CN106355095B - Method for distinguishing is known to fraud webpage using fuzzy theory - Google Patents
Method for distinguishing is known to fraud webpage using fuzzy theory Download PDFInfo
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- CN106355095B CN106355095B CN201611046454.8A CN201611046454A CN106355095B CN 106355095 B CN106355095 B CN 106355095B CN 201611046454 A CN201611046454 A CN 201611046454A CN 106355095 B CN106355095 B CN 106355095B
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Abstract
The present invention discloses a kind of method that fraud webpage is identified using fuzzy theory, it is related to a kind of fraud webpage identification technology not depending on web page characteristics, solve the problems, such as that fraud webpage identifies using the thinking and fuzzy theory shared out the work and helped one another, the quality of webpage is determined by different users, data set after user makes label is analyzed by computer, to solve the existing fraud web page identification method technical problem big to the dependence of webpage.This technical solution is simple and effective, has important practical value in future searches engine.
Description
Technical field
The present invention discloses a kind of method that fraud webpage is identified using fuzzy theory, is related to one kind and not depending on webpage
The fraud webpage identification technology of feature, belongs to internet security and service technology field.
Background technology
Search engine has become the indispensable tool of Internet user, but due to the driving of interests, and fraud webpage is big
Amount mixes in internet.Tricker takes improper means, is carried out to webpage sorting for search engine ordering strategy artificial
Intervene, with acquisition and the disproportionate high ranking in its status, acquisition of the interference user to information, or even damage user benefit, these
Webpage is referred to as cheating webpage, and the mode that tricker takes can be divided into four kinds：Mode based on content, the side based on link
Formula, the mode based on concealing technique and the mode based on redirection, previous anti-fraud research are carried out for four kinds of deception modes
Webpage itself is depended in identification unduly, and recognition result is of short duration effectively, and the fraud web page identification method that searching does not depend on web page characteristics is
A current major issue urgently to be resolved hurrily.
Invention content
A kind of utilization fuzzy theory of the present invention does not depend on the fraud net of web page characteristics to cheating web page identification method
Page recognition methods, solve previous identification fraud web-page approach depends on that webpage itself, recognition result are of short duration effectively to ask unduly
Topic.
For a kind of utilization fuzzy theory of the present invention to cheating web page identification method, technical solution includes following step
Suddenly：
Step 1：
User has browsed webpage, and carrying out evaluation to webpage makes user's mark：Respectively " non-fraud webpage F ", " fraud net
Page S ", " equivocal B " or " not knowing U "；
Step 2：
Each the end of month is downloaded the data set of of that month whole user's marks by search engine；
Step 3：
Several matrix M is divided by the quantity that each webpage different user marks to data seti, wherein i=1,2 ...,
n；
Step 4：
To each matrix Mi：It is denoted as N, changes into each element R of fuzzy similarity matrix R, Rij, wherein i, j=1,2 ...,
N, n ∈ R, calculation formula include：
Wherein, i, j=1,2 ..., n；N is the line number of N；
Wherein, i, j=1,2 ..., n；N is the line number of N, and m is the columns of N；
Step 5：
Fuzzy similarity matrix changes into fuzzy equivalent matrix, and formula is as follows：
N is self-heating number；P is the line number of R；
Until meeting Rb*Rb！=RbCondition, matrix reach convergence；
Step 6：
Convergent matrix is chosen into all confidence values [0,1], calculates Level Matrix；
Step 7：
For each Level Matrix, cluster generates multiple set, it is artificial to select first website from each set successively
Judgement is that fraud webpage is also non-fraud webpage, if fraud webpage then thinks that the set belongs to fraud webpage；If being non-fraud
Webpage then thinks that the set belongs to non-fraud webpage.
The positive effect of the present invention is：It is asked using the thinking and fuzzy theory shared out the work and helped one another to solve fraud webpage identification
Topic, the quality of webpage is determined by different users, and the data set after user makes label is analyzed by computer, existing to solve
The technical problem for having fraud web page identification method big to the dependence of webpage.This technical solution is simple and effective, in future searches
There is important practical value in engine.
Specific implementation mode
In order to illustrate more clearly of technical solution of the present invention, will be described below according to technology described in technical solution to
Go out three embodiments, for those of ordinary skill in the art, without having to pay creative labor, can also incite somebody to action
The technical solution applies in Practical Project.
Embodiment 1
Step 1：After user has browsed webpage, according to the evaluation to webpage, four kinds pre-set from webpage mark
The selection of oneself is provided in (F, S, B, U), such as：What 362F U were indicated is that there are two the labels of user point for website that id is 362
It Wei not F and U.
Step 2：In order to meet the requirement of embodiment, we use data set webspam-uk2007 (" WebSpam
Collections ", http://chato.cl/webspam/datasets/, Crawled by the Laboratory of
Web Algo rithmics, University of Milan, http://law.di.unimi.it/) verify the reality of cluster
The discrimination tested.
Step 3：50 datas that number of users is 2 are chosen from data set, generate the matrix M of 50*2.
Step 4：Fuzzy similarity matrix is calculated according to formula to the matrix and obtains the matrix R of 50*50.
Calculation formula includes：
Wherein, i, j=1,2 ..., n.N is the line number of N；
Wherein, i, j=1,2 ..., n.N is the line number of N, and m is the columns of N；
Step 5：To matrix R caused by step 4, fuzzy equivalent matrix is calculated using formula, result of calculation is m=8,
That is R8·R8=R8, at this moment R is still the matrix of 50*50.
Formula is as follows：
N is self-heating number；P is the line number of R；
Until meeting Rb*Rb！=RbCondition, matrix reach convergence；
Step 6：The sequential organization of element included in matrix from big to small is as follows:It is denoted as λ：1>0.9>0.8.According to
Secondary to take λ=1,0.9,0.8 calculates separately its cut set matrix, and as λ=1, all 1 values of being less than all are substituted for 0 in matrix, generate
First Level Matrix；As λ=0.9, all 0.9 values of being more than or equal to all are substituted for 1 in matrix, all in matrix to be less than 0.9
Value be all substituted for 0, generate second Level Matrix；As λ=0.8, all 0.8 values of being more than or equal to all are substituted for 1 in matrix,
Generate third Level Matrix.
Step 7：
As λ=1,
Cluster generate 5 set, successively from each set choose first website artificial judgment be fraud webpage or
Non- fraud webpage thinks the set category if fraud webpage then thinks that the set belongs to fraud webpage if being non-fraud webpage
In non-fraud webpage, embodiment result such as following table：(the judgement that we provide according to data set for each website in each set
It carries out verifying its corresponding discrimination)
As λ=0.9, cluster generates 4 set, and it is to take advantage of that first website artificial judgment is chosen from each set successively
It is also non-fraud webpage to cheat webpage, if fraud webpage then thinks that the set belongs to fraud webpage, is recognized if being non-fraud webpage
Belong to non-fraud webpage, embodiment result such as following table for the set：(for each website in each set we according to data set
The judgement provided carries out verifying its corresponding discrimination)
As λ=0.8, cluster generates 1 set, and embodiment 1 completes embodiment 1 as mark.
Embodiment 2
Step 1：After user has browsed webpage, according to the evaluation to webpage, four kinds pre-set from webpage mark
The selection of oneself is provided in (F, S, B, U), such as：What 362F U were indicated is that there are two the labels of user point for website that id is 362
It Wei not F and U.
Step 2：In order to meet the requirement of embodiment, we use data set webspam-uk2007 (" WebSpam
Collections ", http://chato.cl/webspam/datasets/, Crawled by the Laboratory of
Web Algorithmics, University of Milan, http://law.di.unimi.it/) verify the experiment of cluster
Discrimination.
Step 3：100 datas that number of users is 2 are chosen from data set, generate the matrix M of 100*2.
Step 4：Fuzzy similarity matrix is calculated according to formula to the matrix and obtains the matrix R of 100*100.
Calculation formula includes：
Wherein, i, j=1,2 ..., n.N is the line number of N；
Wherein, i, j=1,2 ..., n.N is the line number of N, and m is the columns of N；
Step 5：To matrix R caused by step 4, fuzzy equivalent matrix is calculated using formula, result of calculation is m=
16, i.e. R16·R16=R16, at this moment R is still the matrix of 100*100.
Formula is as follows：
N is self-heating number；P is the line number of R；
Until meeting Rb*Rb！=RbCondition, matrix reach convergence；
Step 6：The sequential organization of element included in matrix from big to small is as follows:It is denoted as λ：1>0.9>0.8.According to
Secondary to take λ=1,0.9,0.8 calculates separately its cut set matrix, and as λ=1, all 1 values of being less than all are substituted for 0 in matrix, generate
First Level Matrix；As λ=0.9, all 0.9 values of being more than or equal to all are substituted for 1 in matrix, all in matrix to be less than 0.9
Value be all substituted for 0, generate second Level Matrix；As λ=0.8, all 0.8 values of being more than or equal to all are substituted for 1 in matrix,
Generate third Level Matrix.
Step 7：
As λ=1,
Cluster generate 8 set, successively from each set choose first website artificial judgment be fraud webpage or
Non- fraud webpage thinks the set category if fraud webpage then thinks that the set belongs to fraud webpage if being non-fraud webpage
In non-fraud webpage, embodiment result such as following table：(the judgement that we provide according to data set for each website in each set
It carries out verifying its corresponding discrimination)
As λ=0.9,
Cluster generate 2 set, successively from each set choose first website artificial judgment be fraud webpage or
Non- fraud webpage thinks the set category if fraud webpage then thinks that the set belongs to fraud webpage if being non-fraud webpage
In non-fraud webpage, embodiment result such as following table：(the judgement that we provide according to data set for each website in each set
It carries out verifying its corresponding discrimination)
As λ=0.8, cluster generates 1 set, and embodiment 2 completes embodiment 2 as mark.
Embodiment 3
Step 1：After user has browsed webpage, according to the evaluation to webpage, four kinds pre-set from webpage mark
The selection of oneself is provided in (F, S, B, U), such as：What 362F U were indicated is that there are two the labels of user point for website that id is 362
It Wei not F and U.
Step 2：In order to meet the requirement of embodiment, we use data set webspam-uk2007 (" WebSpam
Collections ", http://chato.cl/webspam/datasets/, Crawled by the Laboratory of
Web Algo rithmics, University of Milan, http://law.di.unimi.it/) verify the reality of cluster
The discrimination tested.
Step 3：200 datas that number of users is 2 are chosen from data set, generate the matrix M of 200*2.
Step 4：Fuzzy similarity matrix is calculated according to formula to the matrix and obtains the matrix R of 200*200.
Calculation formula includes：
Wherein, i, j=1,2 ..., n.N is the line number of N；
Wherein, i, j=1,2 ..., n.N is the line number of N, and m is the columns of N；
Step 5：To matrix R caused by step 4, fuzzy equivalent matrix is calculated using formula, result of calculation is m=8,
That is R8·R8=R8, at this moment R is still the matrix of 200*200.
Formula is as follows：
N is self-heating number；P is the line number of R；
Until meeting Rb*Rb！=RbCondition, matrix reach convergence；
Step 6：The sequential organization of element included in matrix from big to small is as follows:It is denoted as λ：1>0.9>0.8.According to
Secondary to take λ=1,0.9,0.8 calculates separately its cut set matrix, and as λ=1, all 1 values of being less than all are substituted for 0 in matrix, generate
First Level Matrix；As λ=0.9, all 0.9 values of being more than or equal to all are substituted for 1 in matrix, all in matrix to be less than 0.9
Value be all substituted for 0, generate second Level Matrix；As λ=0.8, all 0.8 values of being more than or equal to all are substituted for 1 in matrix,
Generate third Level Matrix.
Step 7：
As λ=1,
Cluster generate 9 set, successively from each set choose first website artificial judgment be fraud webpage or
Non- fraud webpage thinks the set category if fraud webpage then thinks that the set belongs to fraud webpage if being non-fraud webpage
In non-fraud webpage, embodiment result such as following table：(the judgement that we provide according to data set for each website in each set
It carries out verifying its corresponding discrimination)
As λ=0.9,
Cluster generate 3 set, successively from each set choose first website artificial judgment be fraud webpage or
Non- fraud webpage thinks the set category if fraud webpage then thinks that the set belongs to fraud webpage if being non-fraud webpage
In non-fraud webpage, embodiment result such as following table：(the judgement that we provide according to data set for each website in each set
It carries out verifying its corresponding discrimination)
As λ=0.8, cluster generates 1 set, and embodiment 3 completes embodiment 3 as mark.
Claims (1)
1. a kind of knowing method for distinguishing using fuzzy theory to fraud webpage, include the following steps：
Step 1：
User has browsed webpage, and carrying out evaluation to webpage makes user's mark：Respectively " non-fraud webpage F ", " fraud webpage
S ", " equivocal B " or " not knowing U "；
Step 2：
Each the end of month is downloaded the data set of of that month whole user's marks by search engine；
Step 3：
Several matrix M is divided by the quantity that each webpage different user marks to data seti, wherein i=1,2 ..., n；
Step 4：
To each matrix Mi：It is denoted as N, changes into each element R of fuzzy similarity matrix R, Rij, wherein i, j=1,2 ..., n, n ∈
R, calculation formula include：
Wherein, i, j=1,2 ..., n；N is the line number of N；
Wherein, i, j=1,2 ..., n；N is the line number of N, and m is the columns of N；
Step 5：
Fuzzy similarity matrix changes into fuzzy equivalent matrix, and formula is as follows：
B=1,2 ..., n；N is natural number；P is the line number of R；
Until meeting Rb*Rb！=RbCondition, matrix reach convergence；
Step 6：
Convergent matrix is chosen into all confidence values [0,1], calculates Level Matrix；
Step 7：
For each Level Matrix, cluster generates multiple set, selects first website artificial judgment from each set successively
Be fraud webpage be also non-fraud webpage, if fraud webpage then think that the set belongs to fraud webpage；If being non-fraud webpage
Then think that the set belongs to non-fraud webpage.
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| CN107194281B (en) * | 2017-05-25 | 2019-07-16 | 成都知道创宇信息技术有限公司 | A kind of anti-fake system based on block chain technology |
| CN108985815A (en) * | 2018-06-06 | 2018-12-11 | 阿里巴巴集团控股有限公司 | A kind of user identification method, device and equipment |
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