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A Method of Identifying Fraudulent Web Pages Using Fuzzy Theory

A fuzzy theory, web technology, applied in network data indexing, network data retrieval, electronic digital data processing, etc., can solve the problems of excessive reliance on the web page itself, short-lived and effective identification results, etc., and achieve the effect of simple and effective technical solutions

Inactive Publication Date: 2018-10-19
JILIN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] A method for identifying fraudulent webpages using fuzzy theory, a method for identifying fraudulent webpages that does not rely on the characteristics of webpages described in the present invention, solves the problems of over-reliance on the webpage itself and short-lived and effective identification results in previous methods for identifying fraudulent webpages

Method used

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  • A Method of Identifying Fraudulent Web Pages Using Fuzzy Theory
  • A Method of Identifying Fraudulent Web Pages Using Fuzzy Theory
  • A Method of Identifying Fraudulent Web Pages Using Fuzzy Theory

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0028] Step 1: After the user browses the webpage, according to the evaluation of the webpage, give his choice from the four pre-set tags (F, S, B, U) on the webpage, for example: 362F U means that the id is 362 The site has two users labeled F and U.

[0029] Step 2: In order to meet the requirements of the embodiment, we use the dataset webspam-uk2007 ("WebSpamCollections", http: / / chato.cl / webspam / datasets / , Crawled by the Laboratory of Web Algo rituals, University of Milan, http: / / chato.cl / webspam / datasets / / law.di.unimi.it / ) to verify the recognition rate of clustering experiments.

[0030] Step 3: Select 50 pieces of data with 2 users from the data set to generate a matrix M of 50*2.

[0031] Step 4: Calculate the fuzzy similarity matrix according to the formula to obtain a matrix R of 50*50.

[0032] The calculation formula includes:

[0033]

[0034] Wherein, i, j=1, 2, . . . , n. n is the number of rows of N;

[0035]

[0036] Wherein, i, j=1, 2, . . . , n. ...

Embodiment 2

[0051] Step 1: After the user browses the webpage, according to the evaluation of the webpage, give his choice from the four pre-set tags (F, S, B, U) on the webpage, for example: 362F U means that the id is 362 The site has two users labeled F and U.

[0052] Step 2: In order to meet the requirements of the embodiment, we use the dataset webspam-uk2007 ("WebSpamCollections", http: / / chato.cl / webspam / datasets / , Crawled by the Laboratory of Web Algorithmics, University of Milan, http: / / law.di.unimi.it / ) to verify the recognition rate of clustering experiments.

[0053] Step 3: Select 100 pieces of data with 2 users from the data set to generate a matrix M of 100*2.

[0054] Step 4: Calculate the fuzzy similarity matrix according to the formula to obtain a matrix R of 100*100.

[0055] The calculation formula includes:

[0056]

[0057] Wherein, i, j=1, 2, . . . , n. n is the number of rows of N;

[0058]

[0059] Wherein, i, j=1, 2, . . . , n. n is the number of row...

Embodiment 3

[0074] Step 1: After the user browses the webpage, according to the evaluation of the webpage, give his choice from the four pre-set tags (F, S, B, U) on the webpage, for example: 362F U means that the id is 362 The site has two users labeled F and U.

[0075] Step 2: In order to meet the requirements of the embodiment, we use the dataset webspam-uk2007 ("WebSpamCollections", http: / / chato.cl / webspam / datasets / , Crawled by the Laboratory of Web Algo rituals, University of Milan, http: / / chato.cl / webspam / datasets / / law.di.unimi.it / ) to verify the recognition rate of clustering experiments.

[0076] Step 3: Select 200 pieces of data with 2 users from the data set to generate a matrix M of 200*2.

[0077] Step 4: Calculate the fuzzy similarity matrix according to the formula to obtain a matrix R of 200*200.

[0078] The calculation formula includes:

[0079]

[0080] Wherein, i, j=1, 2, . . . , n. n is the number of rows of N;

[0081]

[0082] Wherein, i, j=1, 2, . . . ,...

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Abstract

The invention discloses a method for identifying a fraud website by utilizing a fuzzy theory and relates to a technology for identifying a fraud website independent of website characteristics. The fraud website identifying problem is solved by utilizing the thought of division and coordination of labor and the fuzzy theory. The website quality is decided by different users, and data sets marked by the users are analyzed by a computer to solve the technical problem that an existing fraud website identifying method has large website dependency. The method is simple and effective and has an important practical value in a future search engine.

Description

technical field [0001] The invention discloses a method for identifying fraudulent webpages using fuzzy theory, relates to a fraudulent webpage identification technology that does not depend on webpage features, and belongs to the technical field of Internet security and services. Background technique [0002] Search engines have become an indispensable tool for Internet users, but due to the drive of interests, a large number of fraudulent web pages are mixed in the Internet. Fraudsters use abnormal means to artificially intervene in the ranking of webpages according to the ranking strategy of search engines, so as to obtain high rankings that are not commensurate with their status, interfere with users' access to information, and even damage users' interests. These webpages are called fraudulent webpages, deceiving The methods adopted by researchers can be divided into four types: content-based methods, link-based methods, masking technology-based methods, and redirection-...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F21/56G06F17/30
CPCG06F16/951G06F21/566
Inventor 尚靖博左祥麟左万利王英
Owner JILIN UNIV