CN106529233B - A kind of return visit user identification arithmetic based on browser fingerprint diversity factor - Google Patents
A kind of return visit user identification arithmetic based on browser fingerprint diversity factor Download PDFInfo
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- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
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Abstract
The present invention relates to a kind of return visit user identification arithmetics based on browser fingerprint diversity factor, comprising the following steps: acquisition fingerprint characteristic parameter simultaneously makes fingerprint using hash function；Judge that the fingerprint whether there is in fingerprint base, and if it exists, determine that the fingerprint for the fingerprint of return visit user, otherwise carries out in next step；The global disparity angle value of all fingerprints in the fingerprint and fingerprint base is calculated one by one；A certain global disparity angle value is less than threshold value if it exists, then determines the fingerprint to pay a return visit the upgrading fingerprint of user, and existing fingerprint corresponding with the global disparity angle value in fingerprint base is updated to the upgrading fingerprint；Otherwise determine that the fingerprint is the fingerprint of new user, and the fingerprint is stored in fingerprint base.The present invention not only increases the discrimination for paying a return visit user identification arithmetic, while also reducing the False Rate of identification.
Description
Technical field
The present invention relates to a kind of return visit user identification arithmetics based on browser fingerprint diversity factor.
Background technique
Browser fingerprint is mainly used for the fields such as user tracking, authentication, user's identification.Browser fingerprint is a kind of base
In browser information combination recognition methods, by browser platform from configuration information, software composition and hardware composition etc. layers
The characteristic parameters such as the screen message, plugin information, font information of equipment are got on secondary, and combine formation to use with unique identification
The finger print data at family.Wherein, the fingerprint characteristic parameter of acquisition is the intrinsic status information of user, rather than the privacy of user is believed
Breath.
Achieve following research achievement with regard to browser fingerprint technique at present: Peter Eckersley et al. proposes one kind
With the intimate user identification method of cookie, referred to as browser fingerprint, and pass through experimental verification browser fingerprint tool
There is higher discrimination.Nikiforakis et al. has carried out pair the characteristic parameter acquisition modes in three big business fingerprint algorithms
Than analysis, the function of discovery limitation Flash and JavaScript effectively can inhibit fingerprint to generate, and then it is clear to propose interference
The method that device fingerprint of looking at generates.It is excellent that Keaton Movwery et al. proposes a kind of API for combining HTML5 and Javascript
The fingerprint identification method of gesture realizes the fingerprint recognition based on HTML 5, and wherein most typically HTML5 fingerprint mode refers to for canvas
Line.In addition this technology of JavaScript engine fingerprint, due to different browsers JavaScript engine have it is subtle
Difference, therefore fingerprint is made using small difference.In the prior art also by the phase of character string between successive appraximation fingerprint
Like degree, in certain similar range, then it is assumed that fingerprint is the update fingerprint for paying a return visit user.
It is comprehensive it, contemporary literature mainly study different types of browser fingerprint working principle and interference fingerprint generate side
Method causes the mode of fingerprint static matching not identify completely and pays a return visit asking for user after rarer document concern characteristic parameter variation
Topic.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of, the return visit user based on browser fingerprint diversity factor identifies calculation
Method compares the diversity factor between browser fingerprint by 8 fingerprint characteristic parameters, efficiently identifies the return visit user of website.
To achieve the above object, the present invention adopts the following technical scheme: a kind of return visit based on browser fingerprint diversity factor
User identification arithmetic, which comprises the following steps:
Step S1: acquisition fingerprint characteristic parameter simultaneously makes fingerprint using hash function；
Step S2: judge that the fingerprint whether there is in fingerprint base, and if it exists, determine the fingerprint for the finger of return visit user
Otherwise line carries out step S3；
Step S3: the global disparity angle value of all fingerprints in the fingerprint and fingerprint base is calculated one by one；
Step S4: a certain global disparity angle value is less than threshold value if it exists, then determines the fingerprint for the upgrading of return visit user
Fingerprint, and existing fingerprint corresponding with the global disparity angle value in fingerprint base is updated to the upgrading fingerprint；Otherwise determine institute
The fingerprint that fingerprint is new user is stated, and the fingerprint is stored in fingerprint base.
Further, the calculation method of the global disparity angle value is as follows:
Wherein, D (BF1,BF2) indicate fingerprint BF1With fingerprint BF2Global disparity angle value；d(attri(BF1,BF2)) indicate
Characteristic parameter diversity factor when i takes 1 to 8, respectively represents User agent diversity factor, plugin information diversity factor, font information difference
Degree, screen resolution diversity factor, time zones differences degree, HTTP Accept diversity factor, video diversity factor and Cookie diversity factor；
WattriIndicate that the weight of each characteristic parameter diversity factor, value are the comentropy of character pair parameter.
Further, the User agent diversity factor is by browser related data diversity factor and device architecture difference
Spend two parts composition:
d(attr1(BF1,BF2))=0.5 × Fbr+0.5×Farchi
Wherein, FbrFor browser related data diversity factor, FarchiFor device architecture diversity factor,To refer to
Line BF1Browser title,For fingerprint BF2Browser title,For fingerprint BF1Browser version
Number,For fingerprint BF2Browser version number；d(attr1(BF1,BF2)) it is fingerprint BF1With fingerprint BF2User
Agent diversity factor.
Further, the circular of the plugin information diversity factor is as follows: enabling fingerprint BF1Plug-in unit list be LP
(BF1), fingerprint BF2Plug-in unit list be LP (BF2), then:
FU=| (LP (BF1)\(LP(BF1)∩LP(BF2)))∪LP(BF2)|
Wherein, FU is fingerprint BF1With fingerprint BF2Two plug-in unit lists in plug-in unit sum；
For fingerprint BF1With fingerprint BF2, the ratio of distinctive plug-in unit in two plug-in unit lists is calculated, result uses F respectively1With
F2It indicates, solution procedure is as follows:
Wherein, LP=name ,=verPlugin name and the consistent plug-in unit number of plug-in version number in indication plug unit list LP,
LP=name, ≠ verPlugin name is identical in indication plug unit list LP, the different plug-in unit number of plug-in version number；
Calculate LP (BF1) and LP (BF2) plugin name is identical in two plug-in unit lists, the different plug-in unit ratio of plug-in version number
And use F3It indicates, solution procedure is as follows:
Calculate LP (BF1) and LP (BF2) all identical plug-in unit ratio of plugin name and version number and table is used in two plug-in unit lists
Show F4, solution procedure is as follows:
For fingerprint BF1With fingerprint BF2, plugin information diversity factor d (attr2(BF1,BF2)) solution it is as follows:
Further, the circular of the font information diversity factor is as follows: enabling fingerprint BF1List of fonts be LF
(BF1), fingerprint BF2List of fonts be LF (BF2), then:
FU=| (LF (BF1)\(LF(BF1)∩LF(BF2)))∪LF(BF2)|
Wherein, FU fingerprint BF1With fingerprint BF2Two list of fonts in font sum；
List of fonts LF (BF1) and list of fonts LF (BF2) in the ratio of distinctive font use F respectively1And F2It indicates, asks
Solution preocess is as follows:
List of fonts LF (BF1) and list of fonts LF (BF2) in same font ratio F3It indicates, solution procedure is such as
Under:
For fingerprint BF1With fingerprint BF2, font information diversity factor d (attr3(BF1,BF2)) solution it is as follows:
Further, the screen resolution diversity factor, time zones differences degree, HTTP Accept diversity factor, video difference
Degree and the circular of Cookie diversity factor are as follows:
Wherein, x is natural number and 4≤x≤8；As x=4, d (attr4(BF1,BF2)) indicate screen resolution difference
Degree, attr4(BF1) indicate fingerprint BF1The screen resolution of middle characteristic parameter, attr4(BF2) indicate fingerprint BF2Screen
Resolution ratio；As x=5, d (attr5(BF1,BF2)) indicate time zones differences degree, attr5(BF1) indicate fingerprint BF1Time zone ginseng
Number, attr5(BF2) indicate fingerprint BF2Time zone parameter；Work as x=6, d (attr6(BF1,BF2)), indicate HTTP Accept difference
Degree, attr6(BF1) indicate fingerprint BF1HTTP Accept parameter, attr6(BF2) indicate fingerprint BF2HTTP Accept ginseng
Number；As x=7, d (attr7(BF1,BF2)) indicate video diversity factor, attr7(BF1) indicate fingerprint BF1Video parameter,
attr7(BF2) indicate fingerprint BF2Video parameter；As x=8, d (attr8(BF1,BF2)) indicate Cookie diversity factor,
attr8(BF1) indicate fingerprint BF1Cookie parameter, attr8(BF2) fingerprint BF2Cookie parameter
Further, the calculation method of the comentropy of the characteristic parameter is as follows: enabling a browser fingerprint algorithm is BF
() gives a new browser information x, generates a browser fingerprint BF (x), and discrete probability density function is P
(fn),n∈[0,1,...,N]；Then for characteristic parameter a, comentropy are as follows:
Wherein, fn=BF (x) indicates a browser fingerprint.
Further, the threshold value is 0.025,0.05 or 0.1.
Compared with the prior art, the invention has the following beneficial effects: the present invention first defines 8 fingerprint characteristic parameters
Local diversity factor calculation method, weighted sum obtain measure two fingerprints between diversity factor formula, it is then, complete by what is acquired
Office's difference angle value is compared with preset threshold value, and then judges whether user is to pay a return visit user；Return visit can not only be improved
The discrimination of user identification arithmetic, while the False Rate of identification can also be reduced.
Detailed description of the invention
Fig. 1 is method general flow chart of the invention.
Fig. 2 is the fingerprint recognition rate under different diversity factor threshold values of the invention.
Fig. 3 is the fingerprint False Rate under different diversity factor threshold values of the invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and embodiments.
Fig. 1 is please referred to, the present invention provides a kind of return visit user identification arithmetic based on browser fingerprint diversity factor, feature
It is, comprising the following steps:
Step S1: acquisition fingerprint characteristic parameter simultaneously makes fingerprint using hash function；
Step S2: judge that the fingerprint whether there is in fingerprint base, and if it exists, determine the fingerprint for the finger of return visit user
Otherwise line carries out step S3；
Step S3: the global disparity angle value of all fingerprints in the fingerprint and fingerprint base is calculated one by one；
Step S4: a certain global disparity angle value is less than threshold value (threshold value is 0.025,0.05 or 0.1) if it exists, then sentences
The fixed fingerprint is the upgrading fingerprint for paying a return visit user, and existing fingerprint corresponding with the global disparity angle value in fingerprint base is updated
For the upgrading fingerprint；Otherwise determine that the fingerprint is the fingerprint of new user, and the fingerprint is stored in fingerprint base.
Further, the calculation method of the global disparity angle value is as follows:
Wherein, D (BF1,BF2) indicate fingerprint BF1With fingerprint BF2Global disparity angle value, value interval be [0,1], if referring to
Line BF1With fingerprint BF2Completely the same, functional value 0, functional value is 1 if the two is entirely different；d(attri(BF1,BF2)) table
Show characteristic parameter diversity factor, when i takes 1 to 8, it is poor to respectively represent User agent diversity factor, plugin information diversity factor, font information
Different degree, screen resolution diversity factor, time zones differences degree, HTTP Accept diversity factor and Cookie diversity factor；Indicate each
The weight of characteristic parameter diversity factor, value are the comentropy of character pair parameter.
Further, the User agent diversity factor is by browser related data diversity factor and device architecture difference
Spend two parts composition:
d(attr1(BF1,BF2))=0.5 × Fbr+0.5×Farchi
Wherein, FbrFor browser related data diversity factor, FarchiFor device architecture diversity factor,To refer to
Line BF1Browser title,For fingerprint BF2Browser title,For fingerprint BF1Browser version
Number,For fingerprint BF2Browser version number；d(attr1(BF1,BF2)) it is fingerprint BF1With fingerprint BF2User
Agent diversity factor；Fingerprint BF1With fingerprint BF2Browser title and when completely the same version number, FbrValue is 0；If browser
Title is identical and version number is inconsistent, FbrThen value is 0.125；If the two is entirely different, value 1；FarchiValue principle
With FbrIt is identical.
Diversity factor function d (attr1(BF1,BF2)) value interval be [0,1].If fingerprint BF1With fingerprint BF2User
Agent is identical, and functional value is then 0, if the User agent of two fingerprints is entirely different, functional value 1.
Further, the circular of the plugin information diversity factor is as follows: enabling fingerprint BF1Plug-in unit list be LP
(BF1), fingerprint BF2Plug-in unit list be LP (BF2), then:
FU=| (LP (BF1)\(LP(BF1)∩LP(BF2)))∪LP(BF2)|
Wherein, FU is fingerprint BF1With fingerprint BF2Two plug-in unit lists in plug-in unit sum；
For fingerprint BF1With fingerprint BF2, the ratio of distinctive plug-in unit in two plug-in unit lists is calculated, result uses F respectively1With
F2It indicates, solution procedure is as follows:
Wherein, LP=name ,=verPlugin name and the consistent plug-in unit number of plug-in version number in indication plug unit list LP,
LP=name, ≠ verPlugin name is identical in indication plug unit list LP, the different plug-in unit number of plug-in version number；
Calculate LP (BF1) and LP (BF2) plugin name is identical in two plug-in unit lists, the different plug-in unit ratio of plug-in version number
And use F3It indicates, solution procedure is as follows:
Calculate LP (BF1) and LP (BF2) all identical plug-in unit ratio of plugin name and version number and table is used in two plug-in unit lists
Show F4, solution procedure is as follows:
For fingerprint BF1With fingerprint BF2, plugin information diversity factor d (attr2(BF1,BF2)) solution it is as follows:
Diversity factor function d (attr2(BF1,BF2)) value interval be [0,1].If fingerprint BF1With BF2Plug-in unit list it is complete
Identical, difference angle value is then 0, if the plugin information of two fingerprints is entirely different, difference angle value is 1.
Further, the circular of the font information diversity factor is as follows: enabling fingerprint BF1List of fonts be LF
(BF1), fingerprint BF2List of fonts be LF (BF2), then:
FU=| (LF (BF1)\(LF(BF1)∩LF(BF2)))∪LF(BF2)|
Wherein, FU fingerprint BF1With fingerprint BF2Two list of fonts in font sum；
List of fonts LF (BF1) and list of fonts LF (BF2) in the ratio of distinctive font use F respectively1And F2It indicates, asks
Solution preocess is as follows:
List of fonts LF (BF1) and list of fonts LF (BF2) in same font ratio F3It indicates, solution procedure is such as
Under:
For fingerprint BF1With fingerprint BF2, font information diversity factor d (attr3(BF1,BF2)) solution it is as follows:
Diversity factor function d (attr3(BF1),attr3(BF2)) value interval be [0,1].
Further, the screen resolution diversity factor, time zones differences degree, HTTP Accept diversity factor, video difference
Degree and the circular of Cookie diversity factor are as follows:
Wherein, x is natural number and 4≤x≤8；As x=4, d (attr4(BF1,BF2)) indicate screen resolution difference
Degree, attr4(BF1) indicate fingerprint BF1The screen resolution of middle characteristic parameter, attr4(BF2) indicate fingerprint BF2Screen
Resolution ratio；As x=5, d (attr5(BF1,BF2)) indicate time zones differences degree, attr5(BF1) indicate fingerprint BF1Time zone ginseng
Number, attr5(BF2) indicate fingerprint BF2Time zone parameter；Work as x=6, d (attr6(BF1,BF2)), indicate HTTP between fingerprint
Accept diversity factor, attr6(BF1) indicate fingerprint BF1HTTP Accept parameter, attr6(BF2) indicate fingerprint BF2HTTP
Accept parameter；As x=7, d (attr7(BF1,BF2)) indicate video diversity factor between fingerprint, attr7(BF1) indicate fingerprint
BF1Video parameter, attr7(BF2) indicate fingerprint BF2Video parameter；As x=8, d (attr8(BF1,BF2)) indicate
Cookie diversity factor, attr8(BF1) indicate fingerprint BF1Cookie parameter, attr8(BF2) fingerprint BF2Cookie parameter.Difference
Different degree function d (attrx(BF1,BF2)) value interval be [0,1]
Further, the calculation method of the comentropy of the characteristic parameter is as follows: enabling a browser fingerprint algorithm is BF
() gives a new browser information x, generates a browser fingerprint BF (x), the discrete probabilistic of the browser fingerprint is close
Degree function is P (fn),n∈[0,1,...,N]；Firstly, introducing the concept of self-information I, it is defined as follows shown:
I (BF (x)=fn)=- log (P (fn))
Wherein, self-information amount I characterizes the bit number that the browser fingerprint includes information.
P(fn) comentropy H (BF) be browser fingerprint self-information amount desired value, be defined as follows shown in:
Wherein, the value of H (BF) is bigger, and the accuracy for distinguishing different browsers is higher.
Browser fingerprint is composed of different characteristic parameters, and the information content of each characteristic parameter is individually discussed and is determined
The comentropy of adopted fingerprint characteristic parameter.If some fingerprint characteristic parameter is a, self-information amount and comentropy calculation method are respectively such as
Shown in lower.
I(fn,a)=- log (P (fn,a))
Characteristic parameter mutually independent for two, the calculating of self-information amount can be according to the direct linear, additive of formula.
In order to allow those skilled in the art to better understand technical solution of the present invention, below in conjunction with threshold value three values into
Row further illustrates.
In Fig. 2, what three curves indicated is diversity factor threshold value difference value when being 0.025,0.05 and 0.1, and fingerprint is known
Not rate changes over time situation.The time interval of user's access is longer according to the experimental results, and the discrimination for paying a return visit user is lower,
The reason of generating the result is that the amplitude of variation of fingerprint characteristic parameter increases as user pays a return visit interval time growth.Wherein,
The setting of diversity factor threshold value affects the discrimination for paying a return visit user.The fingerprint diversity factor threshold value of setting is bigger, then it represents that allows
Difference degree between fingerprint is bigger, i.e. the accuracy of return visit user identification is lower.When fingerprint diversity factor threshold value is set as
The discrimination of user is paid a return visit when 0.05, when time interval is one month up to 86% or more.
False Rate is that another index of the algorithm implementation effect is assessed in this experiment.Wherein, what False Rate indicated is to return
Visit the probability of the wrong identification occurred in user's identification process.The setting of diversity factor threshold value not only will affect the identification for paying a return visit user
Rate also will affect False Rate.Wherein, the setting of diversity factor threshold value is larger, can be effectively reduced the False Rate of user's identification, still
The accuracy rate of identification can also decrease.And the setting of diversity factor threshold value is less than normal, can effectively improve the discrimination of user, but same
When also increase False Rate.For under the different diversity factor threshold condition in 0.025,0.05 and 0.1 3 kind, statistics pays a return visit user
The False Rate of identification, experimental result are as shown in Figure 3.
Fig. 3 shows under three kinds of different diversity factor threshold conditions, in one month, pays a return visit the False Rate of user's identification
Interval increases and increases at any time, but the False Rate identified is all below 7%.
In conclusion the setting of diversity factor threshold value and the calculating of fingerprint diversity factor are most important in return visit user identification arithmetic
Two links.Select diversity factor threshold value appropriate that can not only improve the discrimination for paying a return visit user identification arithmetic, while can also
Reduce the False Rate of identification.
The foregoing is merely presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification, is all covered by the present invention.
Claims (7)
1. a kind of return visit user identification arithmetic based on browser fingerprint diversity factor, which comprises the following steps:
Step S1: acquisition fingerprint characteristic parameter simultaneously makes fingerprint using hash function；
Step S2: judge that the fingerprint whether there is in fingerprint base, and if it exists, determine the fingerprint for pay a return visit user fingerprint,
Otherwise step S3 is carried out；
Step S3: the global disparity angle value of all fingerprints in the fingerprint and fingerprint base is calculated one by one；The global disparity angle value
Calculation method it is as follows:
Wherein, D (BF1,BF2) indicate fingerprint BF1With fingerprint BF2Global disparity angle value；d(attri(BF1,BF2)) indicate feature
Parameter differences degree, when i takes 1 to 8, respectively represent User agent diversity factor, plugin information diversity factor, font information diversity factor,
Screen resolution diversity factor, time zones differences degree, HTTP Accept diversity factor, video diversity factor and Cookie diversity factor；Table
Show that the weight of each characteristic parameter diversity factor, value are the comentropy of character pair parameter；
Step S4: if it exists a certain global disparity angle value be less than threshold value, then determine the fingerprint for pay a return visit user upgrading fingerprint,
And existing fingerprint corresponding with the global disparity angle value in fingerprint base is updated to the upgrading fingerprint；Otherwise determine the fingerprint
For the fingerprint of new user, and the fingerprint is stored in fingerprint base.
2. the return visit user identification arithmetic according to claim 1 based on browser fingerprint diversity factor, it is characterised in that: institute
User agent diversity factor is stated to be made of browser related data diversity factor and device architecture diversity factor two parts:
d(attr1(BF1,BF2))=0.5 × Fbr+0.5×Farchi
Wherein, FbrFor browser related data diversity factor, FarchiFor device architecture diversity factor,For fingerprint BF1
Browser title,For fingerprint BF2Browser title,For fingerprint BF1Browser version number,For fingerprint BF2Browser version number；d(attr1(BF1,BF2)) it is fingerprint BF1With fingerprint BF2User agent
Diversity factor.
3. the return visit user identification arithmetic according to claim 1 based on browser fingerprint diversity factor, it is characterised in that: institute
The circular for stating plugin information diversity factor is as follows: enabling fingerprint BF1Plug-in unit list be LP (BF1), fingerprint BF2Plug-in unit
List is LP (BF2), then:
FU=| (LP (BF1)\(LP(BF1)∩LP(BF2)))∪LP(BF2)|
Wherein, FU is fingerprint BF1With fingerprint BF2Two plug-in unit lists in plug-in unit sum；
For fingerprint BF1With fingerprint BF2, the ratio of distinctive plug-in unit in two plug-in unit lists is calculated, result uses F respectively1And F2Table
Show, solution procedure is as follows:
Wherein, LP=name ,=verPlugin name and the consistent plug-in unit number of plug-in version number, LP in indication plug unit list LP=name, ≠ ver
Plugin name is identical in indication plug unit list LP, the different plug-in unit number of plug-in version number；
Calculate LP (BF1) and LP (BF2) plugin name is identical in two plug-in unit lists, the different plug-in unit ratio of plug-in version number is used in combination
F3It indicates, solution procedure is as follows:
Calculate LP (BF1) and LP (BF2) all identical plug-in unit ratio of plugin name and version number and with indicating in two plug-in unit lists
F4, solution procedure is as follows:
For fingerprint BF1With fingerprint BF2, plugin information diversity factor d (attr2(BF1,BF2)) solution it is as follows:
4. the return visit user identification arithmetic according to claim 1 based on browser fingerprint diversity factor, it is characterised in that: institute
The circular for stating font information diversity factor is as follows: enabling fingerprint BF1List of fonts be LF (BF1), fingerprint BF2Font
List is LF (BF2), then:
FU=| (LF (BF1)\(LF(BF1)∩LF(BF2)))∪LF(BF2)|
Wherein, FU fingerprint BF1With fingerprint BF2Two list of fonts in font sum；
List of fonts LF (BF1) and list of fonts LF (BF2) in the ratio of distinctive font use F respectively1And F2It indicates, solution procedure
It is as follows:
List of fonts LF (BF1) and list of fonts LF (BF2) in same font ratio F3It indicates, solution procedure is as follows:
For fingerprint BF1With fingerprint BF2, font information diversity factor d (attr3(BF1,BF2)) solution it is as follows:
5. the return visit user identification arithmetic according to claim 1 based on browser fingerprint diversity factor, it is characterised in that: institute
State the tool of screen resolution diversity factor, time zones differences degree, HTTP Accept diversity factor, video diversity factor and Cookie diversity factor
Body calculation method is as follows:
Wherein, x is natural number and 4≤x≤8；As x=4, d (attr4(BF1,BF2)) indicate screen resolution diversity factor,
attr4(BF1) indicate fingerprint BF1The screen resolution of middle characteristic parameter, attr4(BF2) indicate fingerprint BF2Screen resolution；
As x=5, d (attr5(BF1,BF2)) indicate time zones differences degree, attr5(BF1) indicate fingerprint BF1Time zone parameter, attr5
(BF2) indicate fingerprint BF2Time zone parameter；Work as x=6, d (attr6(BF1,BF2)), indicate HTTP Accept diversity factor, attr6
(BF1) indicate fingerprint BF1HTTP Accept parameter, attr6(BF2) indicate fingerprint BF2HTTP Accept parameter；Work as x=7
When, d (attr7(BF1,BF2)) indicate video diversity factor, attr7(BF1) indicate fingerprint BF1Video parameter, attr7(BF2)
Indicate fingerprint BF2Video parameter；As x=8, d (attr8(BF1,BF2)) indicate Cookie diversity factor, attr8(BF1) table
Show fingerprint BF1Cookie parameter, attr8(BF2) fingerprint BF2Cookie parameter.
6. the return visit user identification arithmetic according to claim 1 based on browser fingerprint diversity factor, it is characterised in that: institute
The calculation method for stating the comentropy of characteristic parameter is as follows: enabling a browser fingerprint algorithm is BF (), give one it is new clear
It lookes at device information x, generates a browser fingerprint BF (x), discrete probability density function is P (fn),n∈[0,1,...,N]；Then
Comentropy, comentropy are asked for characteristic parameter a are as follows:
Wherein, fn=BF (x) indicates a browser fingerprint.
7. the return visit user identification arithmetic according to claim 1 based on browser fingerprint diversity factor, it is characterised in that: institute
Stating threshold value is 0.025,0.05 or 0.1.
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| CN106951765A (en) * | 2017-03-31 | 2017-07-14 | 福建北卡科技有限公司 | A kind of zero authority mobile device recognition methods based on browser fingerprint similarity |
| CN108471398A (en) * | 2018-02-01 | 2018-08-31 | 四川大学 | A kind of network device management method and system |
| CN108364022B (en) * | 2018-02-09 | 2020-07-28 | 杭州默安科技有限公司 | Cross-browser equipment identification method based on machine learning analysis fingerprint similarity |
| CN109309664B (en) * | 2018-08-14 | 2021-03-23 | 中国科学院数据与通信保护研究教育中心 | Browser fingerprint detection behavior monitoring method |
| WO2022041261A1 (en) * | 2020-08-31 | 2022-03-03 | 苏州大成电子科技有限公司 | Fingerprint recognition method for use in rail transit device |
| CN114943024B (en) * | 2022-05-31 | 2023-04-25 | 北京永信至诚科技股份有限公司 | Fingerprint acquisition method and device based on browser |
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