CN103916379B - A kind of CC attack recognition method and system based on high frequency statistics - Google Patents
A kind of CC attack recognition method and system based on high frequency statistics Download PDFInfo
- Publication number
- CN103916379B CN103916379B CN201310640806.2A CN201310640806A CN103916379B CN 103916379 B CN103916379 B CN 103916379B CN 201310640806 A CN201310640806 A CN 201310640806A CN 103916379 B CN103916379 B CN 103916379B
- Authority
- CN
- China
- Prior art keywords
- buffering area
- count value
- statistical item
- hash values
- statistical
- 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
- 238000000034 method Methods 0.000 title claims abstract description 19
- 230000003139 buffering effect Effects 0.000 claims abstract description 66
- 241000287828 Gallus gallus Species 0.000 description 4
- 238000007796 conventional method Methods 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000000151 deposition Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 235000013372 meat Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Landscapes
- Data Exchanges In Wide-Area Networks (AREA)
- Computer And Data Communications (AREA)
Abstract
The invention discloses the CC attack recognition method and system based on high frequency statistics, first, the HTTP GET requests in backbone traffic are recognized, and obtain source IP, purpose IP and URI, calculate hash values；If existing and the hash values identical statistical item in buffering area, then count value adds 1, otherwise judge whether buffering area is full, if it is not, adding buffering area using the hash values as new statistical item, and count value is set as 1, otherwise the count value of all statistical items of buffering area is subtracted 1, and judges to whether there is in buffering area count value for 0 statistical item, if in the presence of, the statistical item is then removed, and buffering area is added using the hash values as new statistical item；If there is the statistical item that unit interval count value exceedes given threshold in buffering area, then it is assumed that there is CC attacks, and alarm.Instant invention overcomes the possibility that CC attacks are flooded by the normal access of large-scale website, the thought counted using high-frequency data recognizes that CC is attacked come effective.
Description
Technical field
The present invention relates to technical field of network security, more particularly to a kind of CC attack recognitions method based on high frequency statistics and
System.
Background technology
Described CC attacks can be classified as one kind of ddos attack.Cause service by sending substantial amounts of request data
Device refusal service, is a kind of connection attack.CC attacks, which common are, acts on behalf of CC attacks, and broiler chicken CC attacks.Acting on behalf of CC attacks is
Hacker generates the legal web-page requests for pointing to victim host by proxy server, realizes DOS and camouflage.And broiler chicken CC attacks are
Hacker attacks software using CC, controls a large amount of broiler chicken, offensive attack, and comparatively speaking the latter is more difficult to defence than the former.Because meat
Chicken can simulate the request that normal users access website, be forged into legal data packet.
Conventional protection DDoS recognizes the CC of abnormal burst in backbone network and accesses stream primarily directed to some websites
Amount, the place different from conventional method is that have substantial amounts of normal flowing of access first, as long as secondly traditional access count statistics
For the website of the network protected, it is not necessary to which other websites are counted, the information counted is considerably less.
The identification core that CC attacks are carried out in backbone network is the statistics that can be conducted interviews to the website attacked, and is set up
The full URL of the whole network statistics resource to be consumed is then not convergent, constantly has new URL to add, and new different domain names are added, and are needed
The content to be counted is Protean, and statistical item is not fixed, and how therefrom to find high frequency CC attacks, and not by regular big
The normal access of type website flood be currently without solve the problem of.
The content of the invention
For above-mentioned technical problem, the invention provides a kind of CC attack recognition method and system based on high frequency statistics,
This method is by setting up a buffering area, using the thought of big data high frequency statistics, abandons the conventional method of accurate statistics, realizes
The purpose that effectively identification CC is attacked.
The present invention adopts with the following method to realize：A kind of CC attack recognition methods based on high frequency statistics, including：
HTTP GET requests in step 1, identification backbone traffic, and utilization HTTP GET requests acquisition source IP,
Purpose IP and URI；
Wherein, purpose IP, HTTP head, URI etc., the URI can be obtained using HTTP GET requests content（Uniform
Resource Locator abbreviation）It is URL, is the field in http protocol, is a URL part；Utilize
HTTP can obtain the contents such as source IP, URI, protocol version, client-side information；
Step 2, the source IP using acquisition, purpose IP and URI calculate hash values；
Step 3, judge in buffering area whether there is with the hash values identical statistical item, if in the presence of the statistics
The count value of project adds 1, and performs step 6, otherwise performs step 4；
Step 4, judge to whether there is remaining space in buffering area, if in the presence of regarding the hash values as new statistical items
Mesh adds buffering area, and sets count value as 1, otherwise subtracts 1 by the count value of all statistical items of buffering area, and perform step 5；
Step 5, judge in buffering area with the presence or absence of count value for 0 statistical item, if in the presence of removing the statistical items
Mesh, and buffering area is added using the hash values as new statistical item, and set count value the hash is abandoned as 1, otherwise
Value, terminates；
Step 6, when exist in buffering area unit interval count value exceed given threshold statistical item, then it is assumed that there is CC
Attack, and alarm.
Further, the size of the buffering area is fixed.Form a stable staqtistical data base.
Further, the given threshold is 10 times of conventional visit capacity.
The present invention is realized using following system：A kind of CC System for attack recognition based on high frequency statistics, including：
Identification module, is obtained for recognizing the HTTP GET requests in backbone traffic, and using the HTTP GET requests
Take source IP, purpose IP and URI；
Wherein, purpose IP, HTTP head, URI etc., the URI can be obtained using HTTP GET requests content（Uniform
Resource Locator abbreviation）It is URL, is the field in http protocol, is a URL part；Utilize
HTTP can obtain the contents such as source IP, URI, protocol version, client-side information；
Computing module, for calculating hash values using the source IP, purpose IP and URI that obtain；
First determination module, for judge in buffering area whether there is with the hash values identical statistical item, if depositing
, then the count value of the statistical item adds 1, and is handled by disposal module, otherwise by the second determination module continue judge；
Second determination module, for judge in buffering area whether there is remaining space, if in the presence of, using the hash values as
New statistical item adds buffering area, and sets count value as 1, otherwise subtracts 1 by the count value of all statistical items of buffering area, and
Continued to judge by the 3rd determination module；
3rd determination module, for judging to whether there is in buffering area count value for 0 statistical item, if in the presence of clearly
Buffering area is added except the statistical item, and using the hash values as new statistical item, and sets count value as 1, otherwise
The hash values are abandoned, are terminated；
Dispose module, when exist in buffering area unit interval count value exceed given threshold statistical item, then it is assumed that deposit
In CC attacks, and alarm.
Further, the size of the buffering area is fixed.
Further, the given threshold is 10 times of conventional visit capacity.
In summary, the invention provides a kind of CC attack recognition method and system based on high frequency statistics, identification is passed through
HTTP GET requests in backbone traffic, and source IP, purpose IP and URI are further obtained, calculate hash using the above
Value, and stored these hash values as statistical item into buffering area, if hash values are had stored in buffering area, accordingly
Count value adds 1, otherwise adds buffering area using the hash values as new statistical item, and sets count value as 1, if the buffering area
It is full, then all count values are subtracted 1, count value is deleted for 0 statistical item.So as to ensure there is a fixed number in the buffer
The statistical item of scope is measured, backbone network data traffic is overcome greatly, does the difficulty accurately counted, accomplish with minimum Resources Consumption
The identification of CC attacks is carried out to the key network data of magnanimity.
Brief description of the drawings
In order to illustrate more clearly of technical scheme, letter will be made to the accompanying drawing to be used needed for embodiment below
Singly introduce, it should be apparent that, drawings in the following description are only some embodiments described in the present invention, for this area
For those of ordinary skill, on the premise of not paying creative work, other accompanying drawings can also be obtained according to these accompanying drawings.
A kind of CC attack recognition method flow diagrams based on high frequency statistics that Fig. 1 provides for the present invention；
A kind of CC System for attack recognition structure charts based on high frequency statistics that Fig. 2 provides for the present invention.
Embodiment
The present invention gives a kind of CC attack recognition method and system based on high frequency statistics, in order that the art
Personnel more fully understand the technical scheme in the embodiment of the present invention, and enable the above objects, features and advantages of the present invention more
Plus become apparent, technical scheme in the present invention is described in further detail below in conjunction with the accompanying drawings：
Present invention firstly provides a kind of CC attack recognition methods based on high frequency statistics, as shown in figure 1, including：
HTTP GET requests in S101 identification backbone traffics, and obtain source IP, mesh using the HTTP GET requests
IP and URI；
For example：Source IP：113.92.175.10；
Purpose IP：184.51.198.33；
GET/pki/crl/products/WinIntPCA.crl HTTP/1.1;
URI：http://crl.microsoft.com/pki/crl/products/WinIntPCA.crl;
S102 calculates hash values using the source IP, purpose IP and URI obtained；
By source IP, purpose IP and URI splice that to obtain a character string as follows：
113.92.175.10|184.51.198.33|
http://crl.microsoft.com/pki/crl/products/WinIntPCA.crl；
The hash values of the character string are calculated, for example：CRC64 values are（3857831069L, 3494489294L）；
S103 judge in buffering area whether there is with the hash values identical statistical item, if so, the then statistical item
Count value add 1, and perform S106, otherwise perform S104；
S104 judges to whether there is remaining space in buffering area, if so, the hash values are added as new statistical item
Enter buffering area, and set count value as 1, otherwise the count value of all statistical items of buffering area is subtracted 1, and perform S105；
S105 judges to whether there is in buffering area count value for 0 statistical item, if so, the statistical item is then removed,
And buffering area is added using the hash values as new statistical item, and set count value the hash values are abandoned as 1, otherwise,
Terminate；
S106, which works as, has the statistical item that unit interval count value exceedes given threshold in buffering area, then it is assumed that there is CC and attack
Hit, and alarm.
Preferably, the size of the buffering area is fixed.
Preferably, the given threshold is 10 times of conventional visit capacity.
For example：Under normal circumstances, conventional visit capacity is 20 times in the unit interval, and the visit capacity of unit interval reaches at present
To more than 200 times, then it is assumed that there is CC attacks.
Present invention also offers a kind of CC System for attack recognition based on high frequency statistics, as shown in Fig. 2 including：
Identification module 201, for recognizing the HTTP GET requests in backbone traffic, and utilizes the HTTP GET requests
Obtain source IP, purpose IP and URI；
Computing module 202, for calculating hash values using the source IP, purpose IP and URI that obtain；
First determination module 203, for judge in buffering area whether there is with the hash values identical statistical item, if
In the presence of, then the count value of the statistical item adds 1, and is handled by disposal module 206, otherwise by the second determination module 204 after
It is continuous to judge；
Second determination module 204, for judging to whether there is remaining space in buffering area, if in the presence of by the hash values
Buffering area is added as new statistical item, and sets count value as 1, otherwise subtracts the count value of all statistical items of buffering area
1, and judgement is continued by the 3rd determination module 205；
3rd determination module 205, for judging to whether there is in buffering area count value for 0 statistical item, if in the presence of,
The statistical item is removed, and buffering area is added using the hash values as new statistical item, and sets count value as 1, it is no
The hash values are then abandoned, are terminated；
Dispose module 206, when exist in buffering area unit interval count value exceed given threshold statistical item, then it is assumed that
There is CC attacks, and alarm.
Preferably, the size of the buffering area is fixed.
Preferably, the given threshold is 10 times of conventional visit capacity.
As described above, The present invention gives a kind of specific implementation of the CC attack recognition method and system based on high frequency statistics
Example, it is with the difference of conventional method, and traditional CC attack recognitions are the acess controls carried out for some websites；And
The CC attack recognitions that conventional method is used in backbone network, then may have very huge data to need processing, and CC is attacked
Hit and be likely to be flooded by the normal access of large-scale website, so as to can not effectively recognize.Method provided by the present invention is chosen solid
Determine the HTTP GET requests in the buffering area of size, identification backbone network, and hash values are calculated using source IP, purpose IP and URI, if
There is identical hash values in buffering area, then count value adds 1, otherwise judge whether buffering area also has remaining space, if so, then by institute
State hash values to add in buffering area, count value is set as 1, if without remaining space, the count value of all statistical items is subtracted
1, the count value that will appear from is removed for 0 statistical item, and the hash values are updated in buffering area.Utilize this dynamic statistics number
According to method, limit the data volume in buffering area；When the count value in the unit interval of a certain hash values exceedes normal condition
Lower averaged count to a certain degree after, then it is assumed that there occurs CC attack.Method provided by the present invention can utilize the money of very little
Source handles the mass data of backbone network, effectively recognizes that the high frequency in the unit interval in single source accesses situation, so that in time
It was found that CC is attacked.
Above example is used to illustrative and not limiting technical scheme.Appointing for spirit and scope of the invention is not departed from
What modification or local replacement, all should cover among scope of the presently claimed invention.
Claims (6)
1. a kind of CC attack recognition methods based on high frequency statistics, it is characterised in that including：
HTTP GET requests in step 1, identification backbone traffic, and obtain source IP, purpose using the HTTP GET requests
IP and URI；
Step 2, the source IP using acquisition, purpose IP and URI calculate hash values；
Step 3, judge in buffering area whether there is with the hash values identical statistical item, if in the presence of the statistical item
Count value add 1, and perform step 6, otherwise perform step 4；
Step 4, judge to whether there is remaining space in buffering area, if in the presence of the hash values are added as new statistical item
Enter buffering area, and set count value as 1, otherwise the count value of all statistical items of buffering area is subtracted 1, and perform step 5；
Step 5, judge in buffering area with the presence or absence of count value for 0 statistical item, if in the presence of, remove the statistical item,
And buffering area is added using the hash values as new statistical item, and set count value the hash values are abandoned as 1, otherwise,
Terminate；
Step 6, when exist in buffering area unit interval count value exceed given threshold statistical item, then it is assumed that there is CC and attack
Hit, and alarm.
2. the method as described in claim 1, it is characterised in that the size of the buffering area is fixed.
3. the method as described in claim 1, it is characterised in that the given threshold is 10 times of conventional visit capacity.
4. a kind of CC System for attack recognition based on high frequency statistics, it is characterised in that including：
Identification module, source is obtained for recognizing the HTTP GET requests in backbone traffic, and using the HTTP GET requests
IP, purpose IP and URI；
Computing module, for calculating hash values using the source IP, purpose IP and URI that obtain；
First determination module, for judge in buffering area whether there is with the hash values identical statistical item, if in the presence of,
The count value of the statistical item adds 1, and is handled by disposal module, is otherwise continued to judge by the second determination module；
Second determination module, for judge in buffering area whether there is remaining space, if in the presence of, using the hash values as newly
Statistical item adds buffering area, and sets count value as 1, otherwise subtracts 1 by the count value of all statistical items of buffering area, and by the
Three determination modules continue to judge；
3rd determination module, for judge in buffering area with the presence or absence of count value for 0 statistical item, if in the presence of removing institute
Statistical item is stated, and buffering area is added using the hash values as new statistical item, and sets count value as 1, is otherwise abandoned
The hash values, terminate；
Dispose module, when exist in buffering area unit interval count value exceed given threshold statistical item, then it is assumed that there is CC
Attack, and alarm.
5. system as claimed in claim 4, it is characterised in that the size of the buffering area is fixed.
6. system as claimed in claim 4, it is characterised in that the given threshold is 10 times of conventional visit capacity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310640806.2A CN103916379B (en) | 2013-12-04 | 2013-12-04 | A kind of CC attack recognition method and system based on high frequency statistics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310640806.2A CN103916379B (en) | 2013-12-04 | 2013-12-04 | A kind of CC attack recognition method and system based on high frequency statistics |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103916379A CN103916379A (en) | 2014-07-09 |
| CN103916379B true CN103916379B (en) | 2017-07-18 |
Family
ID=51041786
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310640806.2A Active CN103916379B (en) | 2013-12-04 | 2013-12-04 | A kind of CC attack recognition method and system based on high frequency statistics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103916379B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104539604B (en) * | 2014-12-23 | 2017-11-24 | 北京奇安信科技有限公司 | Website protection method and device |
| CN106789849B (en) * | 2015-11-24 | 2020-12-04 | 阿里巴巴集团控股有限公司 | CC attack identification method, node and system |
| CN105553974A (en) * | 2015-12-14 | 2016-05-04 | 中国电子信息产业集团有限公司第六研究所 | Prevention method of HTTP slow attack |
| CN108243149A (en) * | 2016-12-23 | 2018-07-03 | 北京华为数字技术有限公司 | A kind of network attack detecting method and device |
| CN110519266B (en) * | 2019-08-27 | 2021-04-27 | 四川长虹电器股份有限公司 | Cc attack detection method based on statistical method |
| CN110808967B (en) * | 2019-10-24 | 2022-04-08 | 新华三信息安全技术有限公司 | Detection method for challenging black hole attack and related device |
| CN114640504B (en) * | 2022-02-24 | 2024-02-06 | 京东科技信息技术有限公司 | CC attack protection method, device, equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101465760A (en) * | 2007-12-17 | 2009-06-24 | 北京启明星辰信息技术股份有限公司 | Method and system for detecting abnegation service aggression |
| CN101505218A (en) * | 2009-03-18 | 2009-08-12 | 杭州华三通信技术有限公司 | Detection method and apparatus for attack packet |
| CN101567815A (en) * | 2009-05-27 | 2009-10-28 | 清华大学 | Method for effectively detecting and defending domain name server (DNS) amplification attacks |
| CN101729569A (en) * | 2009-12-22 | 2010-06-09 | 成都市华为赛门铁克科技有限公司 | Distributed Denial of Service (DDOS) attack protection method, device and system |
| CN102510385A (en) * | 2011-12-12 | 2012-06-20 | 汉柏科技有限公司 | Method for preventing fragment attack of IP (Internet Protocol) datagram |
| CN103179132A (en) * | 2013-04-09 | 2013-06-26 | 中国信息安全测评中心 | Method and device for detecting and defending CC (challenge collapsar) |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN100370757C (en) * | 2004-07-09 | 2008-02-20 | 国际商业机器公司 | Method and system for dentifying a distributed denial of service (DDOS) attack within a network and defending against such an attack |
-
2013
- 2013-12-04 CN CN201310640806.2A patent/CN103916379B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101465760A (en) * | 2007-12-17 | 2009-06-24 | 北京启明星辰信息技术股份有限公司 | Method and system for detecting abnegation service aggression |
| CN101505218A (en) * | 2009-03-18 | 2009-08-12 | 杭州华三通信技术有限公司 | Detection method and apparatus for attack packet |
| CN101567815A (en) * | 2009-05-27 | 2009-10-28 | 清华大学 | Method for effectively detecting and defending domain name server (DNS) amplification attacks |
| CN101729569A (en) * | 2009-12-22 | 2010-06-09 | 成都市华为赛门铁克科技有限公司 | Distributed Denial of Service (DDOS) attack protection method, device and system |
| CN102510385A (en) * | 2011-12-12 | 2012-06-20 | 汉柏科技有限公司 | Method for preventing fragment attack of IP (Internet Protocol) datagram |
| CN103179132A (en) * | 2013-04-09 | 2013-06-26 | 中国信息安全测评中心 | Method and device for detecting and defending CC (challenge collapsar) |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103916379A (en) | 2014-07-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103916379B (en) | A kind of CC attack recognition method and system based on high frequency statistics | |
| Liu et al. | Efficient DDoS attacks mitigation for stateful forwarding in Internet of Things | |
| US10432652B1 (en) | Methods for detecting and mitigating malicious network behavior and devices thereof | |
| CN109194680B (en) | Network attack identification method, device and equipment | |
| CN102291390B (en) | Method for defending against denial of service attack based on cloud computation platform | |
| US8943586B2 (en) | Methods of detecting DNS flooding attack according to characteristics of type of attack traffic | |
| CN103856470B (en) | Detecting method of distributed denial of service attacking and detection device | |
| WO2018121331A1 (en) | Attack request determination method, apparatus and server | |
| CN102571547B (en) | Method and device for controlling hyper text transport protocol (HTTP) traffic | |
| JP2012522295A (en) | Filtering method, system, and network device | |
| CN101572700A (en) | Method for defending HTTP Flood distributed denial-of-service attack | |
| CN105282169A (en) | DDoS attack warning method and system based on SDN controller threshold | |
| CN102271068A (en) | Method for detecting DOS/DDOS (denial of service/distributed denial of service) attack | |
| CN101150586A (en) | CC attack prevention method and device | |
| WO2020037781A1 (en) | Anti-attack method and device for server | |
| CN108683686A (en) | A kind of Stochastic subspace name ddos attack detection method | |
| CN101577644B (en) | Peer-to-peer network application traffic identification method | |
| KR101200906B1 (en) | High Performance System and Method for Blocking Harmful Sites Access on the basis of Network | |
| CN112019533A (en) | Method and system for relieving DDoS attack on CDN system | |
| KR101188305B1 (en) | System and method for botnet detection using traffic analysis of non-ideal domain name system | |
| Huang et al. | An authentication scheme to defend against UDP DrDoS attacks in 5G networks | |
| CN104580228A (en) | System and method for generating blacklist for access requests from network | |
| CN104378358A (en) | HTTP Get Flood attack prevention method based on server log | |
| US20170149821A1 (en) | Method And System For Protection From DDoS Attack For CDN Server Group | |
| WO2011103835A2 (en) | User access control method, apparatus and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address | ||
| CP03 | Change of name, title or address |
Address after: 150010 building 7, innovation and entrepreneurship Plaza, science and technology innovation city, Harbin high tech Industrial Development Zone, Heilongjiang, China (No. 838, world Kun Road) Patentee after: Harbin antiy Technology Group Limited by Share Ltd Address before: 150090 room 506, Hongqi Street, Nangang District, Harbin Development Zone, Heilongjiang, China, 162 Patentee before: Harbin Antiy Technology Co., Ltd. |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: CC attack identification method and system based on high frequency statistics Effective date of registration: 20190718 Granted publication date: 20170718 Pledgee: Bank of Longjiang, Limited by Share Ltd, Harbin Limin branch Pledgor: Harbin antiy Technology Group Limited by Share Ltd Registration number: 2019230000007 |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| CP01 | Change in the name or title of a patent holder |
Address after: 150010 building 7, innovation and entrepreneurship Plaza, science and technology innovation city, Harbin high tech Industrial Development Zone, Heilongjiang, China (No. 838, world Kun Road) Patentee after: Antan Technology Group Co.,Ltd. Address before: 150010 building 7, innovation and entrepreneurship Plaza, science and technology innovation city, Harbin high tech Industrial Development Zone, Heilongjiang, China (No. 838, world Kun Road) Patentee before: Harbin Antian Science and Technology Group Co.,Ltd. |
|
| CP01 | Change in the name or title of a patent holder | ||
| PC01 | Cancellation of the registration of the contract for pledge of patent right |
Date of cancellation: 20211119 Granted publication date: 20170718 Pledgee: Bank of Longjiang Limited by Share Ltd. Harbin Limin branch Pledgor: Harbin Antian Science and Technology Group Co.,Ltd. Registration number: 2019230000007 |
|
| PC01 | Cancellation of the registration of the contract for pledge of patent right |