CN103873320B - Encryption method for recognizing flux and device - Google Patents
Encryption method for recognizing flux and device Download PDFInfo
- Publication number
- CN103873320B CN103873320B CN201310741617.4A CN201310741617A CN103873320B CN 103873320 B CN103873320 B CN 103873320B CN 201310741617 A CN201310741617 A CN 201310741617A CN 103873320 B CN103873320 B CN 103873320B
- Authority
- CN
- China
- Prior art keywords
- bag
- stream
- long
- statistical
- packet
- 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 28
- 230000004907 flux Effects 0.000 title claims abstract description 16
- 238000001514 detection method Methods 0.000 claims abstract description 62
- 238000013179 statistical model Methods 0.000 claims description 160
- 238000001914 filtration Methods 0.000 claims description 82
- 230000008878 coupling Effects 0.000 claims description 31
- 238000010168 coupling process Methods 0.000 claims description 31
- 238000005859 coupling reaction Methods 0.000 claims description 31
- 230000002457 bidirectional effect Effects 0.000 claims description 27
- 238000013519 translation Methods 0.000 claims description 11
- 238000011144 upstream manufacturing Methods 0.000 claims description 11
- 230000003068 static effect Effects 0.000 claims description 10
- 230000005540 biological transmission Effects 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 5
- 241000544061 Cuculus canorus Species 0.000 claims description 4
- 230000014509 gene expression Effects 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 230000006399 behavior Effects 0.000 description 3
- 238000013507 mapping Methods 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 239000000284 extract Substances 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 238000005206 flow analysis Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Landscapes
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
Description
Claims (11)
- It is 1. a kind of to encrypt method for recognizing flux, it is characterised in that:Stream to be identified is matched to recognize stream to be identified with statistical rules Application type;Wherein statistical rules includes:Regular head, deep-packet detection feature and statistical flow characteristic, the regular head are indicated The priority of the corresponding application type of this statistical rules and this statistical rules;It is described to match to recognize the application type of stream to be identified with statistical rules by stream to be identified, including:By the deep-packet detection characteristic matching of each bar statistical rules in stream to be identified and statistical rules set, the statistics that will match to Rule is generated as statistical rules subset;Stream to be identified is matched with the statistical flow characteristic of each bar statistical rules in the statistical rules subset, the statistics that will match to Rule is generated as statistical rules identification collection;Wherein, the statistical flow characteristic includes an at least statistical model and contained statistics mould Logical relation between formula;The statistical model is used to specify the bag distribution characteristics long of stream and meets the phase of bag distribution characteristics long Wang Bao is counted;When the number of the packet for meeting the bag distribution characteristics long that statistical model is specified in the stream to be identified reaches statistics When the expectation bag that pattern is specified is counted, then the stream to be identified is matched with the statistical model;When the stream to be identified is advised with statistics Then contained statistical model matching, and when meeting the logical relation between contained statistical model, then the stream to be identified with The statistical flow characteristic matching of this statistical rules;The corresponding application type of statistical rules with limit priority is concentrated to be identified as described treating statistical rules identification Recognize the application type of stream.
- 2. encryption method for recognizing flux according to claim 1, it is characterised in that:The deep-packet detection of the statistical rules Feature includes the logical relation between an at least pre-filtering sub-rule and contained pre-filtering sub-rule;The pre-filtering sub-rule include it is following at least one:Pre-filtering sub-rule based on character string, based on protocol characteristic bag Pre-filtering sub-rule long, the pre-filtering sub-rule based on static dynamic decryption, based on pre-filtering for having recognized stream contingency table Rule, the pre-filtering sub-rule based on port and IP address-based pre-filtering sub-rule;When the stream to be identified matches with the pre-filtering sub-rule contained by statistical rules, and meet contained pre-filtering cuckoo During logical relation between then, then the deep-packet detection characteristic matching of the stream to be identified and this statistical rules.
- 3. encryption method for recognizing flux according to claim 1, it is characterised in that:The bag distribution characteristics long includes as follows At least one:The bag feature long of translation specifications, the sequence signature of packet and packet of the packet in stream;Translation specifications of the packet in stream include:Bag direction and package location, wherein bag direction includes:Correspondence upstream side To uplink packet direction, the downstream packets direction in correspondence downstream direction and correspondence bidirectional flow direction without bag direction;Package location is Position Number when referring to that packet occurs on direction is flowed, including:Single fixed position, position discrete series and position continuum Between;The sequence signature of the packet includes:Continuity and order;The bag feature long of the packet includes:Wrap particular value long, wrap scope long and bag variable long.
- 4. encryption method for recognizing flux according to claim 3, it is characterised in that:The statistical model is included as follows at least One:Wrap sequence statistic pattern long, bag set statistical model long, bag length and repeat statistical model, the directional statistics pattern of position, bag Counting statistics pattern, bag average statistical model long, bag long and Data-Statistics pattern, wheel bag length and Data-Statistics pattern and byte transmitting-receiving ratio Statistical model;The bag sequence statistic pattern long is appointed as:There is the sequence of data packet with bag feature long on stream direction, package location, And meet continuity and order constraint, wherein expecting that bag counts the sequence of data packet length for being appointed as having bag feature long;The bag set statistical model long is appointed as:Exist in stream direction, bag apparatus and expect that bag counts a packet, packet Bag length be all contained in a set for bag feature long, and meet continuity and order constraint;The bag repeat pattern long is appointed as:Exist on stream direction, package location and expect that bag counts a packet, the bag of packet Length is equal to same bag feature long, and meets continuity;The directional statistics pattern of the position is appointed as:What the bag direction of the packet on bidirectional flow direction, package location was specified Bag direction, expects that bag counts the number for being set as package location;The bag counting statistics pattern is appointed as:Exist on bidirectional flow direction, package location and expect that bag counts a packet, data The bag direction of bag is all the same bag direction specified, and meets continuity;The bag average statistical model long is appointed as:The packet bag of all packets is long on stream direction, package location and is worth Match with a bag feature long, expect that bag counts the number for being set to package location;The wheel bag is long and Data-Statistics pattern is appointed as:The change in the direction of stream each time on bidirectional flow direction, package location is seen Make the beginning of new round packet, specify certain all packet of wheel packet bag it is long and value and one wrap feature phase long Match somebody with somebody, expect that bag counting is set to the wheel number;The byte transmitting-receiving is appointed as than statistical model:Upstream direction byte number on bidirectional flow direction, package location with it is descending The ratio and a bag feature long for flowing direction byte number match, and expect that bag counts the number for being set to position.
- 5. encryption method for recognizing flux according to claim 1, it is characterised in that:The stream to be identified is biography transport control protocol View TCP flow, the packet is the packet with pay(useful) load, and the encryption quanta recognition methods is at most to before stream to be identified 60 The individual packet with pay(useful) load is identified.
- It is 6. a kind of to encrypt flow identifying device, it is characterised in that:Including:Statistical rules deep-packet detection feature pre-filtering module, Statistical rules statistical flow characteristic matching module and stream recognition result detection module;Wherein described statistical rules deep-packet detection feature pre-filtering module, for stream to be identified is each with statistical rules set The deep-packet detection characteristic matching of bar statistical rules, the statistical rules that will match to is generated as statistical rules subset;The statistical rules statistical flow characteristic matching module, for stream to be identified to be counted with each bar in the statistical rules subset The statistical flow characteristic matching of rule, the statistical rules that will match to is generated as statistical rules identification collection;Wherein, the stream statistics are special Levy comprising the logical relation between an at least statistical model and contained statistical model;The statistical model is used to specify the bag of stream Distribution characteristics long is counted with the expectation bag for meeting bag distribution characteristics long;When meeting what statistical model was specified in the stream to be identified When the number for wrapping the packet of distribution characteristics long reaches the expectation bag that statistical model specifies and counts, then the stream to be identified and the system Meter pattern match;When the stream to be identified is matched with the statistical model contained by statistical rules, and meet contained statistical model Between logical relation when, then it is described it is to be identified stream matched with the statistical flow characteristic of this statistical rules;The stream recognition result detection module, for statistical rules identification to be concentrated into the statistical rules with limit priority Corresponding application type is identified as the application type of the stream to be identified.
- It is 7. according to claim 6 to encrypt flow identifying device, it is characterised in that:The statistical rules deep-packet detection is special Pre-filtering module is levied, including:The basic matching unit of feature and statistical rules pre-filtering logic discrimination unit;The basic matching unit of feature includes:The pre-filtering long of parallel character string single mode matching subelement, protocol characteristic bag Coupling subelement, static dynamic decrypt coupling subelement, have recognized stream contingency table coupling subelement, port match subelement and IP Address coupling subelement;The statistical rules pre-filtering logic discrimination unit, for the subelement to being matched in the basic matching unit of the feature Between logical relation verified, generate statistical rules subset.
- It is 8. according to claim 6 to encrypt flow identifying device, it is characterised in that:The bag distribution characteristics long includes as follows At least one:The bag feature long of translation specifications, the sequence signature of packet and packet of the packet in stream;Translation specifications of the packet in stream include:The uplink packet direction of correspondence upstream direction, correspondence downstream direction Downstream packets direction and correspondence bidirectional flow direction without bag direction;The sequence signature of the packet includes:Continuity and order;The bag feature long of the packet includes:Wrap particular value long, wrap scope long and bag variable long.
- It is 9. according to claim 8 to encrypt flow identifying device, it is characterised in that:The statistical rules statistical flow characteristic Include with module:Statistical model matching unit and statistical model matching result logic discrimination unit;The statistical model matching unit include it is following at least one:Wrap the set long of sequence statistic pattern match subelement long, bag Statistical model coupling subelement, bag are long to repeat statistical model coupling subelement, the directional statistics pattern match subelement of position, bag Counting statistics pattern match subelement, bag average statistical model coupling subelement long, bag long and Data-Statistics pattern match subelement, Wheel bag is long to be received and dispatched than statistical model coupling subelement with Data-Statistics pattern match subelement and byte;The statistical model matching result logic discrimination unit, between the subelement that is matched to statistical model matching unit Logical relation verified, generate statistical rules identification collection.
- It is 10. according to claim 9 to encrypt flow identifying device, it is characterised in that:Sequence statistic pattern long is wrapped to be appointed as: There is the sequence of data packet with bag feature long on stream direction, package location, and meet continuity and order constraint, its mid-term Wang Bao counts the sequence of data packet length for being appointed as having bag feature long;Bag set statistical model long is appointed as:Exist in stream direction, bag apparatus and expect that bag counts a packet, the bag of packet Length is all contained in a set for bag feature long, and meets continuity and order constraint;Repeat pattern long is wrapped to be appointed as:Exist on stream direction, package location and expect that bag counts a packet, the bag of packet is grown all Equal to same bag feature long, and meet continuity;The directional statistics pattern of position is appointed as:The Bao Fang that the bag direction of the packet on bidirectional flow direction, package location is specified To expecting that bag is counted and be set as the number of package location;Bag counting statistics pattern is appointed as:Exist on bidirectional flow direction, package location and expect that bag counts a packet, packet Bag direction is all the same bag direction specified, and meets continuity;Average statistical model long is wrapped to be appointed as:The packet bag of all packets is long on stream direction, package location and is worth and one Wrap feature long to match, expect that bag counts the number for being set to package location;Wheel bag is long and Data-Statistics pattern is appointed as:The change in the direction of stream each time on bidirectional flow direction, package location is regarded as newly The beginning of one wheel packet, specify certain all packet of wheel packet bag it is long and value wrap feature long with one and match, the phase Wang Bao is counted and is set to the wheel number;Byte transmitting-receiving is appointed as than statistical model:Upstream direction byte number and downstream side on bidirectional flow direction, package location Match to the ratio and a bag feature long of byte number, expect that bag counts the number for being set to position.
- 11. encryption flow identifying devices according to claim 6, it is characterised in that:The stream to be identified is controlled for transmission Agreement TCP flow, the packet is the packet with pay(useful) load, and the encryption flow identifying device is at most to stream to be identified Preceding 60 packets with pay(useful) load are identified.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201310741617.4A CN103873320B (en) | 2013-12-27 | 2013-12-27 | Encryption method for recognizing flux and device |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201310741617.4A CN103873320B (en) | 2013-12-27 | 2013-12-27 | Encryption method for recognizing flux and device |
Publications (2)
Publication Number | Publication Date |
---|---|
CN103873320A CN103873320A (en) | 2014-06-18 |
CN103873320B true CN103873320B (en) | 2017-06-13 |
Family
ID=50911465
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201310741617.4A Active CN103873320B (en) | 2013-12-27 | 2013-12-27 | Encryption method for recognizing flux and device |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN103873320B (en) |
Families Citing this family (27)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104954365B (en) * | 2015-05-27 | 2018-10-16 | 北京亿赛通网络安全技术有限公司 | A kind of method of fast automatic identification refined net behavior |
CN105406993A (en) * | 2015-10-28 | 2016-03-16 | 中国人民解放军信息工程大学 | Encrypted stream recognition method and device |
CN106650760A (en) | 2015-10-28 | 2017-05-10 | 华为技术有限公司 | Method and device for recognizing user behavioral object based on flow analysis |
CN105429819B (en) * | 2015-11-04 | 2018-08-17 | 深圳市蜂联科技有限公司 | A kind of long detection method of packet of application identification |
CN105245551B (en) * | 2015-11-04 | 2018-11-02 | 深圳市蜂联科技有限公司 | A kind of application and identification method based on DNS and the long combination of packet |
CN106209506B (en) * | 2016-06-30 | 2019-10-25 | 瑞斯康达科技发展股份有限公司 | A kind of virtualization deep-packet detection flow analysis method and system |
CN106534135B (en) * | 2016-11-16 | 2020-07-17 | 新华三技术有限公司 | Method and device for generating flow detection rule |
CN106850344B (en) * | 2017-01-22 | 2019-10-29 | 中国人民解放军信息工程大学 | Encryption method for recognizing flux based on stream gradient guiding |
CN107508764B (en) * | 2017-07-03 | 2020-04-10 | 网宿科技股份有限公司 | Network data traffic type identification method and device |
CN107528837B (en) * | 2017-08-17 | 2020-06-09 | 深信服科技股份有限公司 | Encrypted video identification method and device, computer device and readable storage medium |
CN109802924B (en) * | 2017-11-17 | 2022-05-17 | 华为技术有限公司 | Method and device for identifying encrypted data stream |
CN109936512B (en) | 2017-12-15 | 2021-10-01 | 华为技术有限公司 | Flow analysis method, public service flow attribution method and corresponding computer system |
CN108377223B (en) * | 2018-01-05 | 2019-12-06 | 网宿科技股份有限公司 | multi-packet identification method, data packet identification method and flow guiding method |
CN107948022B (en) * | 2018-01-11 | 2021-04-30 | 北京安博通科技股份有限公司 | Identification method and identification device for peer-to-peer network traffic |
CN108833360B (en) * | 2018-05-23 | 2019-11-08 | 四川大学 | A kind of malice encryption method for recognizing flux based on machine learning |
CN110768933B (en) * | 2018-07-27 | 2022-08-09 | 深信服科技股份有限公司 | Network flow application identification method, system and equipment and storage medium |
CN108881306B (en) * | 2018-08-08 | 2020-04-28 | 西安交通大学 | Encrypted flow analysis defense method based on data packet size sequence |
CN109275045B (en) * | 2018-09-06 | 2020-12-25 | 东南大学 | DFI-based mobile terminal encrypted video advertisement traffic identification method |
CN109525587A (en) * | 2018-11-30 | 2019-03-26 | 新华三信息安全技术有限公司 | A kind of recognition methods of data packet and device |
CN109672687B (en) * | 2018-12-31 | 2021-04-13 | 南京理工大学 | HTTP confusion flow detection method based on suspicion degree evaluation |
CN111245850A (en) * | 2020-01-15 | 2020-06-05 | 福建奇点时空数字科技有限公司 | Encrypted P2P protocol identification method based on connection statistical rule analysis |
CN112036518B (en) * | 2020-11-05 | 2021-02-02 | 中国人民解放军国防科技大学 | Application program flow classification method based on data packet byte distribution and storage medium |
CN112994931B (en) * | 2021-02-05 | 2023-01-17 | 绿盟科技集团股份有限公司 | Rule matching method and equipment |
CN112866289B (en) * | 2021-03-02 | 2022-09-30 | 恒为科技(上海)股份有限公司 | Method and system for extracting feature rule |
CN113938436B (en) * | 2021-09-26 | 2023-05-26 | 中国联合网络通信集团有限公司 | Method and device for identifying service type of data |
CN114584632B (en) * | 2022-02-24 | 2023-05-16 | 成都北中网芯科技有限公司 | Deep packet inspection method and device |
CN115378741B (en) * | 2022-10-25 | 2023-03-21 | 中国电子科技集团公司第三十研究所 | Early identification method for fine-grained behavior flow of lightweight encryption application |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1764951A1 (en) * | 2005-09-15 | 2007-03-21 | Alcatel | Statistical trace-based method, apparatus, node and system for real-time traffic classification |
CN101594303A (en) * | 2009-07-10 | 2009-12-02 | 清华大学 | The quick net packet classifying method of traffic statistics information Network Based |
CN101741644A (en) * | 2009-12-16 | 2010-06-16 | 成都市华为赛门铁克科技有限公司 | Flow detection method and apparatus |
CN102255754A (en) * | 2011-07-08 | 2011-11-23 | 中国人民解放军国防科学技术大学 | Serial accessing high speed backbone network traffic acquisition and monitoring method |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2013535895A (en) * | 2010-07-23 | 2013-09-12 | 日本電気株式会社 | Communication system, node, statistical information collecting apparatus, statistical information collecting method and program |
-
2013
- 2013-12-27 CN CN201310741617.4A patent/CN103873320B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1764951A1 (en) * | 2005-09-15 | 2007-03-21 | Alcatel | Statistical trace-based method, apparatus, node and system for real-time traffic classification |
CN101594303A (en) * | 2009-07-10 | 2009-12-02 | 清华大学 | The quick net packet classifying method of traffic statistics information Network Based |
CN101741644A (en) * | 2009-12-16 | 2010-06-16 | 成都市华为赛门铁克科技有限公司 | Flow detection method and apparatus |
CN102255754A (en) * | 2011-07-08 | 2011-11-23 | 中国人民解放军国防科学技术大学 | Serial accessing high speed backbone network traffic acquisition and monitoring method |
Also Published As
Publication number | Publication date |
---|---|
CN103873320A (en) | 2014-06-18 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN103873320B (en) | Encryption method for recognizing flux and device | |
Dyer et al. | Protocol misidentification made easy with format-transforming encryption | |
CN109756501B (en) | High-privacy network proxy method and system based on HTTP (hyper text transport protocol) | |
CN104038389A (en) | Multiple application protocol identification method and device | |
CN105162626B (en) | Network flow depth recognition system and recognition methods based on many-core processor | |
CN102315974A (en) | Stratification characteristic analysis-based method and apparatus thereof for on-line identification for TCP, UDP flows | |
WO2007109445A1 (en) | Monitoring regular expressions on out-of-order streams | |
CN112822167A (en) | Abnormal TLS encrypted traffic detection method and system | |
CN102217251A (en) | Data forwarding method, data processing method, system and device thereof | |
Wang et al. | Design and implementation of an intrusion detection system by using extended BPF in the Linux kernel | |
Bader et al. | MalDIST: From encrypted traffic classification to malware traffic detection and classification | |
CN103780501A (en) | Peer-to-peer network traffic identification method of inseparable-wavelet support vector machine | |
Limmer et al. | Improving the performance of intrusion detection using dialog-based payload aggregation | |
CN104660592B (en) | A kind of load distributing method based on secure socket layer protocol feature | |
Meng et al. | Netgpt: Generative pretrained transformer for network traffic | |
Mehta et al. | A survey of network based traffic classification methods | |
Aghaei-Foroushani et al. | On evaluating ip traceback schemes: a practical perspective | |
Shamsimukhametov et al. | Are neural networks the best way for encrypted traffic classification? | |
CN105049456B (en) | A kind of secret communication method based on web page interlinkage request | |
CN101771697B (en) | Network data stream identification method based on pattern matching method | |
CN101854366A (en) | Peer-to-peer network flow-rate identification method and device | |
Kapoor et al. | Rexactor: Automatic regular expression signature generation for stateless packet inspection | |
Tavallaee et al. | Online classification of network flows | |
CN110417804A (en) | A kind of bidirectional identity authentication encryption communication method and system suitable for chip microcontroller | |
Erlacher et al. | Improving network monitoring through aggregation of HTTP/1.1 Dialogs in IPFIX |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
C53 | Correction of patent for invention or patent application | ||
CB02 | Change of applicant information |
Address after: 100085 Haidian District East Road, No. three, China control building, floor, floor, 1 Applicant after: BEIJING TOPSEC TECHNOLOGY CO., LTD. Address before: 100085 Haidian District East Road, No. three, China control building, floor, floor, 1 Applicant before: Beijing heaven melts letter Science Technologies Co., Ltd. |
|
COR | Change of bibliographic data |
Free format text: CORRECT: APPLICANT; FROM: BEIJING HEAVEN MELTS LETTER SCIENCE TECHNOLOGIES CO., LTD. TO: BEIJING TOPSEC TECHNOLOGY CO., LTD. |
|
C53 | Correction of patent for invention or patent application | ||
CB02 | Change of applicant information |
Address after: 100085 Haidian District East Road, No. three, China control building, floor, floor, 1 Applicant after: Beijing heaven melts letter Science Technologies Co., Ltd. Address before: 100085 Haidian District East Road, No. three, China control building, floor, floor, 1 Applicant before: BEIJING TOPSEC TECHNOLOGY CO., LTD. |
|
COR | Change of bibliographic data |
Free format text: CORRECT: APPLICANT; FROM: BEIJING TOPSEC TECHNOLOGY CO., LTD. TO: BEIJING HEAVEN MELTS LETTER SCIENCE TECHNOLOGIES CO., LTD. |
|
CB02 | Change of applicant information |
Address after: 100085 Beijing, East Road, No. 1, building on the north side of the building, Room 301, room 3 Applicant after: BEIJING TOPSEC TECHNOLOGY CO., LTD. Address before: 100085 Haidian District East Road, No. three, China control building, floor, floor, 1 Applicant before: Beijing heaven melts letter Science Technologies Co., Ltd. |
|
COR | Change of bibliographic data | ||
CB02 | Change of applicant information | ||
CB02 | Change of applicant information |
Address after: 100085 Beijing, East Road, No. 1, building on the north side of the building, Room 301, room 3 Applicant after: Beijing heaven melts letter Science Technologies Co., Ltd. Address before: 100085 Beijing, East Road, No. 1, building on the north side of the building, Room 301, room 3 Applicant before: BEIJING TOPSEC TECHNOLOGY CO., LTD. |
|
GR01 | Patent grant | ||
GR01 | Patent grant |