CN111262854A - Internet anti-cheating behavior method, device, equipment and readable storage medium - Google Patents
Internet anti-cheating behavior method, device, equipment and readable storage medium Download PDFInfo
- Publication number
- CN111262854A CN111262854A CN202010041772.5A CN202010041772A CN111262854A CN 111262854 A CN111262854 A CN 111262854A CN 202010041772 A CN202010041772 A CN 202010041772A CN 111262854 A CN111262854 A CN 111262854A
- Authority
- CN
- China
- Prior art keywords
- behavior
- cheating
- sequence
- internet
- user
- 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.)
- Pending
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1425—Traffic logging, e.g. anomaly detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0225—Avoiding frauds
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/10—Network architectures or network communication protocols for network security for controlling access to devices or network resources
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1441—Countermeasures against malicious traffic
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
Landscapes
- Engineering & Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Computing Systems (AREA)
- Computer Hardware Design (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Accounting & Taxation (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention discloses a method, a device, equipment and a readable storage medium for preventing internet cheating, wherein the method comprises the following steps: acquiring a behavior sequence accessed by a user, wherein the behavior sequence is a click sequence and operation interval time for completing the target behavior; inputting the behavior sequence into a trained preset model, and judging whether cheating behaviors exist or not; and if the suspected cheating behavior exists, inhibiting the access. The behavior sequence is used for judging whether cheating exists or not, so that the judgment from the physical level of simple equipment is avoided, the behavior sequence is used for representing the user use habits such as the operation mode, the operation interval and the like of the user, the fitting of the behavior characteristics of the real user can be approximated, and the malicious user can be accurately discriminated.
Description
Technical Field
The invention relates to the field of internet, in particular to a method, a device, equipment and a readable storage medium for preventing internet cheating.
Background
With the rapid development of mobile internet and e-commerce platforms and the popularization and popularity of electronic channels, more and more marketing activities are transferred to internet channels for development, and online marketing modes have deeply penetrated into the daily lives of mobile internet users, such as various types of marketing activities like group buying, killing in seconds, lottery and the like.
Meanwhile, the cheating phenomenon that online valuable resources originally planned to be issued to target users are maliciously seized through various technical means occurs. Most of the existing anti-cheating means are based on device fingerprints, accessed IP addresses and the like, and a distributed group brushing mode similar to a P2P network cannot be prevented, so that how to accurately identify cheating orders becomes a problem to be solved urgently.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides an internet anti-cheating behavior method which can effectively identify existing cheating phenomena.
The invention also provides an anti-cheating behavior device for the internet.
The invention also provides equipment for preventing the internet cheating.
The invention also provides a computer readable storage medium.
In a first aspect, an embodiment of the present invention provides an internet anti-cheating method: the method comprises the following steps: acquiring a behavior sequence accessed by a user, wherein the behavior sequence is a click sequence and operation interval time for completing the target behavior;
inputting the behavior sequence into a trained preset model, and judging whether cheating behaviors exist or not;
and if the suspected cheating behavior exists, inhibiting the access.
The internet anti-cheating behavior method provided by the embodiment of the invention at least has the following beneficial effects: by bringing the access behavior sequence of the user into the preset model, the cheating user is effectively identified, and the accuracy is high.
According to another embodiment of the present invention, an internet anti-cheating behavior method includes a preset model training step, which specifically includes:
defining preset category target behaviors;
establishing a behavior sequence sample library of a normal user aiming at the target behavior, and taking the probability of behavior sequence occurrence in the historical behavior of the normal user as a standard for entering the behavior sequence sample library;
defining a similarity function, wherein the similarity function is used for describing the similarity degree with the normal user behavior pattern.
Further, the inhibiting operation includes blocking access and/or adding a verification step.
Further, recording the cheating behavior in a log.
Further, the method also comprises the step of verifying whether the accessed device data has an exception.
Further, the device data includes: device fingerprint and/or IP address and/or access time.
In a second aspect, an embodiment of the present invention provides an internet anti-cheating behavior device, including:
an obtaining unit, configured to obtain a behavior sequence accessed by a user, where the behavior sequence is a click sequence and an operation interval time for completing the target behavior
The judging unit is used for inputting the behavior sequence into a trained preset model and judging whether cheating behaviors exist or not;
and the access suppression unit is used for performing access suppression on the users suspected of cheating.
In a third aspect, an embodiment of the present invention provides an internet anti-cheating behavior device, including:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the internet anti-cheating method.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions for causing a computer to execute the internet anti-cheating method.
Drawings
FIG. 1 is a flowchart illustrating an embodiment of a method for anti-cheating on the Internet according to the present invention;
fig. 2 is a flowchart illustrating a specific embodiment of a preset model training step of the internet anti-cheating behavior method according to the embodiment of the present invention.
Detailed Description
The concept and technical effects of the present invention will be clearly and completely described below in conjunction with the embodiments to fully understand the objects, features and effects of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and those skilled in the art can obtain other embodiments without inventive effort based on the embodiments of the present invention, and all embodiments are within the protection scope of the present invention.
In the description of the present invention, if an orientation description is referred to, for example, the orientations or positional relationships indicated by "upper", "lower", "front", "rear", "left", "right", etc. are based on the orientations or positional relationships shown in the drawings, only for convenience of describing the present invention and simplifying the description, but not for indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. If a feature is referred to as being "disposed," "secured," "connected," or "mounted" to another feature, it can be directly disposed, secured, or connected to the other feature or indirectly disposed, secured, connected, or mounted to the other feature.
In the description of the embodiments of the present invention, if "a number" is referred to, it means one or more, if "a plurality" is referred to, it means two or more, if "greater than", "less than" or "more than" is referred to, it is understood that the number is not included, and if "greater than", "lower" or "inner" is referred to, it is understood that the number is included. If reference is made to "first" or "second", this should be understood to distinguish between features and not to indicate or imply relative importance or to implicitly indicate the number of indicated features or to implicitly indicate the precedence of the indicated features.
Referring to fig. 1, a flowchart of an internet anti-cheating behavior method in an embodiment of the present invention is shown. The method specifically comprises the following steps:
s1, acquiring a behavior sequence accessed by a user, wherein the behavior sequence is a click sequence and an operation interval time for completing the target behavior;
s2, inputting the behavior sequence into a trained preset model, and judging whether cheating behaviors exist or not;
and S3, if the cheating is suspected, suppressing the access.
Whether cheating phenomena exist or not is judged through the behavior sequence, the judgment from the physical layer of simple equipment is avoided, the operation modes, operation intervals and other user use habits of the users are represented by the behavior sequence, the fitting of behavior characteristics of real users can be approximated, and malicious users can be accurately discriminated.
Referring to fig. 2, fig. 2 shows a preset model training step including:
s01, defining preset category target behaviors;
specifically, the target behavior includes: purchase, browse, join collection, join shopping carts, etc.
S02, establishing a behavior sequence sample library of a normal user aiming at the target behavior, and taking the probability of behavior sequence occurrence in the historical behavior of the normal user as a standard for entering the behavior sequence sample library;
specifically, for example, taking the target behavior purchase as an example, the corresponding behavior sequence includes but is not limited to:
sequence 1: browsing commodity details, selecting commodity quantity and adding the commodity into a shopping cart;
sequence 2: browsing the commodity details and clicking to purchase immediately;
and (3) sequence: clicking a shopping cart and clicking to purchase immediately;
and extracting sequences of the selected normal user behavior sequence sample library according to the occurrence probability of each sequence in the normal user historical behaviors.
And S03, defining a similarity function, wherein the similarity function is used for describing the similarity degree with the normal user behavior pattern.
If the similarity value is within a certain interval range, judging the user to be in normal behavior; and if the range is exceeded, judging as fraudulent behavior. Thus, a user behavior model is established for identifying whether the behavior is a cheating behavior.
After the model training is completed, the model also needs to be tested. Firstly, randomly extracting a plurality of data for manual examination to obtain a sampling prediction result of the batch of test data. Then, the model is used for automatic testing to obtain a test result.
And according to the prediction result and the test result, calculating indexes such as accuracy and recall rate of the model, optimizing the model, and finally obtaining the model meeting the use requirement.
Wherein, the accuracy calculation refers to the ratio of the number of classified correct samples to the total number of samples for a given data set. And using a group of test data as input, obtaining behavior type (cheating/normal) judgment of each test data through a preset model, judging the test data to be correct if the test data are consistent with a manual auditing result, and judging the test data to be wrong if the test data are not consistent with the manual auditing result. And calculating the ratio judged to be correct in the whole group of data to obtain the accuracy of the model.
And the recall ratio calculation is used for explaining the ratio of the positive examples judged to be true in the model to the total positive examples. And using a group of test data as input, obtaining the judgment of the behavior type of each test data through a preset model, judging the test data to be correct if the test data is consistent with the manual examination result and is normal user behavior, and judging the test data to be wrong if the test data is not consistent with the manual examination result and is not normal user behavior. And calculating the ratio of the number of the normal user behaviors in the whole group of data to the number of all the normal user behaviors to obtain the recall rate of the model.
And after the preset model is trained, packaging the input end of the model into a WEB API service calling interface for subsequent use.
In another embodiment, the method further comprises preprocessing the training data, including: unified coding, unreasonable data elimination and data normalization processing. Because the originally collected user access data has a certain proportion of incomplete data and error data, the quality of data analysis is improved.
Specifically, the uniform coding means that a uniform coding format and a coding set are adopted for different user behaviors, and a uniform data coding mode is guaranteed to be input into a subsequent training model.
The unreasonable data elimination refers to elimination of obvious unreasonable and incomplete data generated due to system defects and the like, so that the interference of error data with normal model training is avoided.
In the data normalization process, different index data often have different dimensions and dimension units, which affect the result of data analysis, and in order to eliminate the dimension effect between the index data, data normalization process is required to solve the comparability between the index data. After the raw data is subjected to data standardization processing, all indexes are in the same order of magnitude, so that the convergence speed of model training is improved, and the precision of the model is improved.
In another embodiment, the suppression operation includes blocking access and/or adding a verification step, such as prompting for a verification code or sliding a progress bar to match a picture.
And when the suspected cheating behavior is detected, recording the cheating behavior into a log.
In another embodiment, the method further comprises the step of identifying the cheating user based on the device characteristics and the network access behavior, and particularly verifying whether the accessed device data has an abnormality, wherein the device data comprises but is not limited to a device fingerprint and/or an IP address and/or an access time.
Specifically, it includes identifying whether there is a single device multi PV (Page View), or a small number of device multi PVs; associating the device fingerprint with the IP address, establishing a time domain mapping relation, and identifying malicious brushing based on the device pool and the IP pool; identifying an agent IP and preventing batch cheating behaviors through the operation of the agent IP; screening an IP blacklist and blocking access from malicious IP; screening for device blacklists, blocking access from malicious devices, etc.
One embodiment of the invention provides an internet anti-cheating behavior device, which comprises:
an obtaining unit, configured to obtain a behavior sequence accessed by a user, where the behavior sequence is a click sequence and an operation interval time for completing the target behavior
The judging unit is used for inputting the behavior sequence into a trained preset model and judging whether cheating behaviors exist or not;
and the access suppression unit is used for performing access suppression on the users suspected of cheating.
An embodiment of the present invention provides an internet anti-cheating behavior device, including:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the internet anti-cheating method.
An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions for causing a computer to execute the internet anti-cheating behavior method.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention. Furthermore, the embodiments of the present invention and the features of the embodiments may be combined with each other without conflict.
Claims (9)
1. An internet anti-cheating behavior method is characterized by comprising the following steps:
acquiring a behavior sequence accessed by a user, wherein the behavior sequence is a click sequence and operation interval time for completing a target behavior;
inputting the behavior sequence into a trained preset model, and judging whether cheating behaviors exist or not;
and if the suspected cheating behavior exists, inhibiting the access.
2. The internet anti-cheating behavior method according to claim 1, comprising a preset model training step, specifically comprising:
defining preset category target behaviors;
establishing a behavior sequence sample library of a normal user aiming at the target behavior, and taking the probability of behavior sequence occurrence in the historical behavior of the normal user as a standard for entering the behavior sequence sample library;
defining a similarity function, wherein the similarity function is used for describing the similarity degree with the normal user behavior pattern.
3. The internet anti-cheating act method according to claim 1, wherein said inhibiting operation comprises blocking access and/or adding a verification step.
4. The internet anti-cheating act method of claim 1, further comprising recording the cheating act in a log.
5. The internet anti-cheating act method according to any of claims 1-4, further comprising the steps of: and verifying whether the accessed device data has an exception.
6. The internet anti-cheating act method of claim 5, wherein said device data comprises: device fingerprint and/or IP address and/or access time.
7. An internet anti-cheating behavior device, comprising:
an obtaining unit, configured to obtain a behavior sequence accessed by a user, where the behavior sequence is a click sequence and an operation interval time for completing the target behavior
The judging unit is used for inputting the behavior sequence into a trained preset model and judging whether cheating behaviors exist or not;
and the access suppression unit is used for performing access suppression on the users suspected of cheating.
8. An internet anti-cheating behavior device, comprising:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the internet anti-cheating method of any of claims 1-6.
9. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the internet anti-cheating act method of any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010041772.5A CN111262854A (en) | 2020-01-15 | 2020-01-15 | Internet anti-cheating behavior method, device, equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010041772.5A CN111262854A (en) | 2020-01-15 | 2020-01-15 | Internet anti-cheating behavior method, device, equipment and readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111262854A true CN111262854A (en) | 2020-06-09 |
Family
ID=70954053
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010041772.5A Pending CN111262854A (en) | 2020-01-15 | 2020-01-15 | Internet anti-cheating behavior method, device, equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111262854A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111787009A (en) * | 2020-06-30 | 2020-10-16 | 北京明略昭辉科技有限公司 | Abnormal flow judgment method and device |
| CN111865987A (en) * | 2020-07-21 | 2020-10-30 | 百度在线网络技术（北京）有限公司 | Cheating flow processing method, device, equipment and storage medium |
| CN112733045A (en) * | 2021-04-06 | 2021-04-30 | 北京轻松筹信息技术有限公司 | User behavior analysis method and device and electronic equipment |
| CN113569949A (en) * | 2021-07-28 | 2021-10-29 | 广州博冠信息科技有限公司 | Abnormal user identification method and device, electronic equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107153656A (en) * | 2016-03-03 | 2017-09-12 | 阿里巴巴集团控股有限公司 | A kind of information search method and device |
| CN108109011A (en) * | 2017-12-28 | 2018-06-01 | 北京皮尔布莱尼软件有限公司 | A kind of anti-cheat method of advertisement and computing device |
| CN108596632A (en) * | 2017-03-07 | 2018-09-28 | 北京嘀嘀无限科技发展有限公司 | A kind of anti-cheating recognition methods and system based on order attributes and user behavior |
| CN109003137A (en) * | 2018-07-23 | 2018-12-14 | 广州至真信息科技有限公司 | A kind of anti-method and device practised fraud of advertisement |
| US20190141068A1 (en) * | 2017-09-21 | 2019-05-09 | Camp Mobile Corporation | Online service abuser detection |
| CN110097389A (en) * | 2018-01-31 | 2019-08-06 | 上海甚术网络科技有限公司 | A kind of anti-cheat method of ad traffic |
| CN110659954A (en) * | 2019-08-29 | 2020-01-07 | 北京三快在线科技有限公司 | Cheating identification method and device, electronic equipment and readable storage medium |
-
2020
- 2020-01-15 CN CN202010041772.5A patent/CN111262854A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107153656A (en) * | 2016-03-03 | 2017-09-12 | 阿里巴巴集团控股有限公司 | A kind of information search method and device |
| CN108596632A (en) * | 2017-03-07 | 2018-09-28 | 北京嘀嘀无限科技发展有限公司 | A kind of anti-cheating recognition methods and system based on order attributes and user behavior |
| US20190141068A1 (en) * | 2017-09-21 | 2019-05-09 | Camp Mobile Corporation | Online service abuser detection |
| CN108109011A (en) * | 2017-12-28 | 2018-06-01 | 北京皮尔布莱尼软件有限公司 | A kind of anti-cheat method of advertisement and computing device |
| CN110097389A (en) * | 2018-01-31 | 2019-08-06 | 上海甚术网络科技有限公司 | A kind of anti-cheat method of ad traffic |
| CN109003137A (en) * | 2018-07-23 | 2018-12-14 | 广州至真信息科技有限公司 | A kind of anti-method and device practised fraud of advertisement |
| CN110659954A (en) * | 2019-08-29 | 2020-01-07 | 北京三快在线科技有限公司 | Cheating identification method and device, electronic equipment and readable storage medium |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111787009A (en) * | 2020-06-30 | 2020-10-16 | 北京明略昭辉科技有限公司 | Abnormal flow judgment method and device |
| CN111865987A (en) * | 2020-07-21 | 2020-10-30 | 百度在线网络技术（北京）有限公司 | Cheating flow processing method, device, equipment and storage medium |
| CN112733045A (en) * | 2021-04-06 | 2021-04-30 | 北京轻松筹信息技术有限公司 | User behavior analysis method and device and electronic equipment |
| CN112733045B (en) * | 2021-04-06 | 2021-06-22 | 北京轻松筹信息技术有限公司 | User behavior analysis method and device and electronic equipment |
| CN113569949A (en) * | 2021-07-28 | 2021-10-29 | 广州博冠信息科技有限公司 | Abnormal user identification method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111262854A (en) | Internet anti-cheating behavior method, device, equipment and readable storage medium | |
| CN109922032B (en) | Method, device, equipment and storage medium for determining risk of logging in account | |
| CN103927307B (en) | A kind of method and apparatus of identification website user | |
| CN109933984B (en) | Optimal clustering result screening method and device and electronic equipment | |
| CN111401416A (en) | Abnormal website identification method and device and abnormal countermeasure identification method | |
| CN113032792B (en) | System business vulnerability detection method, system, equipment and storage medium | |
| CN108334758A (en) | A kind of detection method, device and the equipment of user's ultra vires act | |
| CN107679626A (en) | Machine learning method, device, system, storage medium and equipment | |
| CN108206769B (en) | Method, apparatus, device and medium for filtering network quality alarms | |
| CN113098887A (en) | Phishing website detection method based on website joint characteristics | |
| CN112422574A (en) | Risk account identification method, device, medium and electronic equipment | |
| CN106790025B (en) | Method and device for detecting link maliciousness | |
| CN112529575A (en) | Risk early warning method, equipment, storage medium and device | |
| CN116051185B (en) | Advertisement position data abnormality detection and screening method | |
| CN117254983A (en) | Method, device, equipment and storage medium for detecting fraud-related websites | |
| CN115795475A (en) | Method and device for determining software system risk and electronic equipment | |
| CN113553571B (en) | Method and device for measuring reliability of terminal equipment | |
| CN115348117A (en) | User level unauthorized behavior determination method and device | |
| CN115174205A (en) | Network space safety real-time monitoring method, system and computer storage medium | |
| CN114553473A (en) | Abnormal login behavior detection system and method based on login IP and login time | |
| CN113918435A (en) | Application program risk level determination method and device and storage medium | |
| CN113434826A (en) | Detection method and system for counterfeit mobile application and related products | |
| CN113691552A (en) | Threat intelligence effectiveness evaluation method, device, system and computer storage medium | |
| CN113052604A (en) | Object detection method, device, equipment and storage medium | |
| CN111782908A (en) | WEB violation operation behavior detection method based on data mining cluster analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200609 |