CN103152387B - A kind of apparatus and method obtaining HTTP user behavior track - Google Patents
A kind of apparatus and method obtaining HTTP user behavior track Download PDFInfo
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Abstract
The invention discloses a kind of device obtaining HTTP user behavior track, identify extraction module identification HTTP request message, extract the URL information in HTTP request message and Referer information, and store described URL information and Referer information to contingency table; Analysis module generates directed graph according to the points relationship of contingency table record, filters out the HTTP request that browser initiates automatically, form HTTP user behavior trajectory diagram and also export output module to according to directed graph; Output module exports HTTP user behavior trajectory diagram.The invention also discloses a kind of method obtaining HTTP user behavior track simultaneously, utilize the present invention can reduce taking network device computes and storage resources, reduce the impact on Internet Transmission.
Description
Technical field
The present invention relates to network technology, be specifically related to a kind of apparatus and method obtaining HTML (Hypertext Markup Language) (HTTP, HyperTextTransportProtocol) user behavior track.
Background technology
Along with popularizing of network, increasing user hankers after the change being understood society by network.By linking (referred to as surfing the Net) of client and the Internet, user can obtain the information that self needs.User is in upper network process; usual meeting initiatively be clicked and browse oneself interested webpage; often a complete webpage comprises a large amount of resource links, and as advertisement link, web page frame link (as picture), and the such resource link of advertisement, picture is not the interested part of user.For this phenomenon, telecom operators can utilize data analysing method to analyze the webpage that user in the scheduled time surfed the web, and then filter out the uninterested web page interlinkage of user, i.e. the web page interlinkage automatically initiated of browser; Retain the interested web page interlinkage of user, i.e. the web page interlinkage of user's active click; And carry out data mining according to the result analyzed, such as, from the web page interlinkage retained, sum up user like browsing which webpage, which webpage more by the welcome etc. of users.Here, by filtering out web page interlinkage that browser initiates automatically, retain the process of the web page interlinkage that user initiatively clicks and be called and obtain HTTP user behavior track.
Give an example, as shown in Figure 1, when user clicks Sina homepage sina.com.cn, browser sends HTTP request message to server, and server returns the http response message of this HTTP request message; According to described http response message, browser by automatically initiating the HTTP request of related resources linking, as xxxx.ad.sina.com.cn, yyyy.ad.sina.com.cn etc.; User, after opening Sina's homepage, clicks edu.sina.com.cn again, and browser can send the HTTP request message of edu.sina.com.cn to server.
In above-mentioned situation, both had the resource link that user initiatively clicks, also had the resource link of the automatic initiation of browser, and adopted in prior art and with the following method the resource link that the resource link initiatively clicked, browser are initiated automatically is identified:
Browser is while receiving described http response message, store the asset link information in described http response message, all HTTP request messages that within the time pre-set (as 5 seconds) occur are analyzed, if analyze the URL(uniform resource locator) (URL in current HTTP request message, UniformResourceLocator) information is stored in described asset link information, then think that current HTTP request message is that browser is initiated automatically; If the URL information analyzed in current HTTP request message is not stored in described asset link information, then think that current HTTP request message is that user initiatively clicks generation.
Although the method adopted in prior art can filter out the resource link that most of browser is initiated automatically, retain the resource link that user initiatively clicks.But because network traffics are huge, the current HTTP request message of continual analysis, for the network equipment as the calculating of router, gateway etc. and storage resources bring larger expense, http response message has often been compressed and burst still more, and this brings serious challenge to the network equipment.
Summary of the invention
In view of this, main purpose of the present invention is to provide a kind of apparatus and method obtaining HTTP user behavior track, can reduce taking network device computes and storage resources, reduce the impact on Internet Transmission.
For achieving the above object, technical scheme of the present invention is achieved in that
The invention provides a kind of device obtaining HTML (Hypertext Markup Language) HTTP user behavior track, this device comprises: identify extraction module, analysis module and output module; Wherein,
Described identification extraction module, for identifying HTTP request message, extracts uniform resource position mark URL information in HTTP request message and with reference to address (Referer) information, and stores described URL information and Referer information to contingency table;
Described analysis module, generates directed graph for the points relationship according to described contingency table record, filters out according to directed graph the HTTP request that browser initiates automatically, forms HTTP user behavior trajectory diagram and also exports described output module to;
Described output module, for exporting HTTP user behavior trajectory diagram.
In such scheme, described identification extraction module is further used for:
Receive the Business Stream in network, deep packet inspection method is first utilized to identify HTTP message in described Business Stream, again according to http protocol standard, HTTP request message is identified in HTTP message, according to message content extracting rule, extract URL information and the Referer information of described HTTP request message, described URL information and Referer information are recorded in contingency table.
In such scheme, described analysis module filters out according to directed graph the HTTP request that browser initiates automatically and is:
Described analysis module wipes out many home nodes in directed graph and/or tip leaf node.
In such scheme, described analysis module also for:
Search whether containing HTML html element element file request in the HTTP raw requests of contingency table record, if can find, then delete described html element element file request.
Present invention also offers a kind of method obtaining HTTP user behavior track, described method comprises:
Identify HTTP request message, extract the URL information in HTTP request message and Referer information, and store described URL information and Referer information to contingency table;
Points relationship according to described contingency table record generates directed graph, filters out according to directed graph the HTTP request that browser initiates automatically, forms HTTP user behavior trajectory diagram and also exports.
In such scheme, described identification HTTP request message, extracts the URL information in HTTP request message and Referer information, and stores described URL information and Referer information to contingency table is:
Receive the Business Stream in network, deep packet inspection method is utilized to identify HTTP message in Business Stream, according to http protocol standard, identify HTTP request message, according to message content extracting rule, extract URL information and the Referer information of described HTTP request message, described URL information and Referer information are recorded in contingency table.
In such scheme, describedly filter out according to directed graph the HTTP request that browser initiates automatically and be:
Wipe out the many home nodes in directed graph and/or tip leaf node.
In such scheme, before the described points relationship according to described contingency table record becomes directed graph, described method also comprises:
Whether search in the HTTP raw requests of contingency table record containing html element element file request; If can find, then delete described html element element file request.
The apparatus and method of acquisition HTTP action trail provided by the invention, extract URL information and the Referer information of HTTP request message from the HTTP request message identified, and record URL information and Referer information in contingency table; Points relationship according to contingency table record generates directed graph; Filter out according to directed graph the HTTP request that browser initiates automatically, form HTTP user behavior trajectory diagram; HTTP user behavior trajectory diagram is exported.Utilize the present invention, only need identify HTTP request message, taking network device computes and storage resources can be reduced, reduce the impact on Internet Transmission.
Accompanying drawing explanation
Fig. 1 is the embodiment schematic diagram obtaining HTTP user behavior track in prior art;
Fig. 2 is the composition structural representation that the present invention obtains the device of HTTP user behavior track;
Fig. 3 (a) ~ (d) is for analysis module of the present invention is to the process schematic diagram of directed graph;
Fig. 4 is the realization flow schematic diagram that the present invention obtains the method for HTTP user behavior track;
Fig. 5 is the realization flow schematic diagram that the present invention obtains method one specific embodiment of HTTP user behavior track.
Embodiment
The invention provides a kind of device obtaining HTTP user behavior track, can be applicable to the network equipment, as router, gateway etc.
As shown in Figure 2, described device comprises: identify extraction module 10, analysis module 11 and output module 12; Wherein,
Described identification extraction module 10, for identifying HTTP request message, extracts the URL information in HTTP request message and Referer information, and stores described URL information and Referer information to contingency table;
Here, described Referer information belongs to a kind of HTTP relevant information; Described HTTP request message identifies from the network service flow received; Described extraction can be extracted according to the message content extracting rule pre-set.
Described analysis module 11, generates directed graph for the points relationship according to described contingency table record, filters out according to directed graph the HTTP request that browser initiates automatically, forms HTTP user behavior trajectory diagram and also exports described output module 12 to;
Here, HTTP request that browser initiates automatically is filtered out described in specifically: wipe out the many home nodes in directed graph and/or tip leaf node.
Described output module 12, for exporting HTTP user behavior trajectory diagram.
Described analysis module filters out HTTP request that browser initiates automatically by many home nodes of wiping out in directed graph or the tip leaf node wiped out in directed graph or the many home nodes wiped out in directed graph and tip leaf node simultaneously according to directed graph.
Further, described identification extraction module 10, for from the network service flow received, first utilizes deep packet inspection method to identify HTTP message in Business Stream; Again according to http protocol (RFC2616, RequestForComments2616) standard, in HTTP message, identify HTTP request message; Then, according to message content extracting rule, extract URL information and the Referer information of described HTTP request message, described URL information and Referer information are recorded in contingency table.
Wherein, described Business Stream comprises: Email, Streaming Media, equity link (P2P, PeertoPeer), the networking telephone (VOIP, VoiceOverInternetProtocol), webpage WEB etc.; Described message content extracting rule is what pre-set, can arrange flexibly according to the actual conditions of network.
Here, the form that the described URL information of explanation of giving an example and Referer information record in contingency table:
The URL information extracted in current HTTP request message is http://edu.sina.com.cn/kids/, Referer information is http://news.sina.com.cn/, so, with http://edu.sina.com.cn/kids/ in described contingency table " sensing " http://news.sina.com.cn/ " form record; namely point to the points relationship record of Referer information with URL information, show to jump to http://edu.sina.com.cn/kids/ by http://news.sina.com.cn/.Here, claim each points relationship recorded in described contingency table to be a HTTP raw requests, by record at least one HTTP raw requests in described contingency table; The HTTP raw requests recorded in described contingency table has the points relationship that URL information points to Referer information.
Further, described analysis module 11 searches the HTTP raw requests with identical URL information, different Referer information recorded in described contingency table; When finding, delete URL information identical in described HTTP raw requests, retain different Referer information, to distinguish different HTTP raw requests; Search not then, in contingency table, still point to the form record of Referer information with URL information.
The process that such scheme describes can also be seen as: described analysis module 11 generates directed graph according to the points relationship of described contingency table record, wipes out the process of the many home nodes in directed graph.
Below from directed graph angle, wipe out many home nodes in directed graph for described analysis module 11 and tip leaf node is described simultaneously.
The directed graph that Fig. 3 (a) generates according to the points relationship of described contingency table record for described analysis module 11; In Fig. 3 (a), each ellipse represents a node, and each node both may represent the HTTP request automatically initiated by browser, also may represent and initiatively be clicked and the HTTP request of generation by user.
Such as, in described contingency table, recording URL information is: three HTTP raw requests: NodeD001 of NodeD001 point to NodeC002, NodeD001 and point to NodeC003, NodeD001 sensing NodeC004.
For above-mentioned points relationship, generate node NodeC002, NodeC003, NodeC004 all by relation that node NodeD001 quotes.In directed graph, two and two or more node are quoted by same node, claim this same node to be many home nodes.According to quoting in directed graph, be cited relation, all many home nodes that described analysis module 11 identifiable design goes out, and as node NodeD001 and node NodeC001, described analysis module 11 wipes out all many home nodes, as shown in Figure 3 (b).
After all many home nodes in the directed graph of generation are wiped out by described analysis module 11, wipe out the tip leaf node in directed graph again, as NodeE001, NodeE002, NodeE003, NodeE004, NodeE005, NodeH001, NodeD005, NodeC002, NodeC003, NodeB002 and NodeB003 in Fig. 3 (c); Final only remaining node NodeA001, NodeB001, NodeC004 and NodeD002, as shown in Fig. 3 (d); Fig. 3 (d) is HTTP user behavior trajectory diagram, and node NodeA001, NodeB001, NodeC004 and NodeD002 represent and initiatively clicked and the HTTP request of generation by user.
Here, for ensureing the information of content not containing non-HTTP raw requests of described contingency table record, before generation directed graph, described analysis module 11 also by search described contingency table record further HTTP raw requests in whether containing HTML (HTML, HyperTextMarkupLanguage) element files request, as file request such as .css .js and .jpg, if can find, then delete described html element element file request.
Described analysis module 11 exports described HTTP user behavior trajectory diagram to described output module 12, and described output module 12 pairs of HTTP user behavior trajectory diagrams export, so that webmaster personnel check.According to the actual demand of user, output format can be set to text or figure.
Device of the present invention can also be applied to the equipment such as Broadband Remote Access Server (BRAS, BroadbandRemoteAccessServer), core network gateway, home gateway, evolution base station eNodeB and wireless controller.
Based on the device of above-mentioned acquisition HTTP user behavior track, present invention also offers a kind of method obtaining HTTP user behavior track, as shown in Figure 4, described method comprises:
Step 40: identify HTTP request message, extract the URL information in HTTP request message and Referer information, and store described URL information and Referer information to contingency table;
Here, described HTTP request message identifies from the network service flow received; Described extraction can be extracted according to the message content extracting rule pre-set.
Step 41: the points relationship according to described contingency table record generates directed graph, filters out according to directed graph the HTTP request that browser initiates automatically, forms HTTP user behavior trajectory diagram and also exports;
Here, HTTP request that browser initiates automatically is filtered out described in specifically: wipe out the many home nodes in directed graph and/or tip leaf node.That is, HTTP request that browser initiates automatically is filtered out by many home nodes of wiping out in directed graph or the tip leaf node wiped out in directed graph or the many home nodes wiped out in directed graph and tip leaf node simultaneously according to directed graph.
Concrete, the operation of step 40 can be completed by identification extraction module, and as shown in Figure 5, step 40 can be:
Step 401: identify that extraction module receives the Business Stream in network;
Here, described Business Stream comprises: Email, Streaming Media, P2P, VOIP, WEB etc.;
Step 402: identify that extraction module utilizes deep packet inspection method to identify HTTP message in Business Stream;
Step 403: identify that extraction module is according to RFC2616 standard, identifies the HTTP request message in HTTP message;
Step 404: identify that extraction module extracts URL information in described HTTP request message and Referer information according to message content extracting rule;
Step 405: identify that described URL information and Referer information are recorded in contingency table by extraction module;
Here, the form pointing to this points relationship of described Referer information with described URL information in contingency table carries out record, for ease of follow-up generation directed graph; Each points relationship recorded in described contingency table is a HTTP raw requests, by record at least one HTTP raw requests in described contingency table.
Concrete, the operation of step 41 can be completed by analysis module and output module, and to wipe out many home nodes in directed graph and tip leaf node simultaneously, described step 41 can be:
Step 411: analysis module is according to the points relationship recorded in contingency table, and namely URL information points to Referer information, generates directed graph;
As shown in Fig. 3 (a), each node both may represent the HTTP request automatically initiated by browser, also may represent and initiatively be clicked and the HTTP request of generation by user; Node NodeC002, NodeC003, NodeC004 are all quoted by node NodeD001.Why having the existence of this adduction relationship, is because record at contingency table three HTTP raw requests: NodeD001 sensing NodeC002, NodeD001 sensing NodeC003, NodeD001 sensing NodeC004 that URL information is NodeD001; In directed graph shown in Fig. 3 (a), node NodeD001, node NodeC001 are many home nodes.
Step 412: analysis module wipes out all many home nodes in directed graph;
Here, as shown in Figure 3 (b), node NodeD001, node NodeC001 is wiped out.
Step 413: analysis module wipes out the tip leaf node in directed graph again;
In Fig. 3 (c), node NodeE001, NodeE002, NodeE003, NodeE004, NodeE005, NodeH001, NodeD005, NodeC002, NodeC003, NodeB002 and NodeB003 are tip leaf node, these tip leaf nodes are wiped out by analysis module, remaining node NodeA001, NodeB001, NodeC004 and NodeD002; As shown in Fig. 3 (d), node NodeA001, NodeB001, NodeC004 and NodeD002 are HTTP user behavior trajectory diagram, represent and are initiatively clicked and the HTTP request of generation by user.
Step 414: HTTP user behavior trajectory diagram is sent to output module by analysis module.
Step 415: output module carries out text output or images outputting, so that webmaster personnel check to described HTTP user behavior trajectory diagram.
In above-mentioned steps 411 and step 412, be described from the angle of the directed graph generated, can also be described from the angle of contingency table, for:
Step 411 ': in contingency table, that searches described contingency table record has identical URL information, the HTTP raw requests of different Referer information, when finding, performs step 412 '; Search not then, in contingency table, still point to the form record of Referer information with URL information.
Step 412 ': delete the identical URL information of HTTP raw requests, retain different Referer information, to distinguish different HTTP raw requests, continue to perform step 413.
Also can be optimized in step 41, before described step 411, whether analysis module also can be searched containing html element element file request in the HTTP raw requests of contingency table record, as file request such as .css .js and .jpg; If can find, then described html element element file request is deleted.The object adding this optimization process is the information of content containing non-HTTP raw requests in order to ensure contingency table record.
The apparatus and method of acquisition HTTP user behavior track provided by the invention, first identify the HTTP request message in Business Stream; Extract URL information and the Referer information of current HTTP request message again, and the points relationship that URL information and Referer information point to Referer information with URL information is recorded in contingency table; Then the points relationship according to contingency table record generates directed graph, because each node in directed graph may represent the HTTP request automatically initiated by browser, also may represent and initiatively be clicked and the HTTP request of generation by user, method by wiping out many home nodes in directed graph and/or tip leaf node reaches the HTTP request filtering out browser and automatically initiate, and obtains the object of HTTP user behavior track; Finally with the output format of figure or text, HTTP user behavior track is exported, check to facilitate webmaster personnel.Utilize the present invention, only need identify HTTP request message, without the need to paying close attention to http response message, taking network device computes and storage resources can be reduced, reduce the impact on Internet Transmission.
The above, be only preferred embodiment of the present invention, be not intended to limit protection scope of the present invention.
Claims (8)
1. obtain a device for HTML (Hypertext Markup Language) HTTP user behavior track, it is characterized in that, this device comprises: identify extraction module, analysis module and output module; Wherein,
Described identification extraction module, for identifying HTTP request message, extracts uniform resource position mark URL information in HTTP request message and with reference to address Referer information, and stores described URL information and Referer information to contingency table;
Described analysis module, generates directed graph for the points relationship according to described contingency table record, filters out according to directed graph the HTTP request that browser initiates automatically, forms HTTP user behavior trajectory diagram and also exports described output module to;
Described output module, for exporting HTTP user behavior trajectory diagram;
Described analysis module, also for searching the HTTP raw requests with identical URL information, different Referer information recorded in described contingency table; When finding, delete URL information identical in described HTTP raw requests, retain different Referer information; Search not then, in contingency table, point to the form record of Referer information with URL information.
2. the device of acquisition HTTP user behavior track according to claim 1, it is characterized in that, described identification extraction module is further used for:
Receive the Business Stream in network, deep packet inspection method is first utilized to identify HTTP message in described Business Stream, again according to http protocol standard, HTTP request message is identified in HTTP message, according to message content extracting rule, extract URL information and the Referer information of described HTTP request message, described URL information and Referer information are recorded in contingency table.
3. the device of acquisition HTTP user behavior track according to claim 1, is characterized in that, described analysis module filters out according to directed graph the HTTP request that browser initiates automatically and is:
Described analysis module wipes out many home nodes in directed graph and/or tip leaf node.
4. the device of the acquisition HTTP user behavior track according to any one of claims 1 to 3, is characterized in that, described analysis module also for:
Search whether containing HTML html element element file request in the HTTP raw requests of contingency table record, if can find, then delete described html element element file request.
5. obtain a method for HTTP user behavior track, it is characterized in that, described method comprises:
Identify HTTP request message, extract the URL information in HTTP request message and Referer information, and store described URL information and Referer information to contingency table;
Points relationship according to described contingency table record generates directed graph, filters out according to directed graph the HTTP request that browser initiates automatically, forms HTTP user behavior trajectory diagram and also exports;
The described points relationship according to described contingency table record generates directed graph, the HTTP request that browser initiates automatically is filtered out according to directed graph, form HTTP user behavior trajectory diagram, also comprise: search the HTTP raw requests with identical URL information, different Referer information recorded in described contingency table; When finding, delete URL information identical in described HTTP raw requests, retain different Referer information, to distinguish different HTTP raw requests; Search not then, in contingency table, still point to the form record of Referer information with URL information.
6. the method for acquisition HTTP user behavior track according to claim 5, it is characterized in that, described identification HTTP request message, extracts the URL information in HTTP request message and Referer information, and stores described URL information and Referer information to contingency table is:
Receive the Business Stream in network, deep packet inspection method is utilized to identify HTTP message in Business Stream, according to http protocol standard, identify HTTP request message, according to message content extracting rule, extract URL information and the Referer information of described HTTP request message, described URL information and Referer information are recorded in contingency table.
7. the method for acquisition HTTP user behavior track according to claim 5, is characterized in that, describedly filters out according to directed graph the HTTP request that browser initiates automatically and is:
Wipe out the many home nodes in directed graph and/or tip leaf node.
8. the method for acquisition HTTP user behavior track according to claim 5, it is characterized in that, before the described points relationship according to described contingency table record becomes directed graph, described method also comprises:
Whether search in the HTTP raw requests of contingency table record containing html element element file request; If can find, then delete described html element element file request.
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Families Citing this family (6)
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| CN103905434A (en) * | 2014-03-13 | 2014-07-02 | 亿赞普(北京)科技有限公司 | Method and device for processing network data |
| CN105989019B (en) * | 2015-01-29 | 2019-08-16 | 北京秒针信息咨询有限公司 | A kind of method and device for cleaning data |
| CN105812481A (en) * | 2016-04-20 | 2016-07-27 | 上海斐讯数据通信技术有限公司 | Hypertext transfer protocol request identification system and hypertext transfer protocol request identification method |
| CN106789938B (en) * | 2016-11-30 | 2020-04-21 | 四川秘无痕科技有限责任公司 | Method for monitoring search trace of browser at mobile phone end in real time |
| CN108228887B (en) * | 2018-01-31 | 2019-12-03 | 百度在线网络技术（北京）有限公司 | Method and apparatus for generating information |
| CN109597833A (en) * | 2018-10-15 | 2019-04-09 | 平安科技（深圳）有限公司 | Event prediction method, apparatus, computer equipment and storage medium based on big data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101909079A (en) * | 2010-07-15 | 2010-12-08 | 北京迈朗世讯科技有限公司 | User online behavior data acquisition method in backbone link and system |
| US7873656B1 (en) * | 2007-09-25 | 2011-01-18 | Trend Micro Incorporated | Apparatus and methods to reduce proxy overhead in a gateway |
| CN102081670A (en) * | 2011-01-20 | 2011-06-01 | 张金海 | Data filtering method and data filtering device |
| CN102870118A (en) * | 2012-06-30 | 2013-01-09 | 华为技术有限公司 | Access method, device and system to user behavior |
-
2013
- 2013-01-30 CN CN201310037596.8A patent/CN103152387B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7873656B1 (en) * | 2007-09-25 | 2011-01-18 | Trend Micro Incorporated | Apparatus and methods to reduce proxy overhead in a gateway |
| CN101909079A (en) * | 2010-07-15 | 2010-12-08 | 北京迈朗世讯科技有限公司 | User online behavior data acquisition method in backbone link and system |
| CN102081670A (en) * | 2011-01-20 | 2011-06-01 | 张金海 | Data filtering method and data filtering device |
| CN102870118A (en) * | 2012-06-30 | 2013-01-09 | 华为技术有限公司 | Access method, device and system to user behavior |
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