CN103095711B - A kind of application layer ddos attack detection method for website and system of defense - Google Patents
A kind of application layer ddos attack detection method for website and system of defense Download PDFInfo
- Publication number
- CN103095711B CN103095711B CN201310018798.8A CN201310018798A CN103095711B CN 103095711 B CN103095711 B CN 103095711B CN 201310018798 A CN201310018798 A CN 201310018798A CN 103095711 B CN103095711 B CN 103095711B
- Authority
- CN
- China
- Prior art keywords
- sequence
- user
- website
- page
- clicks
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 23
- 230000007123 defense Effects 0.000 title abstract description 10
- 238000005295 random walk Methods 0.000 claims abstract description 25
- 238000012549 training Methods 0.000 claims abstract description 13
- 239000011159 matrix material Substances 0.000 claims description 17
- 239000013598 vector Substances 0.000 claims description 17
- 230000007704 transition Effects 0.000 claims description 12
- 238000003064 k means clustering Methods 0.000 claims description 3
- 230000005012 migration Effects 0.000 claims description 2
- 238000013508 migration Methods 0.000 claims description 2
- 238000000034 method Methods 0.000 abstract description 17
- 230000008569 process Effects 0.000 abstract description 9
- 238000010586 diagram Methods 0.000 description 7
- 230000005856 abnormality Effects 0.000 description 6
- 230000006399 behavior Effects 0.000 description 6
- 238000012545 processing Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 1
- 238000000205 computational method Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Landscapes
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The present invention relates to a kind of application layer ddos attack detection method and system of defense, relate to network security, particularly the detection of application layer ddos attack and defence.The present invention is by being analyzed user access activity, it is proposed that click on detection method and the system of defense of sequence prediction based on user.First extract the page URL of website, utilize clustering algorithm to cluster, obtain the page classifications of this websiteV j Sequence is clicked on user；Then utilize user to click on sequence construct random walk figure, calculate next observation cycle of user by random walk process and click on sequence；By training threshold value, the sequence similarity finally calculating forecasting sequence and click on sequence, judges that user clicks on the abnormity of sequence.The present invention can effectively detect application layer ddos attack, particularly simulates the query-attack of normal users behavior, can be widely applied to data center's Website server Prevention-Security.
Description
Technical field
The present invention relates to network safety filed, especially relate to the application layer ddos attack detection method for website and prevent
Imperial system.
Background technology
Distributed denial of service attack (Distributed Denial of Service, DDoS), always the Internet
One of the most serious threat that service supplier-Web server is faced.Tradition DDoS based on Internet or transport layer attacks
Mode of hitting is detected well by increasingly mature network protection technology (fire wall, Intrusion Detection Technique etc.), calculates simultaneously
The change of pattern makes more to service to be interacted by Web, and this accelerates ddos attack mode and develops to application layer.Occur
Ddos attack in application layer generally uses real IP address as attacking node, utilizes the leak of application layer protocol, to target
Server sends a large amount of query-attack based on HTTP legal agreement, can easily pass through network-safeguard system, and this makes it become undoubtedly
The safety problem solved it is badly in need of for current web services device.
But most achievement in research is to detect Internet or transport layer ddos attack, be not suitable for based on
The detection of application layer ddos attack.Existing application layer ddos attack detection method is also mainly for big vast formula based on http protocol
Attack detecting, realizes attack detecting by the traffic characteristic of HTTP request or protocol characteristic are carried out statistical analysis, this for
The asymmetrical attack mode using normal speed query-attack is then invalid.
Ranjan proposes the detection method of dialogue-based middle HTTP request statistics abnormality degree, and the method is first to website pages
Classifying in face, then adds up the classification situation of user's HTTP request in each session, and train normal users with this
HTTP request model, finally by the departure degree identification query-attack with normal users model.The method is attacked for asymmetric
The characteristic hit, has carried out page classifications with HTTP request to resource consumption situation first；But it uses the HTTP request in session
Statistical nature carries out the training of normal model, the feature remaining flow embodied, but the stream of asymmetrical attack person
Flow characteristic is consistent with normal users, and the unified model used can not access feature to all types user is described.
Summary of the invention
It is an object of the invention to: for website based on application layer http protocol ddos attack, it is provided that one can be to employing
The attack traffic of flood formula and asymmetrical attack mode carries out method and the system of defense detected；Due under asymmetrical attack mode,
The discharge characteristic of assailant is consistent with normal users, brings difficulty to attack detecting and defence, to this end, inventor proposes accordingly
Solution.
A kind of application layer ddos attack detection method for website, comprises the steps:
Extract the page URL of website, utilize K-means clustering algorithm that HTTP request is classified according to website, obtain
The page classifications set of this websiteV j ,jFor page type, by HTTP request and page classifications setV j Mate, and then obtain
User clicks on sequenceu i ={x 1 ,x 2 ,…,x n ,x i Represent the one click of user,iFor isolated user number.
User is utilized to click on sequenceu i ={x 1 ,x 2 ,…,x n Training obtain page transition probability matrixP Vj 。
Sequence is clicked on according to useru i ={x 1 ,x 2 ,…,x n , construct this user institute accession page random walk figure.
Sequence is clicked on according to the user in user's Current observation cycleu i ={x 1 ,x 2 ,…,x n , page transition probability matrixP Vj Random walk calculating is carried out, it was predicted that the user obtaining next observation cycle of user clicks on sequence with page random walk figureu ’ i =
{x ’ 1 ,x ’ 2 ,…,x ’ n }.ProbabilityDistribution Vector computing formula during random walk calculates is, its
InpFor adjacency matrix,s 0It is vectorial for initial probability distribution,For redirecting probability of happening,dEach top is jumped to during for redirecting
The ProbabilityDistribution Vector of point.pIt is specially page transition probability matrixP Vj ；s 0Obtain from random walk figure；dIt is specially the page to turn
Move probability matrixP Vj In a column vector；Redirect probability of happeningIt is set to 0.15.
The user calculated in the Current observation cycle clicks on sequenceu i ={x 1 ,x 2 ,…,x n And user's point of next observation cycle
Hit sequenceu ’ i ={x ’ 1 ,x ’ 2 ,…,x ’ n Sequence similarity.The computing formula of sequence similarity is,u i Represent
User in the Current observation cycle clicks on sequence,u ’ Represent that the user of next observation cycle clicks on sequence.
Compare according to sequence similarity and threshold value and judge that user clicks on sequenceu i The most normal, if sequence similarity
Then show that less than threshold value this user clicks on sequenceu i For attack sequence, otherwise it it is normal access sequence.
A kind of application layer ddos attack system of defense for website, including request processing module, model training module, sequence
Row prediction module and abnormality detection module, wherein, request processing module utilizes K-means clustering algorithm to HTTP request according to net
Classifying in station, obtains classification setV j , and then obtain user and click on sequenceu i , and user is clicked on sequenceu i Biography sends to model
Training module and sequence prediction module；Model training module clicks on sequence according to useru i Structure random walk figure, it is thus achieved that website
Page transition probability matrixP Vj , and submit to Sequence Detection module；Sequence prediction module clicks on sequence useru i Current sight
On the basis of the survey cycle, according to the prediction of random walk figure, next observation cycle user clicks on sequenceu ’ i ；Abnormality detection module will
User in the Current observation cycle clicks on sequenceu i Sequence is clicked on next observation cycle useru ’ i Carry out sequence similarity meter
Calculate.
The present invention can effectively detect application layer ddos attack, particularly simulates the query-attack of normal users behavior, can be wide
General it is applied to data center's Website server Prevention-Security.
Accompanying drawing explanation
Fig. 1 is that application layer ddos attack system of defense disposes schematic diagram；
Fig. 2 is application layer ddos attack system of defense architectural schematic；
Fig. 3 is application layer ddos attack detection mode schematic diagram；
Fig. 4 is that user clicks on sequence prediction and abnormality detection schematic diagram；
Fig. 5 is random walk schematic diagram.
Detailed description of the invention
Being illustrated in figure 1 application layer ddos attack system of defense and dispose schematic diagram, system deployment is in data center's web services
Device 1.2 front end, for protecting all web servers 1.2 of data center.System is to accessing data center server 80 port
The HTTP request of 1.1 detects, and then abandons if query-attack, is then transmitted to server if normal request.
Being illustrated in figure 2 application layer ddos attack system of defense architectural schematic, this system is mainly by following four
Module forms:
Request processing module 1, the HTTP request that this module is responsible for accessing server carries out pretreatment, first according to HTTP
Request uses K-means(clustering algorithm based on distance) page of a website classified, classified by clustering algorithm
SetV j ；J is page type, secondly according to HTTP request and page classifications setV j Mate, obtain the click sequence of useru i ={x 1 ,x 2 ,…,x n },u i In element x i ∈V j , user is clicked on sequence and gives model training module 2 and sequence prediction mould
Block 3.
Model training module 2, this module is responsible for training website user access activity, structuring user's accession page random walk
Figure and the transition probability matrix of Website pageP Vj , submit to sequence prediction module and use.User to access pages random walk figure pin
To unique user, may be used for distinguishing the access behavior of unique user；The transition probability matrix of Website page is used for describing access
The clustering behavior of all users in this website, for the diversity avoiding unique user behavior difference to be brought.
Sequence prediction module 3, this module is responsible for clicking on sequence useru i ={x 1 ,x 2 ,…,x n Current observation cycle
On the basis of, according to the prediction of random walk figure, next observation cycle user clicks on sequenceu ’ i ={x ’ 1 ,x ’ 2 ,…,x ’ n }。
Abnormality detection module 4, this module is responsible for clicking on user the normality of sequence and is detected, will use in observation cycle
Sequence is clicked at familyu i ={x 1 ,x 2 ,…,x n And predict that the user obtained clicks on sequenceu ’ i ={x ’ 1 ,x ’ 2 ,…,x ’ n Carry out sequence phase
Seemingly spend calculating, if similarity is higher than threshold value, then asks for normal users, be transmitted to server；If similarity is less than threshold value,
Then ask for ddos attack, then abandon the HTTP request of this user.
Pretend normal users access process to solve assailant, system have employed in user clicks on sequence recognition based on
The Forecasting Methodology of random walk, it is possible to the access behavior to user's next cycle carries out Accurate Prediction.
The application layer ddos attack detection method using the present invention to propose, compensate for tradition and is characterized as basis with customer flow
The deficiency of method, it is possible to the asymmetric HTTP query-attack that effectively resource consumption is high to traffic characteristic is normal detects, and keeps away
Having exempted from legacy system uses same model to describe the error that all user behaviors are brought.
Being illustrated in figure 3 application layer ddos attack detection method schematic diagram, the method includes following six step:
1, first extract the page URL of website, according to URL depth, URL popularity and consumer loyalty degree, utilize K-means
(clustering algorithm based on distance) clustering algorithm clusters, and obtains the page classifications of this websiteV j ,jFor page type.
2, distinguish a user by IP, obtain the click sequence in session of this useru i ={x 1 ,x 2 ,…,x n ,i
For isolated user number, utilize the click sequence training page transition probability matrix of all usersP Vj 。
3, sequence is clicked on according to a session of useru i ={x 1 ,x 2 ,…,x n , construct this user institute accession page with
Machine migration figure.
4, according to the click sequence in user's Current observation timeu i ={x 1 ,x 2 ,…,x n , page transition probability matrixP Vj
Random walk process calculating is carried out, it was predicted that the user obtaining next observation cycle of user clicks on sequence with page random walk figureu ’ i
={x ’ 1 ,x ’ 2 ,…,x ’ n }。
5, calculating observation sequenceu i ={x 1 ,x 2 ,…,x n And forecasting sequenceu ’ i ={x ’ 1 ,x ’ 2 ,…,x ’ n Sequence similarity,
Computing formula is,u i Represent that the user in the Current observation cycle clicks on sequence,u ’ Represent next observation cycle
User clicks on sequence.
6, compare according to sequence similarity and threshold value and judge the normality of this sequence, if sequence similarity is less than threshold
Value then shows that this user's access sequence is attack sequence, otherwise is normal access sequence.
With an instantiation, user is clicked on sequence random walk figure building method below to be analyzed, example: the page
SetIn have 5 pages, i.e.k=5, training setDIn have 4 user's access sequences be respectivelyu 1=1,2,3,4,4},u 2
=3,4,5,2},u 3=3,5,2,4,1,3},u 4=2,1,5}, then by page setWith training setDThat derives is oriented
Random walk figure as shown in Figure 5.
Fig. 4 show user and clicks on sequence prediction and abnormality detection schematic diagram, and this method is by acquisition in observation cycle T
User clicks on sequenceu i (k)={x 1 ,x 2 ,…,x n , calculate the user in next observation cycle T+1 by random walk process
Click on sequenceu i `(k+1)={x n+1, x n+2, …, x m+n , concrete calculating process is as follows:
Sequence is clicked on the user in observation cycle Tu i (k)={x 1 ,x 2 ,…,x n For inputting, pass through random walk process
Calculate user to click on next timex n+1, 4 input parameters of random walk process needs: adjacency matrixp, initial probability distribution vectors 0, redirect probability of happening, when redirecting, jump to the ProbabilityDistribution Vector on each summit in figured.Wherein adjacency matrixpFor
Website page transition probability matrixP Vj ；Initial probability distribution vectors 0Obtain from random walk figure；Redirect ProbabilityDistribution Vectord
For Website page transition probability matrixP Vj In a column vector；Redirect probability of happeningIt is set to 0.15.
Output probability distribution vector after walk process is denoted as every times,sComputational methods be shown below:
(1)
By vectorsInput as formula (1), ProbabilityDistribution Vector now, until convergence, is remembered by the formula that iterates (1)
Make, vectorIt is the ProbabilityDistribution Vector of steady statue, from vectorIn choosex n+1Click next timex n+1。
According to newly obtained clickx n+1Constitute and click on sequenceu i (k)={x 2 ,x 2 ,…,x n+1, using it as enter through with
Machine walk process calculates and clicks on next timex n+2, such double countingmSecondary, the user obtained in observation cycle T+1 clicks on sequenceu i `
(k+1)={x n+1, x n+2, …, x m+n }.Observation sequence in calculating observation cycle T+1u i (k+1)={x 1, x 2, …, x m }
And forecasting sequenceu i `(k+1)={x n+1, x n+2, …, x m+n Similarity, it is achieved sequence variation detect.
Claims (3)
1. the application layer ddos attack detection method for website, it is characterised in that comprise the steps:
Extract the page URL of website, according to URL depth, URL popularity and consumer loyalty degree, utilize K-means clustering algorithm pair
HTTP request is classified according to website, obtains the page classifications set of this websiteV j ,jFor page type, distinguish one by IP
Individual user, and then the user obtained in session of this user clicks on sequenceu i ={x 1 ,x 2 ,…,x n ,iFor isolated user number；
The user utilizing all users clicks on sequenceu i ={x 1 ,x 2 ,…,x n Training obtain page transition probability matrixP Vj ；
Sequence is clicked on according to useru i ={x 1 ,x 2 ,…,x n , construct this user institute accession page random walk figure；
Sequence is clicked on according to the user in user's Current observation cycleu i ={x 1 ,x 2 ,…,x n , page transition probability matrixP Vj With
Page random walk figure carries out random walk calculating, it was predicted that the user obtaining next observation cycle of user clicks on sequenceu ’ i ={x ’ 1 ,x ’ 2 ,…,x ’ n }；
The user calculated in the Current observation cycle clicks on sequenceu i ={x 1 ,x 2 ,…,x n And the user of next observation cycle click on sequence
Rowu ’ i ={x ’ 1 ,x ’ 2 ,…,x ’ n Sequence similarity, computing formula is,u i In representing the Current observation cycle
User clicks on sequence,u ’ Represent that the user of next observation cycle clicks on sequence；
Compare according to sequence similarity and threshold value and judge that user clicks on sequenceu i It is the most normal, if sequence similarity is less than
Threshold value then shows that this user clicks on sequenceu i For attack sequence, otherwise it it is normal access sequence.
A kind of application layer ddos attack detection method for website, it is characterised in that: described
To page classifications setV j After, by HTTP request and page classifications setV j Mate, and then obtain user and click on sequenceu i =
{x 1 ,x 2 ,…,x n }。
A kind of application layer ddos attack detection method for website, it is characterised in that: described with
ProbabilityDistribution Vector computing formula during machine migration calculates is, whereinpFor adjacency matrix,s 0For
Initial probability distribution vector,For redirecting probability of happening,dThe ProbabilityDistribution Vector on each summit is jumped to during for redirecting.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310018798.8A CN103095711B (en) | 2013-01-18 | 2013-01-18 | A kind of application layer ddos attack detection method for website and system of defense |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310018798.8A CN103095711B (en) | 2013-01-18 | 2013-01-18 | A kind of application layer ddos attack detection method for website and system of defense |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103095711A CN103095711A (en) | 2013-05-08 |
| CN103095711B true CN103095711B (en) | 2016-10-26 |
Family
ID=48207844
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310018798.8A Active CN103095711B (en) | 2013-01-18 | 2013-01-18 | A kind of application layer ddos attack detection method for website and system of defense |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103095711B (en) |
Families Citing this family (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103810241B (en) * | 2013-11-22 | 2017-04-05 | 北京奇虎科技有限公司 | Filter method and device that a kind of low frequency is clicked on |
| CN104901971B (en) * | 2015-06-23 | 2019-03-15 | 北京东方棱镜科技有限公司 | The method and apparatus that safety analysis is carried out to network behavior |
| TWI562013B (en) * | 2015-07-06 | 2016-12-11 | Wistron Corp | Method, system and apparatus for predicting abnormality |
| CN105592070B (en) * | 2015-11-16 | 2018-10-23 | 中国银联股份有限公司 | Application layer DDoS defence methods and system |
| CN105510971A (en) * | 2016-02-18 | 2016-04-20 | 福建师范大学 | Seismic data abnormality detection method based on random walk |
| CN105812280B (en) * | 2016-05-05 | 2019-06-04 | 四川九洲电器集团有限责任公司 | A kind of classification method and electronic equipment |
| CN106209861B (en) * | 2016-07-14 | 2019-07-12 | 南京邮电大学 | One kind being based on broad sense Jie Kade similarity factor Web application layer ddos attack detection method and device |
| CN107798571B (en) * | 2016-08-31 | 2019-08-30 | 阿里巴巴集团控股有限公司 | Malice address/malice order identifying system, method and device |
| WO2018095192A1 (en) | 2016-11-23 | 2018-05-31 | 腾讯科技（深圳）有限公司 | Method and system for website attack detection and prevention |
| CN106778259B (en) * | 2016-12-28 | 2020-01-10 | 北京明朝万达科技股份有限公司 | Abnormal behavior discovery method and system based on big data machine learning |
| CN108874813B (en) * | 2017-05-10 | 2022-07-29 | 腾讯科技（北京）有限公司 | Information processing method, device and storage medium |
| CN107204991A (en) * | 2017-07-06 | 2017-09-26 | 深信服科技股份有限公司 | A kind of server exception detection method and system |
| CN107491970B (en) * | 2017-08-17 | 2021-04-02 | 北京三快在线科技有限公司 | Real-time anti-cheating detection monitoring method and system and computing equipment |
| CN107707547A (en) * | 2017-09-29 | 2018-02-16 | 北京神州绿盟信息安全科技股份有限公司 | The detection method and equipment of a kind of ddos attack |
| CN108540440A (en) * | 2018-02-02 | 2018-09-14 | 努比亚技术有限公司 | DDOS attack solution, server and computer readable storage medium |
| CN111476610B (en) * | 2020-04-16 | 2023-06-09 | 腾讯科技（深圳）有限公司 | Information detection method, device and computer readable storage medium |
| CN112488321B (en) * | 2020-12-07 | 2022-07-01 | 重庆邮电大学 | Antagonistic machine learning defense method oriented to generalized nonnegative matrix factorization algorithm |
| CN112231700B (en) * | 2020-12-17 | 2021-05-11 | 腾讯科技（深圳）有限公司 | Behavior recognition method and apparatus, storage medium, and electronic device |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102184406A (en) * | 2009-11-11 | 2011-09-14 | 索尼公司 | Information processing device, information processing method, and program |
| CN102487293A (en) * | 2010-12-06 | 2012-06-06 | 中国人民解放军理工大学 | Satellite communication network abnormity detection method based on network control |
-
2013
- 2013-01-18 CN CN201310018798.8A patent/CN103095711B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102184406A (en) * | 2009-11-11 | 2011-09-14 | 索尼公司 | Information processing device, information processing method, and program |
| CN102487293A (en) * | 2010-12-06 | 2012-06-06 | 中国人民解放军理工大学 | Satellite communication network abnormity detection method based on network control |
Non-Patent Citations (2)
| Title |
|---|
| 基于shell命令和多阶Markov链模型的用户伪装攻击检测;肖喜 翟起滨 田新广 陈小娟 叶润国;《电子学报》;20110531;第39卷(第5期);正文第1201页 * |
| 基于用户行为分析的应用层DDoS攻击检测方法;赵国锋 喻守成 文晟;《计算机应用研究》;20110228;第28卷(第2期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103095711A (en) | 2013-05-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103095711B (en) | A kind of application layer ddos attack detection method for website and system of defense | |
| Xie et al. | Monitoring the application-layer DDoS attacks for popular websites | |
| Sun et al. | {HinDom}: A robust malicious domain detection system based on heterogeneous information network with transductive classification | |
| Xie et al. | A large-scale hidden semi-Markov model for anomaly detection on user browsing behaviors | |
| Jyothi et al. | Brain: Behavior based adaptive intrusion detection in networks: Using hardware performance counters to detect ddos attacks | |
| CN113079143A (en) | Flow data-based anomaly detection method and system | |
| Zhu et al. | A deep learning approach for network anomaly detection based on AMF-LSTM | |
| CN109117634A (en) | Malware detection method and system based on network flow multi-view integration | |
| CN109600363A (en) | A kind of internet-of-things terminal network portrait and abnormal network access behavioral value method | |
| CN107392016A (en) | A kind of web data storehouse attack detecting system based on agency | |
| CN109284296A (en) | A kind of big data PB grades of distributed informationm storage and retrieval platforms | |
| Patil et al. | S-DDoS: Apache spark based real-time DDoS detection system | |
| Xu et al. | Detection on application layer DDoS using random walk model | |
| Ye et al. | Application layer DDoS detection using clustering analysis | |
| CN104113544B (en) | Network inbreak detection method and system based on fuzzy hidden conditional random fields model | |
| Liao et al. | Feature extraction and construction of application layer DDoS attack based on user behavior | |
| Meng et al. | Ddos attack detection system based on analysis of users' behaviors for application layer | |
| Beckett et al. | New sensing technique for detecting application layer DDoS attacks targeting back-end database resources | |
| Lei et al. | Detecting malicious domains with behavioral modeling and graph embedding | |
| Liang et al. | Unveiling fake accounts at the time of registration: An unsupervised approach | |
| CN107231383A (en) | The detection method and device of CC attacks | |
| Agrawal et al. | Estimating strength of a DDoS attack in real time using ANN based scheme | |
| Wang et al. | HTTP-SoLDiER: An HTTP-flooding attack detection scheme with the large deviation principle | |
| Badis et al. | Toward a source detection of botclouds: a pca-based approach | |
| Jiang et al. | A highly efficient remote access Trojan detection method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20221104 Address after: 710061 Room 222, East of Floor 2, Office Building, Hanguang Community, No. 10, Hanguang South Section, Yanta District, Xi'an, Shaanxi Patentee after: Xi'an Longhe Linchuang Intellectual Property Agency Co.,Ltd. Address before: 400065 Chongqing Nan'an District huangjuezhen pass Chongwen Road No. 2 Patentee before: CHONGQING University OF POSTS AND TELECOMMUNICATIONS Effective date of registration: 20221104 Address after: Room 1111, Building 1, Wanting Building, Labor Community, Xixiang Street, Bao'an District, Shenzhen City, Guangdong Province, 518101 Patentee after: Shenzhen Occupy Information Technology Co.,Ltd. Address before: 710061 Room 222, East of Floor 2, Office Building, Hanguang Community, No. 10, Hanguang South Section, Yanta District, Xi'an, Shaanxi Patentee before: Xi'an Longhe Linchuang Intellectual Property Agency Co.,Ltd. |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240408 Address after: Room 05-5, 8th Floor, Hesheng Industrial and Commercial Building, No. 89 Fuxin Middle Road, Wangzhuang Street, Jin'an District, Fuzhou City, Fujian Province, 350000 Patentee after: Fuzhou Qilian Information Consulting Co.,Ltd. Country or region after: China Address before: Room 1111, Building 1, Wanting Building, Labor Community, Xixiang Street, Bao'an District, Shenzhen City, Guangdong Province, 518101 Patentee before: Shenzhen Occupy Information Technology Co.,Ltd. Country or region before: China |
|
| TR01 | Transfer of patent right |