CN118095422A - Knowledge-graph-based network pollution main body mining method and system - Google Patents
Knowledge-graph-based network pollution main body mining method and system Download PDFInfo
- Publication number
- CN118095422A CN118095422A CN202410471770.8A CN202410471770A CN118095422A CN 118095422 A CN118095422 A CN 118095422A CN 202410471770 A CN202410471770 A CN 202410471770A CN 118095422 A CN118095422 A CN 118095422A
- Authority
- CN
- China
- Prior art keywords
- main body
- mining
- knowledge graph
- network
- information
- 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
- 238000005065 mining Methods 0.000 title claims abstract description 81
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000012098 association analyses Methods 0.000 claims description 18
- 230000008859 change Effects 0.000 claims description 13
- 238000001914 filtration Methods 0.000 claims description 12
- 230000003203 everyday effect Effects 0.000 claims description 9
- 238000004140 cleaning Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 3
- 230000008520 organization Effects 0.000 claims description 3
- 230000010485 coping Effects 0.000 claims 1
- 238000010219 correlation analysis Methods 0.000 claims 1
- 230000005611 electricity Effects 0.000 claims 1
- 230000007704 transition Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a network pollution main body mining method and system based on a knowledge graph, which belong to the field of network security, and comprise the following steps: the method comprises the steps of collecting data, constructing a map, defining rules, mining a main body, updating the data and iterating the rules, so that operators can master historical transition of the network nuisance main body, the discovery capability of the network nuisance main body is improved, and the efficiency of mining the network nuisance main body is improved.
Description
Technical Field
The invention belongs to the technical field of network security, and particularly relates to a network pollution main body mining method and system based on a knowledge graph.
Background
In the context of rapid development and popularity of the internet, network problems are becoming increasingly serious. The network mainly refers to various harmful information and behaviors propagated through the internet, which have adverse effects on individuals, society and environment. However, the existing technology mainly uses manpower to discover the main body, but with the upgrade of technology and the development of the main body, the discovery mode is very practical and time-consuming and labor-consuming, and cannot better discover the main body, so that the discovery efficiency of the network pollution main body is low.
In order to solve the problem, a new method is needed, namely a network pollution main body mining method based on a knowledge graph. Knowledge maps are a new data structure that can organize and represent various information in the form of graphs to better understand and process complex information. By using the knowledge graph, we can more comprehensively and deeply excavate the main body of the network from the multi-dimensional characteristic, thereby improving the discovery efficiency and accuracy of the main body of the network pollution.
Disclosure of Invention
The technical scheme adopted by the invention for achieving the purpose is as follows: the network pollution main body mining method based on the knowledge graph comprises the following steps:
S1, collecting data: the collected websites are researched and judged, and basic data information of the collected websites is obtained;
s2, constructing a map, namely cleaning and filtering the basic data information of the websites obtained in the step S1, filtering out the intranet site addresses and privacy-protected Whois information, and storing creation time and update time contained in the relation among each data into a map database according to a predefined knowledge map structure and by combining all data collected in the step S1;
S3, defining a network pollution main body mining rule according to the knowledge graph architecture and the requirement of mining the network pollution main body, and storing the common rule for reuse;
s4, main body mining, namely, according to the basic data information in the step S1 and the knowledge graph framework in the step S3, constructing a main body mining rule of network pollution which is applicable to various scenes;
And S5, updating data and iterating rules, namely updating a graph database every day, continuously expanding the relation change and time sequence change of the network pollution main body, storing the used mining rules, optimizing according to the newly collected data and new requirements, and then completing mining by utilizing the network pollution main body mining rules.
Preferably, the basic data information in the step of collecting data in S1 includes site IP, registered domain name and SDK information.
Preferably, the step S1 of collecting data firstly carries out research analysis on the collected websites, then analyzes the site IP addresses of the websites to obtain the information of the affiliated institutions, and filters the intranet IP addresses; obtaining Whois information of the registered domain name according to the acquired registered domain name, and resolving the registered mobile phone number, the name of the registrant, the registered mailbox and the domain name registrar; inquiring and acquiring record information of the registered domain name according to the registered domain name, and then analyzing a record number and a record main body; and finally analyzing the request information of the website, analyzing the related SDK of the website and obtaining the company to which the SDK belongs.
Preferably, the step of S2 constructing the map firstly cleans and filters the basic data information of the websites collected in the step S1, filters out the website addresses and privacy-protected Whois information of the internal website, constructs the knowledge map according to a predefined knowledge map architecture, and stores the knowledge map in a map database.
Preferably, the step of constructing the map in S2 is to add corresponding creation time and update time to each point and edge when storing the knowledge graph.
Preferably, the step of defining the S3 rule is to define the mining rule of the network pollution main body according to the knowledge graph structure, the association analysis rule and the mining requirement of the network pollution main body, and store the common rule for reuse; the association analysis rule structure is as follows: the outbound site IP and the registered domain name are resolved through the existing website address, and then other websites which are strongly related to the website are found and the main body of the website is acquired.
Preferably, the knowledge graph structure is as follows: analyzing the obtained website address, firstly obtaining SDK information of the website, and obtaining information of a company to which the SDK belongs through the SDK information; then obtaining the site IP of the website, and obtaining the IP structure through the site IP; finally, obtaining the registered domain name of the website, obtaining the registered mobile phone number, the name of a registrant, the registered mailbox, the domain name registrar and the record number through the registered domain name, and obtaining the record main body through the record number.
Preferably, the step of S4 main body mining fuses the direct rule and the association analysis rule presented by the knowledge graph structure, and all main bodies meeting the conditions are found out in the whole time sequence range.
Preferably, the step of S5 data updating and rule iteration is to research and judge the websites collected every day, collect related data and store the related data in a graph database, continuously expand the relation and time sequence change among the network pollution main bodies, store the used main body mining rules, and optimize according to new data and new requirements.
A network pollution main body mining system based on a knowledge graph comprises the following modules:
The data collecting module is used for researching and judging the website to obtain the name of an organization and a registrant required by the IP of the website, registering the number of a mobile phone, registering a mailbox, a domain name registrar and a company to which the SDK belongs, constructing basic data information and entering the map constructing module;
Constructing a map module: the basic data information collected by the data collecting module is cleaned and filtered, and then the basic data information is stored in a graph database according to a predefined knowledge graph structure and combining the creation time and the update time contained in the relation existing between each basic data, and the basic data information is entered into a rule defining module;
rule definition module: according to the knowledge graph structure and the mining requirements of the network pollution main body, defining mining rules of the network pollution main body, storing common rules for repeated use, and entering a main body mining module;
The main body excavating module: fusing the direct rules presented by the knowledge graph structure and the association analysis rules, finding out all network pollution main bodies meeting the conditions, and entering a data updating and rule iteration module;
Data updating and rule iteration module: the basic data information collected every day is stored in a graph database, the used network pollution main body mining rules are stored, then optimization is carried out according to the newly collected basic data information and new requirements, and then mining is completed by utilizing the network pollution main body mining rules.
Compared with the prior art, the technical scheme of the invention has the following advantages/beneficial effects:
1. The related information of the network pollution main body is expressed in the form of a knowledge graph, so that complex relation changes and time sequence changes are easier to understand and analyze.
2. And the hidden relations among the network pollution main bodies are discovered through the combination of the knowledge patterns and the association analysis rules, so that the discovery capability of the network pollution main bodies is improved.
3. By combining the structural characteristics of the knowledge graph and different network pollution main body mining scenes and carrying out different combinations and matches through the direct rules and the association analysis rules, the efficiency of mining different network pollution main bodies can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a network pollution main body mining method based on a knowledge graph.
Fig. 2 is a schematic diagram of a knowledge graph structure used in the present invention.
FIG. 3 is a schematic diagram of an association analysis rule used in the present invention.
Fig. 4 is a schematic diagram of a network pollution main body mining system structure based on a knowledge graph.
Detailed Description
To make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, based on the embodiments of the invention, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the invention. Accordingly, the detailed description of the embodiments of the invention provided below is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus, once an item is defined in one figure, it may not be further defined and explained in the following figures.
Example 1:
As shown in fig. 1, the method for mining the network pollution main body based on the knowledge graph comprises the following steps:
S1, collecting data: the collected websites are researched and judged, and basic data information of the collected websites is obtained;
s2, constructing a map, namely cleaning and filtering the basic data information of the websites obtained in the step S1, filtering out the intranet site addresses and privacy-protected Whois information, and storing creation time and update time contained in the relation among each data into a map database according to a predefined knowledge map structure and by combining all data collected in the step S1;
S3, defining a network pollution main body mining rule according to the knowledge graph architecture and the requirement of mining the network pollution main body, and storing the common rule for reuse;
s4, main body mining, namely, according to the basic data information in the step S1 and the knowledge graph framework in the step S3, constructing a main body mining rule of network pollution which is applicable to various scenes;
And S5, updating data and iterating rules, namely updating a graph database every day, continuously expanding the relation change and time sequence change of the network pollution main body, storing the used mining rules, optimizing according to the newly collected data and new requirements, and then completing mining by utilizing the network pollution main body mining rules.
When data of a collected website is obtained, the data is constructed into a knowledge graph form, then mining rules in different forms are defined according to requirements when the data is used, mining of network nuisance main bodies is carried out according to the rules, and all relevant main bodies are found out to be regarded as the end of mining.
The basic data information in the step of collecting data S1 includes site IP, registered domain name, and SDK information.
S1, collecting data, namely firstly, analyzing the collected websites by studying and judging, then analyzing the site IP addresses of the websites to obtain the information of the affiliated institutions, and filtering the intranet IP addresses; obtaining Whois information of the registered domain name according to the acquired registered domain name, and resolving the registered mobile phone number, the name of the registrant, the registered mailbox and the domain name registrar; inquiring and acquiring record information of the registered domain name according to the registered domain name, and then analyzing a record number and a record main body; and finally analyzing the request information of the website, analyzing the related SDK of the website and obtaining the company to which the SDK belongs.
And S2, firstly, cleaning and filtering the basic data information of the websites collected in the step S1, filtering out the intranet site address and the privacy-protected Whois information, then constructing a knowledge graph according to a predefined knowledge graph architecture, and storing the knowledge graph in a graph database.
S2, constructing a map, namely adding corresponding creation time and updating time to each point and each edge when the knowledge map is stored.
And S3, defining the mining rules of the network pollution main body according to the knowledge graph structure, the association analysis rules and the mining requirements of the network pollution main body, and storing the common rules for reuse.
According to the illustration in fig. 2, the knowledge graph structure is: resolving the obtained website address, firstly obtaining SDK information of the website, and obtaining information of a company to which the SDK belongs through the SDK information; then the site IP of the website can be obtained, and the IP structure is obtained through the site IP; finally, the registered domain name of the website can be obtained, the registered mobile phone number, the name of a registrant, the registered mailbox, the domain name registrant and the record number can be obtained through the registered domain name, and the record main body can be obtained through the record number.
As shown in fig. 3, the association analysis rule structure is: the outbound site IP and the registered domain name are resolved through the existing website address, and then other websites which are strongly related to the website are found and the main body of the website is acquired.
S4, the main body mining step fuses the direct rules and the association analysis rules presented by the knowledge graph structure, and all main bodies meeting the conditions are found out in the whole time sequence range.
S5, the data updating and rule iteration step is to study and judge the websites collected every day, collect related data and store the related data in a graph database, continuously expand the relation and time sequence change among the network pollution main bodies, store the used main body mining rules, and optimize according to new data and new requirements.
The invention expresses the related information of the network pollution main body in the form of the knowledge graph, thereby facilitating the understanding of the complexity relation change and the time sequence change.
The data of the network pollution main body and the time sequence change information can reflect the relationship and the time sequence change of the network pollution main body.
Storing the nuisance subject related relationships and time sequence changes in combination with multi-source data fusion through knowledge graph forms can provide more comprehensive and accurate information related to the network nuisance subject.
The direct mining rules and the association analysis rules are constructed according to the knowledge graph architecture, and the network pollution main body can be mined through the association analysis rules when the direct rules are not matched with the related network pollution main body.
Through combining knowledge patterns with association analysis rules, hidden relations among network pollution main bodies can be found.
Combining knowledge graph architecture characteristics and different network pollution main body mining scenes, different combinations and matches can be carried out through direct rules and association analysis rules to mine the network pollution main body.
The cleaning and filtering in the step S1 is to process abnormal values and clean position information, filter noise data (nonsensical data) in nodes, and look like wrong mailbox numbers and long titles; filtering a privacy-protected field in the whois information; filtering intranet addresses for IP addresses, such as nonsensical IP of 127.0.0.1; attribute cleaning is performed according to the field information, for example, whether a registered mailbox is a domestic mailbox provider, whether a registered mailbox is domestic, and whether asn of an IP is domestic.
Example 2:
as shown in fig. 4, a network pollution main body mining system based on a knowledge graph comprises the following modules:
The data collecting module is used for researching and judging the website to obtain the name of an organization and a registrant required by the IP of the website, registering the number of a mobile phone, registering a mailbox, a domain name registrar and a company to which the SDK belongs, constructing basic data information and entering the map constructing module;
Constructing a map module: the basic data information collected by the data collecting module is cleaned and filtered, and then the basic data information is stored in a graph database according to a predefined knowledge graph structure and combining the creation time and the update time contained in the relation existing between each basic data, and the basic data information is entered into a rule defining module;
rule definition module: according to the knowledge graph structure and the mining requirements of the network pollution main body, defining mining rules of the network pollution main body, storing common rules for repeated use, and entering a main body mining module;
The main body excavating module: fusing the direct rules presented by the knowledge graph structure and the association analysis rules, finding out all network pollution main bodies meeting the conditions, and entering a data updating and rule iteration module;
Data updating and rule iteration module: the basic data information collected every day is stored in a graph database, the used network pollution main body mining rules are stored, then optimization is carried out according to the newly collected basic data information and new requirements, and then mining is completed by utilizing the network pollution main body mining rules.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that the above-mentioned preferred embodiment should not be construed as limiting the invention, and the scope of the invention should be defined by the appended claims. It will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the spirit and scope of the invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (10)
1. The network pollution main body mining method based on the knowledge graph is characterized by comprising the following steps of:
S1, collecting data: the collected websites are researched and judged, and basic data information of the collected websites is obtained;
s2, constructing a map, namely cleaning and filtering the basic data information of the websites obtained in the step S1, filtering out the intranet site addresses and privacy-protected Whois information, and storing creation time and update time contained in the relation among each data into a map database according to a predefined knowledge map structure and by combining all data collected in the step S1;
S3, defining a network pollution main body mining rule according to the knowledge graph architecture and the requirement of mining the network pollution main body, and storing the common rule for reuse;
S4, main body mining, namely, according to the knowledge graph architecture and the basic data information in the step S1, setting up a main body mining rule of network pollution for coping with various scenes;
And S5, updating data and iterating rules, namely updating a graph database every day, continuously expanding the relation change and time sequence change of the network pollution main body, storing the used mining rules, optimizing according to the newly collected data and new requirements, and then completing mining by utilizing the network pollution main body mining rules.
2. The method for mining a network nuisance main body based on a knowledge graph of claim 1, wherein, the basic data information in the step of S1 collecting data comprises site IP, registered domain name and SDK information.
3. The method for mining a network nuisance main body based on a knowledge graph as claimed in claim 2, wherein the step of S1 is to analyze collected websites by studying and judging, then analyze their site IP addresses to obtain information of affiliated institutions, and filter intranet IP addresses; obtaining Whois information of the registered domain name according to the acquired registered domain name, and resolving the registered mobile phone number, the name of the registrant, the registered mailbox and the domain name registrar; inquiring and acquiring record information of the registered domain name according to the registered domain name, and then analyzing a record number and a record main body; and finally analyzing the request information of the website, analyzing the related SDK of the website and obtaining the company to which the SDK belongs.
4. The method for mining a network nuisance main body based on a knowledge graph according to claim 1, wherein the step of S2 is characterized in that the step of constructing the graph firstly cleans and filters the basic data information of the website collected in the step of S1, filters out the address of the intranet site and the privacy-protected white information, then constructs the knowledge graph according to a predefined knowledge graph architecture, and stores the knowledge graph in a graph database.
5. The method for mining a network nuisance main body based on a knowledge graph of claim 4, wherein, the step of S2 constructing the graph requires adding corresponding creation time and update time between each point and electricity of the knowledge graph and the point when storing the knowledge graph.
6. The method for mining a network nuisance main body based on a knowledge graph according to claim 1, wherein the step of defining S3 rule is to define a mining rule of the network nuisance main body according to a knowledge graph structure, a correlation analysis rule and a mining requirement of the network nuisance main body, and store the rule for reuse; the association analysis rule structure is as follows: the outbound site IP and the registered domain name are resolved through the existing website address, and then other websites which are strongly related to the website are found and the main body of the website is acquired.
7. The network nuisance main body mining method based on knowledge graph of claim 6, wherein the knowledge graph structure is: analyzing the obtained website address, firstly obtaining SDK information of the website, and obtaining information of a company to which the SDK belongs through the SDK information; then obtaining the site IP of the website, and obtaining the IP structure through the site IP; and finally obtaining the registered domain name of the website, obtaining a registered mobile phone number, a registrant name, a registered mailbox, a domain name registrar and a record number through the registered domain name, and obtaining a record main body through the record number.
8. The method for mining a network nuisance main body based on a knowledge graph according to claim 1, wherein the step of mining S4 main body fuses the direct rule and the association analysis rule presented by the knowledge graph structure, and finds out all main bodies meeting the conditions in a full time sequence range.
9. The method for mining network pollution bodies based on knowledge graph according to claim 1, wherein the step of S5 data updating and rule iteration is to research and judge the web sites collected every day and collect related data to store the data in a graph database, continuously expand the relationship and time sequence change between the network pollution bodies, store the used body mining rules, and optimize according to new data and new requirements.
10. A network pollution main body mining system based on a knowledge graph is characterized by comprising the following modules,
The data collecting module is used for researching and judging the website to obtain the name of an organization and a registrant required by the IP of the website, registering the number of a mobile phone, registering a mailbox, a domain name registrar and a company to which the SDK belongs, constructing basic data information and entering the map constructing module;
Constructing a map module: the basic data information collected by the data collecting module is cleaned and filtered, and then the basic data information is stored in a graph database according to a predefined knowledge graph structure and combining the creation time and the update time contained in the relation existing between each basic data, and the basic data information is entered into a rule defining module;
rule definition module: according to the knowledge graph structure and the mining requirements of the network pollution main body, defining mining rules of the network pollution main body, storing common rules for repeated use, and entering a main body mining module;
The main body excavating module: fusing the direct rules presented by the knowledge graph structure and the association analysis rules, finding out all network pollution main bodies meeting the conditions, and entering a data updating and rule iteration module;
Data updating and rule iteration module: the basic data information collected every day is stored in a graph database, the used network pollution main body mining rules are stored, then optimization is carried out according to the newly collected basic data information and new requirements, and then mining is completed by utilizing the network pollution main body mining rules.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410471770.8A CN118095422A (en) | 2024-04-19 | 2024-04-19 | Knowledge-graph-based network pollution main body mining method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410471770.8A CN118095422A (en) | 2024-04-19 | 2024-04-19 | Knowledge-graph-based network pollution main body mining method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118095422A true CN118095422A (en) | 2024-05-28 |
Family
ID=91163946
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410471770.8A Pending CN118095422A (en) | 2024-04-19 | 2024-04-19 | Knowledge-graph-based network pollution main body mining method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118095422A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180089779A1 (en) * | 2016-09-29 | 2018-03-29 | Linkedln Corporation | Skill-based ranking of electronic courses |
| CN110290116A (en) * | 2019-06-04 | 2019-09-27 | 中山大学 | A kind of malice domain name detection method of knowledge based map |
| CN112995361A (en) * | 2021-04-30 | 2021-06-18 | 鹏城实验室 | Domain name knowledge graph construction method, device, equipment and storage medium |
| CN114124576A (en) * | 2022-01-24 | 2022-03-01 | 成都无糖信息技术有限公司 | Knowledge graph-based fraud website association method and system |
| CN114915468A (en) * | 2022-05-10 | 2022-08-16 | 广州数智网络科技有限公司 | Intelligent analysis and detection method for network crime based on knowledge graph |
| US20220367051A1 (en) * | 2021-05-14 | 2022-11-17 | Accenture Global Solutions Limited | Methods and systems for estimating causal effects from knowledge graphs |
| CN116886400A (en) * | 2023-08-03 | 2023-10-13 | 国网湖南省电力有限公司 | Malicious domain name detection method, system and medium |
| CN117614931A (en) * | 2023-03-28 | 2024-02-27 | 中国互联网络信息中心 | Method and device for quickly finding and analyzing black ash produced domain name based on domain name pool |
-
2024
- 2024-04-19 CN CN202410471770.8A patent/CN118095422A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180089779A1 (en) * | 2016-09-29 | 2018-03-29 | Linkedln Corporation | Skill-based ranking of electronic courses |
| CN110290116A (en) * | 2019-06-04 | 2019-09-27 | 中山大学 | A kind of malice domain name detection method of knowledge based map |
| CN112995361A (en) * | 2021-04-30 | 2021-06-18 | 鹏城实验室 | Domain name knowledge graph construction method, device, equipment and storage medium |
| US20220367051A1 (en) * | 2021-05-14 | 2022-11-17 | Accenture Global Solutions Limited | Methods and systems for estimating causal effects from knowledge graphs |
| CN114124576A (en) * | 2022-01-24 | 2022-03-01 | 成都无糖信息技术有限公司 | Knowledge graph-based fraud website association method and system |
| CN114915468A (en) * | 2022-05-10 | 2022-08-16 | 广州数智网络科技有限公司 | Intelligent analysis and detection method for network crime based on knowledge graph |
| CN117614931A (en) * | 2023-03-28 | 2024-02-27 | 中国互联网络信息中心 | Method and device for quickly finding and analyzing black ash produced domain name based on domain name pool |
| CN116886400A (en) * | 2023-08-03 | 2023-10-13 | 国网湖南省电力有限公司 | Malicious domain name detection method, system and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107016072B (en) | Knowledge inference system and method based on social network knowledge graph | |
| CN109657074B (en) | News knowledge graph construction method based on address tree | |
| CN111274282A (en) | Air quality mining system and method and data acquisition monitoring device | |
| CN107358535B (en) | Community discovery method and device | |
| CN111611309A (en) | Interactive visualization method for call ticket data relation network | |
| CN110825950B (en) | Hidden service discovery method based on meta search | |
| Ochian et al. | Big data search for environmental telemetry | |
| CN111221935B (en) | Map road network fusion updating method and system based on big data mining | |
| WO2022076182A1 (en) | Neighborhood‐based entity disambiguation system and method | |
| CN110737779A (en) | Knowledge graph construction method and device, storage medium and electronic equipment | |
| CN118095422A (en) | Knowledge-graph-based network pollution main body mining method and system | |
| CN112005525A (en) | System and method for extracting structure from large, dense and noisy networks | |
| CN112862302A (en) | Petroleum data model modeling method and tool | |
| CN115757655A (en) | Data blood relationship analysis system and method based on metadata management | |
| Fallucchi et al. | Ontology-driven public administration web hosting monitoring system | |
| CN114547322A (en) | Knowledge graph method for identifying water environment pollution | |
| CN109828972B (en) | Data integration method based on directed graph structure | |
| CN112732845A (en) | End-to-end-based large-scale knowledge graph construction and storage method and system | |
| CN113079034A (en) | Internet basic resource and relation model thereof, and construction and application methods of model | |
| Al-Sadoon et al. | From static to dynamic information containers | |
| CN107886107B (en) | Big data fusion method, system and device | |
| CN103034662B (en) | Database sharing apparatus and method, search application integrating system and method | |
| CN104111965A (en) | Differential matrix based OGC (open geospatial consortium) geographic information service description word reduction method | |
| de Meij et al. | Making urban energy use more intelligible using semantic digital twins. | |
| Wang et al. | Fast Cycle Structure Detection for Power Grids Based on Graph Computing |
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 |