CN109861955A - A kind of anti-private of traffic characteristic connects method - Google Patents
A kind of anti-private of traffic characteristic connects method Download PDFInfo
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- CN109861955A CN109861955A CN201811019801.7A CN201811019801A CN109861955A CN 109861955 A CN109861955 A CN 109861955A CN 201811019801 A CN201811019801 A CN 201811019801A CN 109861955 A CN109861955 A CN 109861955A
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Abstract
The invention discloses a kind of anti-privates of traffic characteristic to connect method, includes the following steps: the definition of (1) rule: arranging the data class read from kafka first, and when Kstream application starts, rule definition configuration is loaded into memory；(2) user conversation is safeguarded: the polymerization analysis of data is carried out to the session of user, the user's going on line or off line data listened to are sent kafka by Monitor application, these data are consumed in Kstream application；(3) data characteristics obtains: Kstream application compares the source address ip of the ip and mirror image data message in the online message of user first, if the same shows that the data message is that thus user generates, and is then defined according to rule.The present invention by obtaining the session information of user from aaa server or AAA proxy server, meanwhile, in conjunction with the uplink traffic issued from network egress mirror user, carry out dialogue-based content analysis, it obtains in the session, if there is the private conclusion for connecing behavior, it is good that anti-private connects effect.
Description
Technical field
The present invention relates to hair data analysis technique field, specially a kind of anti-private of traffic characteristic connects method.
Background technique
As the Large scale construction and development of Mobile Internet technology of internet, business are grown rapidly, network traffic data is quick-fried
Hairdo increases, and intelligent terminal becomes increasingly popular, and people increasingly be unable to do without network.In the populated areas such as school and villages within the city,
A large number of users drags N number of terminal situation serious using router access online, an account, and such case is not only transported to broadband services
Battalion brings certain pressure, reduces the income of operator, but also conceal the true identity of Internet user, leads to the network information
Safety can not be managed.
New road is dedicated to anti-private connection function research and development, provides the solution for being connected in basis with the anti-private of client, there is 20
The deployment of multiple provincial operators, maintenance, upgrading experience track all kinds of private catcher sections throughout the year, and provide targetedly function liter
Grade.It is limited to the factors such as access protocol, mobile phone operating system, the anti-private of client connects that there are some insurmountable problems, such as
The packet capturing under MAC+IP address reproduction, pppoe environment under Portal environment cracks, for this purpose, it is proposed that a kind of flow is special
It levies anti-private and connects method.
Summary of the invention
The purpose of the present invention is to provide a kind of anti-privates of traffic characteristic to connect method, mentioned above in the background art to solve
Problem.
To achieve the above object, the invention provides the following technical scheme: a kind of anti-private of traffic characteristic connects method, including it is as follows
Step:
(1) rule definition: arranging the data class read from kafka first, will be regular when Kstream application starts
Definition configuration is loaded into memory；
(2) user conversation is safeguarded: the polymerization analysis of data is carried out to the session of user, Monitor application will listen to
User's going on line or off line data are sent to kafka, these data are consumed in Kstream application；
(3) data characteristics obtains: Kstream application compares ip and mirror image data message in the online message of user first
Source address ip if the same shows that the data message is that thus user generates, and is then defined according to rule, by data message
In field carry out json parsing, url parsing or user_agent parsing etc., obtain characteristic value be stored in redis；
(4) private connects judgement: brand priority with higher, and the equipment different for Direct Recognition, other are classified,
In one session, after characteristic value polymerization, judge that private connects by specified conditions；
(5) subsequent control: being determined the private session connect, be forced it is offline, when some user is within the scope of certain time, hair
Raw multiple private connects behavior, then is added into blacklist or carries out account freezing processing.
Preferably, agreement is read from the http.request field in alipay.amdc rule in the step (1)
The value of these characteristic attributes of utdid, osType, clientVersion, uid, these features according to device, ostype, osv,
These classification of appinfo, accountinfo come tissue, different mode classifications, the method difference polymerizeing in analysis.
Preferably, the polymerization analysis of data is carried out when user conversation starts in the step (2), when user offline,
Data need not be received.
Preferably, data acquisition is carried out using flow analysis system in the step (2), and flow analysis system mainly divides
It is responsible for monitoring users limit information up and down for three subsystems: Monitor；Kstream is responsible for aggregated data and analyzes and export result；
Manager is responsible for definition and the tissue of perdurable data and data analyst coverage, provides data-interface to show.
Preferably, the flow analysis system is obtained firstly the need of the mirror image data by BRAS, the acquisition of data according to rule
It takes, while listening for the upper offline information that aaa server is sent, management service defines number according to NasIp, IP address section and collector
According to the range of acquisition.
Preferably, the flow analysis system can be compatible with the anti-private of original client of new road and practice midwifery product, cooperate original
Client is connect using the anti-private of realization.
Preferably, characteristic value by following characteristics carries out classification preservation in the step (3):
Device: the same account of same application has identical value；
Ostype: operating system classification windows, ios, android, mac；
Osv: operating system version；
Brand: mobile phone or ardware model number；
Appinfo: the version of application；
Accountinfo: the account of some application of user.
Preferably, other in the step (4) are classified, and in a session, after characteristic value polymerization, are judged by the following conditions
Private connects:
A, the different characteristic value in Brand classification, more than one；
B, the different characteristic value in Ostype classification, more than one；
C, specially treated is not done in other classification, the characteristic value under different rules, is likely to more than one, when having at least
When rule as two occurs, determine that private connects.
Compared with prior art, the beneficial effects of the present invention are: usage scenario of the present invention is extensive, either PPPOE dialing
Or Portal dialing either other dialing can be supported to detect；It accurately identifies private and connects behavior；Anti- to crack, strategy takes in rear end
Business device, good confidentiality avoid specific aim from analyzing or crack；Unified management, acquisition and analysis strategy can national synchronized update, anti-private
Strategy modification is connect to come into force immediately；Private, which connects, to look into, and be able to record that user's private connects processed reason, evidence-based；Regional management,
Carrier-class is supported to be managed for some school；Data visualization, detailed data sheet are very clear；Deployment architecture
Simply, easy to transform, the original authentication and accounting System in campus is not influenced；Network flow acquisition and recognition strategy are succinctly efficient, can be with
Lower cost supports a large number of users.
Detailed description of the invention
Fig. 1 is inventive network deployment architecture figure；
Fig. 2 is application architecture figure of the present invention；
Fig. 3 is the regular definition interfaces figure of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The present invention provides a kind of technical solution: a kind of anti-private of traffic characteristic connects method, includes the following steps:
(1) rule definition: arranging the data class read from kafka first, will be regular when Kstream application starts
Definition configuration is loaded into memory；
(2) user conversation is safeguarded: the polymerization analysis of data is carried out to the session of user, Monitor application will listen to
User's going on line or off line data are sent to kafka, these data are consumed in Kstream application；
(3) data characteristics obtains: Kstream application compares ip and mirror image data message in the online message of user first
Source address ip if the same shows that the data message is that thus user generates, and is then defined according to rule, by data message
In field carry out json parsing, url parsing or user_agent parsing etc., obtain characteristic value be stored in redis；
(4) private connects judgement: brand priority with higher, and the equipment different for Direct Recognition, other are classified,
In one session, after characteristic value polymerization, judge that private connects by specified conditions；
(5) subsequent control: being determined the private session connect, be forced it is offline, when some user is within the scope of certain time, hair
Raw multiple private connects behavior, then is added into blacklist or carries out account freezing processing.
Embodiment one:
First part: rule definition
Arrange the data class read from kafka first, when Kstream application starts, rule is defined into configuration load
Into memory.
Second part: user conversation maintenance
The polymerization analysis of data is carried out to the session of user, the user's going on line or off line data listened to are sent out in Monitor application
It is sent to kafka, these data are consumed in Kstream application.
Part III: data characteristics obtains
Kstream application compares the source address ip of the ip and mirror image data message in the online message of user first, if phase
It is same then show that the data message is that thus user generates, it is then defined according to rule, the field in data message is carried out
Json parsing, url parsing or user_agent parsing etc. obtain characteristic value and are stored in redis.
Part IV: private connects judgement
Brand priority with higher, the equipment different for Direct Recognition, other classification are in a session, special
After value indicative polymerization, judge that private connects by specified conditions.
Part V: subsequent control
It is determined the private session connect, is forced offline, when some user is within the scope of certain time, multiple private occurs and connects row
To be then added into blacklist or carrying out account freezing processing.
Embodiment two:
First part: rule definition
Arrange the data class read from kafka first, when Kstream application starts, rule is defined into configuration load
Into memory, arrange to read utdid, osType from the http.request field in alipay.amdc rule,
The value of these characteristic attributes of clientVersion, uid, these features according to device, ostype, osv, appinfo,
These classification of accountinfo carry out tissue, and different mode classifications, the method polymerizeing in analysis is different, which defines
It is to go to obtain some specific user characteristics value from what application, characteristic value represents individual subscriber feature, such as equipment type
Number, OS Type, each application is directed to the feature field etc. of user.
Second part: user conversation maintenance
The polymerization analysis of data is carried out to the session of user, the user's going on line or off line data listened to are sent out in Monitor application
It is sent to kafka, these data are consumed in Kstream application.
Part III: data characteristics obtains
Kstream application compares the source address ip of the ip and mirror image data message in the online message of user first, if phase
It is same then show that the data message is that thus user generates, it is then defined according to rule, the field in data message is carried out
Json parsing, url parsing or user_agent parsing etc. obtain characteristic value and are stored in redis.
Part IV: private connects judgement
Brand priority with higher, the equipment different for Direct Recognition, other classification are in a session, special
After value indicative polymerization, judge that private connects by specified conditions.
Part V: subsequent control
It is determined the private session connect, is forced offline, when some user is within the scope of certain time, multiple private occurs and connects row
To be then added into blacklist or carrying out account freezing processing.
Embodiment three:
First part: rule definition
Arrange the data class read from kafka first, when Kstream application starts, rule is defined into configuration load
Into memory, arrange to read utdid, osType from the http.request field in alipay.amdc rule,
The value of these characteristic attributes of clientVersion, uid, these features according to device, ostype, osv, appinfo,
These classification of accountinfo carry out tissue, and different mode classifications, the method polymerizeing in analysis is different, which defines
It is to go to obtain some specific user characteristics value from what application, characteristic value represents individual subscriber feature, such as equipment type
Number, OS Type, each application is directed to the feature field etc. of user.
Second part: user conversation maintenance
The polymerization analysis of data is carried out to the session of user, the user's going on line or off line data listened to are sent out in Monitor application
It is sent to kafka, Kstream application consumes these data and carries out the polymerization analysis of data when user conversation starts, work as user
When offline, it is not necessary to receive data, carry out data acquisition using flow analysis system, and flow analysis system is broadly divided into three sons
System: Monitor is responsible for monitoring users limit information up and down；Kstream is responsible for aggregated data and analyzes and export result；Manager
It is responsible for definition and the tissue of perdurable data and data analyst coverage, provides data-interface to show, flow analysis system is first
The mirror image data by BRAS is first needed, the acquisition of data is according to Rule, the upper offline letter sent while listening for aaa server
Breath, for management service according to NasIp, IP address section and collector define the range of data acquisition；Flow analysis system can be compatible with
The new anti-private of the original client in road is practiced midwifery product, cooperates original client using realizing that anti-private connects.
Part III: data characteristics obtains
Kstream application compares the source address ip of the ip and mirror image data message in the online message of user first, if phase
It is same then show that the data message is that thus user generates, it is then defined according to rule, the field in data message is carried out
Json parsing, url parsing or user_agent parsing etc. obtain characteristic value and are stored in redis.
Part IV: private connects judgement
Brand priority with higher, the equipment different for Direct Recognition, other classification are in a session, special
After value indicative polymerization, judge that private connects by specified conditions.
Part V: subsequent control
It is determined the private session connect, is forced offline, when some user is within the scope of certain time, multiple private occurs and connects row
To be then added into blacklist or carrying out account freezing processing.
Example IV:
First part: rule definition
Arrange the data class read from kafka first, when Kstream application starts, rule is defined into configuration load
Into memory, arrange to read utdid, osType from the http.request field in alipay.amdc rule,
The value of these characteristic attributes of clientVersion, uid, these features according to device, ostype, osv, appinfo,
These classification of accountinfo carry out tissue, and different mode classifications, the method polymerizeing in analysis is different, which defines
It is to go to obtain some specific user characteristics value from what application, characteristic value represents individual subscriber feature, such as equipment type
Number, OS Type, each application is directed to the feature field etc. of user.
Second part: user conversation maintenance
The polymerization analysis of data is carried out to the session of user, the user's going on line or off line data listened to are sent out in Monitor application
It is sent to kafka, Kstream application consumes these data and carries out the polymerization analysis of data when user conversation starts, work as user
When offline, it is not necessary to receive data, carry out data acquisition using flow analysis system, and flow analysis system is broadly divided into three sons
System: Monitor is responsible for monitoring users limit information up and down；Kstream is responsible for aggregated data and analyzes and export result；Manager
It is responsible for definition and the tissue of perdurable data and data analyst coverage, provides data-interface to show, flow analysis system is first
The mirror image data by BRAS is first needed, the acquisition of data is according to Rule, the upper offline letter sent while listening for aaa server
Breath, for management service according to NasIp, IP address section and collector define the range of data acquisition；Flow analysis system can be compatible with
The new anti-private of the original client in road is practiced midwifery product, cooperates original client using realizing that anti-private connects.
Part III: data characteristics obtains
Kstream application compares the source address ip of the ip and mirror image data message in the online message of user first, if phase
It is same then show that the data message is that thus user generates, it is then defined according to rule, the field in data message is carried out
Json parsing, url parsing or user_agent parsing etc. obtain characteristic value and are stored in redis；
Characteristic value carries out classification preservation by following characteristics:
Device: the same account of same application has identical value；
Ostype: operating system classification windows, ios, android, mac；
Osv: operating system version；
Brand: mobile phone or ardware model number；
Appinfo: the version of application；
Accountinfo: the account of some application of user；
Because some rules directly specify use environment, it is possible to Direct Classification, for example define windows upgrading
Rule, then when windows upgrading data message occur when, can directly brand classification in addition windowspc set
It is standby, windows operating system can also be added in ostype classification.
Part IV: private connects judgement
Brand priority with higher, the equipment different for Direct Recognition, other classification are in a session, special
After value indicative polymerization, judge that private connects by specified conditions.
Part V: subsequent control
It is determined the private session connect, is forced offline, when some user is within the scope of certain time, multiple private occurs and connects row
To be then added into blacklist or carrying out account freezing processing.
Embodiment five:
First part: rule definition
Arrange the data class read from kafka first, when Kstream application starts, rule is defined into configuration load
Into memory, arrange to read utdid, osType from the http.request field in alipay.amdc rule,
The value of these characteristic attributes of clientVersion, uid, these features according to device, ostype, osv, appinfo,
These classification of accountinfo carry out tissue, and different mode classifications, the method polymerizeing in analysis is different, which defines
It is to go to obtain some specific user characteristics value from what application, characteristic value represents individual subscriber feature, such as equipment type
Number, OS Type, each application is directed to the feature field etc. of user.
Second part: user conversation maintenance
The polymerization analysis of data is carried out to the session of user, the user's going on line or off line data listened to are sent out in Monitor application
It is sent to kafka, Kstream application consumes these data and carries out the polymerization analysis of data when user conversation starts, work as user
When offline, it is not necessary to receive data, carry out data acquisition using flow analysis system, and flow analysis system is broadly divided into three sons
System: Monitor is responsible for monitoring users limit information up and down；Kstream is responsible for aggregated data and analyzes and export result；Manager
It is responsible for definition and the tissue of perdurable data and data analyst coverage, provides data-interface to show, flow analysis system is first
The mirror image data by BRAS is first needed, the acquisition of data is according to Rule, the upper offline letter sent while listening for aaa server
Breath, for management service according to NasIp, IP address section and collector define the range of data acquisition；Flow analysis system can be compatible with
The new anti-private of the original client in road is practiced midwifery product, cooperates original client using realizing that anti-private connects.
Part III: data characteristics obtains
Kstream application compares the source address ip of the ip and mirror image data message in the online message of user first, if phase
It is same then show that the data message is that thus user generates, it is then defined according to rule, the field in data message is carried out
Json parsing, url parsing or user_agent parsing etc. obtain characteristic value and are stored in redis；
Characteristic value carries out classification preservation by following characteristics:
Device: the same account of same application has identical value；
Ostype: operating system classification windows, ios, android, mac；
Osv: operating system version；
Brand: mobile phone or ardware model number；
Appinfo: the version of application；
Accountinfo: the account of some application of user；
Because some rules directly specify use environment, it is possible to Direct Classification, for example define windows upgrading
Rule, then when windows upgrading data message occur when, can directly brand classification in addition windowspc set
It is standby, windows operating system can also be added in ostype classification.
Part IV: private connects judgement
Brand priority with higher, the equipment different for Direct Recognition, other classification are in a session, special
After value indicative polymerization, judge that private connects by the following conditions:
A, the different characteristic value in Brand classification, more than one；
B, the different characteristic value in Ostype classification, more than one；
C, specially treated is not done in other classification, the characteristic value under different rules, is likely to more than one, when having at least
When rule as two occurs, determine that private connects.
Part V: subsequent control
It is determined the private session connect, is forced offline, when some user is within the scope of certain time, multiple private occurs and connects row
To be then added into blacklist or carrying out account freezing processing.
The present invention by obtaining the session information of user from aaa server or AAA proxy server, meanwhile, in conjunction with from
The uplink traffic that network egress mirror user issues, carries out dialogue-based content analysis, obtains in the session, if exist
Private connects the conclusion of behavior, this anti-private connection technology can independently dispose operation independent of original client, uses net in user
The access authentications modes such as page certification, wechat certification, still are able to guarantee that anti-private connects effect.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
A variety of variations, modification, replacement can be carried out to these embodiments without departing from the principles and spirit of the present invention by understanding
And modification, the scope of the present invention is defined by the appended.
Claims (8)
1. a kind of anti-private of traffic characteristic connects method, characterized by the following steps:
(1) rule definition: arranging the data class read from kafka first, and when Kstream application starts, rule is defined
Configuration is loaded into memory；
(2) user conversation is safeguarded: the polymerization analysis of data, the user that Monitor application will listen to are carried out to the session of user
Upper and lower line number consumes these data according to kafka, Kstream application is sent to；
(3) data characteristics obtains: Kstream application with comparing the source of the ip and mirror image data message in the online message of user first
Location ip if the same shows that the data message is that thus user generates, and is then defined according to rule, will be in data message
Field carries out json parsing, and url parsing or user_agent parsing etc. obtain characteristic value and be stored in redis；
(4) private connects judgement: brand priority with higher, the equipment different for Direct Recognition, other classification, at one
In session, after characteristic value polymerization, judge that private connects by specified conditions；
(5) subsequent control: being determined the private session connect, is forced offline, when some user is within the scope of certain time, occurs more
Secondary private connects behavior, then is added into blacklist or carries out account freezing processing.
2. the anti-private of a kind of traffic characteristic according to claim 1 connects method, it is characterised in that: agreement in the step (1)
Read utdid, osType, clientVersion from the http.request field in alipay.amdc rule, uid these
The value of characteristic attribute, these features carry out tissue according to device, ostype, osv, these classification of appinfo, accountinfo,
Different mode classifications, the method polymerizeing in analysis are different.
3. the anti-private of a kind of traffic characteristic according to claim 1 connects method, it is characterised in that: in the step (2) when with
When the session start of family, the polymerization analysis of data is carried out, when user offline, it is not necessary to receive data.
4. the anti-private of a kind of traffic characteristic according to claim 1 connects method, it is characterised in that: used in the step (2)
Flow analysis system carries out data acquisition, and flow analysis system is broadly divided into three subsystems: Monitor is responsible for monitoring users
Upper and lower limit information；Kstream is responsible for aggregated data and analyzes and export result；Manager is responsible for perdurable data and data analysis
The definition of range and tissue provide data-interface to show.
5. the anti-private of a kind of traffic characteristic according to claim 4 connects method, it is characterised in that: the flow analysis system is first
The mirror image data by BRAS is first needed, the acquisition of data is according to Rule, the upper offline letter sent while listening for aaa server
Breath, for management service according to NasIp, IP address section and collector define the range of data acquisition.
6. the anti-private of a kind of traffic characteristic according to claim 5 connects method, it is characterised in that: the flow analysis system energy
It is enough compatible with the anti-private of original client of new road to practice midwifery product, cooperates original client using realizing that anti-private connects.
7. the anti-private of a kind of traffic characteristic according to claim 1 connects method, it is characterised in that: feature in the step (3)
Value carries out classification preservation by following characteristics:
Device: the same account of same application has identical value；
Ostype: operating system classification windows, ios, android, mac；
Osv: operating system version；
Brand: mobile phone or ardware model number；
Appinfo: the version of application；
Accountinfo: the account of some application of user.
8. the anti-private of a kind of traffic characteristic according to claim 1 connects method, it is characterised in that: other in the step (4)
Classification after characteristic value polymerization, judges that private connects by the following conditions in a session:
A, the different characteristic value in Brand classification, more than one；
B, the different characteristic value in Ostype classification, more than one；
C, specially treated is not done in other classification, the characteristic value under different rules, is likely to more than one, when having at least two
When such rule occurs, determine that private connects.
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| CN111737102A (en) * | 2020-08-21 | 2020-10-02 | 北京志翔科技股份有限公司 | Safety early warning method and computer readable storage medium |
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Application publication date: 20190607 |