coefficients representing three elements of URL links in a webpage, forms in the webpage and JavaScript events in the webpage,>、/>、/>and representing the results of cosine similarity calculation of URL links in two different webpage states, forms of the webpages and JavaScript events in the webpages respectively.
6. The Web application API mining method based on page status similarity analysis according to claim 1, wherein said performing API mining in step S6 includes: and extracting all URLs conforming to RESTful API, SOAP API and JSON-RPC API specifications in the URL link of the current page state, and GET request URL containing request parameters and all form requests in the page state.
7. A Web application API mining system for implementing page state similarity based analysis, the system being configured to implement a Web application API mining method based on page state similarity analysis as claimed in any one of claims 1-6.
8. An electronic device, characterized in that: the electronic device includes a memory and a processor for executing a program stored in the memory to implement the Web application API mining method based on page status similarity analysis according to any one of claims 1 to 6.
9. A computer-readable storage medium storing a computer program for implementing the Web application API mining method according to any one of claims 1-6 based on page state similarity analysis.
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