CN107995204A - Hadoop framework method for evaluating trust based on Bayes models - Google Patents
Hadoop framework method for evaluating trust based on Bayes models Download PDFInfo
- Publication number
- CN107995204A CN107995204A CN201711310134.3A CN201711310134A CN107995204A CN 107995204 A CN107995204 A CN 107995204A CN 201711310134 A CN201711310134 A CN 201711310134A CN 107995204 A CN107995204 A CN 107995204A
- Authority
- CN
- China
- Prior art keywords
- mrow
- trust
- client
- namenode
- node
- 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
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
- H04L63/08—Network architectures or network communication protocols for network security for authentication of entities
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0654—Management of faults, events, alarms or notifications using network fault recovery
- H04L41/0663—Performing the actions predefined by failover planning, e.g. switching to standby network elements
-
- 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
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
-
- 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
- H04L63/105—Multiple levels of security
-
- 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
- H04L63/20—Network architectures or network communication protocols for network security for managing network security; network security policies in general
- H04L63/205—Network architectures or network communication protocols for network security for managing network security; network security policies in general involving negotiation or determination of the one or more network security mechanisms to be used, e.g. by negotiation between the client and the server or between peers or by selection according to the capabilities of the entities involved
-
- 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
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/142—Network analysis or design using statistical or mathematical methods
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
-
- 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
- H04L63/1458—Denial of Service
Landscapes
- Engineering & Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computer Hardware Design (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computer And Data Communications (AREA)
Abstract
The invention discloses a kind of Hadoop framework method for evaluating trust based on Bayes models, mainly utilize evaluation of the Hadoop framework cloud security platform trust link to the trusting behavior of client, the Service Source obtained again using evaluation result control client, so as to prevent malicious client illicit competition resource, and then reduce the service quality of Hadoop framework cloud security platform.The present invention is proposed based on Bayes average trust models, the model can prevent other legitimate clients of Hadoop framework cloud platform to be subject to the Denial of Service attack of " passive ", improve the fairness that client obtains Service Source, building dynamic behaviour trust link more can accurately analyze service behavior of the client node after Hadoop framework cloud platform is accessed at the same time, can effectively prevent the collaboration of malicious node from cheating.
Description
Technical field
The present invention relates to the calculating assessment of the credible behavior of distributed computing system interior joint, and in particular to one kind is based on Bayes
The Hadoop framework method for evaluating trust of average trust model.
Background technology
Hadoop framework cloud platform is a Distributed Calculation and storage platform increased income.Since Hadoop builds cloud platform
The features such as distinctive scalability, disposition flexibility, more and more users pay much attention it.Hadoop structure cloud platforms are made
For most popular one of cloud platform of increasing income, its further development of the safety effects of the platform.Typical Hadoop framework cloud
The security of platform is the security strategy based on kerberos authentication agreement, although this method efficiently solves access, cloud platform is used
The legitimacy of family identity, but the strategy can not prevent " legal " client from obtaining more interests by irregularities, i.e., it is objective
" passive " Denial of Service attack caused by the end of family.Therefore the trust analysis for client behavior is the emphasis studied herein.
The problem of whether behavior of " legal " user is reliable be not able to verify that for Hadoop framework cloud platform, this paper presents
Based on Bayes average trust models, which can prevent other legitimate clients of Hadoop framework cloud platform to be subject to " passive "
Denial of Service attack, improve client obtain Service Source fairness, while build dynamic behaviour trust link can
Service behavior of the more accurate analysis client node after Hadoop framework cloud platform is accessed, can effectively prevent malice from saving
The collaboration deception of point.
The content of the invention
A kind of the defects of it is an object of the invention to overcome the prior art, there is provided collaboration that can effectively prevent malicious node
The Hadoop framework method for evaluating trust based on Bayes average trust models of deception.
For the technical solution that the present invention will be described in detail, it is necessary first to in the present invention be based on goal of the invention Research foundation into
Row illustrates.
First, Hadoop ecological safeties are described.
The ecological security system characteristic of Hadoop framework cloud platform has a relatively complete design principle, including：
Scalability, fault-tolerance and automatic reparations, storage system programmability, clear simple programming are abstracted and big data is uniformly deposited
Platform is stored up, the security performance for possessing the following aspects：
1) authentication：Authentication techniques are to ensure that client user ensures its identity after accessing Hadoop framework cloud platform
Legitimacy, realize the Single Sign On to client node；
2) authorize：Access level is set up for the different data of susceptibility, the client then accessed needs gives phase
The role of the grade of service is answered, different grades of sensitive data is accessed so as to fulfill different grades of user；
3) data-privacy and encryption：Substantial amounts of customer privacy data and significant data are stored with cloud platform, to data
Secret protection and data encryption are a basic demands of cloud platform safety；
4) system safety：To ensure its normal operation for a complete Hadoop framework cloud platform, in cloud platform
Physical layer, data storage layer, middleware management level and application node layer are required for ensureing its security；
5) audit and event-monitoring：For the cloud platform system of data ecological safety, the security system using event as driving
Need to ensure that each event is safe after performing, and Audit Report is provided to event implementing result；
6) credible and secure system：To prevent legitimate client end node " illegal " behavior from violation of rules and regulations, damaging Hadoop framves wantonly
The service benefits of other legitimate clients are, it is necessary to establish the degrees of comparison of complete set for each client node in structure cloud platform
Scoring；
7) adaptive security：The safe scoring of client node is by Hadoop framework cloud platform automated execution, tool
There is adaptive characteristic, according to client executing task situation, the monitoring node in cloud platform dynamically provides each client in real time
Node score value.
The Hadoop framework method for evaluating trust based on Bayes models of the present invention, mainly utilizes Hadoop framework Yunan County
Full evaluation of the platform trust link to the trusting behavior of client, then the Service Source obtained using evaluation result control client,
So as to prevent malicious client illicit competition resource, and then reduce the service quality of Hadoop framework cloud security platform.
Further scheme is：
The Hadoop framework cloud security platform trust link include DataNode, NameNode/ backup NameNode and
JobTracker, realizes the trusting behavior scoring to client node, specifically includes：
Each DataNode nodes in Hadoop framework cloud platform carry out periodic trust service evaluation to client,
Then oneself is transferred to the assessment result of client NameNode/ backups NameNode.When the normal work of NameNode nodes
When making, backup NameNode is in " dormancy " state, and the trust data that otherwise DataNode nodes return is by backup NameNode
Node receives, then carries out trusting calculating processing.JobTracker is used for the monitoring function of Hadoop framework cloud platform, and the node is defeated
It is to come from NameNode/ backup NameNode to enter information, exports and is used to correct DataNode nodes to each DataNode nodes
To the trust value of each client node behavior；
When client node accesses Hadoop framework cloud platform, monitoring node JobTracker and trust link control are trusted
Node NameNode/ backups NameNode processed carries out trust information interaction, after completing to trust calculating initialization,
NameNode/ backups NameNode will select some DataNode nodes to provide client storage and calculate data service, and
The DataNode nodes that driving is providing service are formed one by one to the row of existing customer end node according to the location order of deployment
For the trust data chain examined, i.e., a upper DataNode node passes to trust value by I/O transmission links next
A DataNode nodes, last end DataNode nodes return to all trust values and give NameNode/ backups NameNode.Work as letter
Appoint link control node NameNode/ backups NameNode when receiving trust evaluation value, trust monitoring node JobTracker and
After trust link control node NameNode/ backups NameNode carries out trust information " exchange " again, monitoring node is trusted
JobTracker determines the trust value of client.
Further scheme is：
Each client node Behavior trustworthiness value is by trusting monitoring node JobTracker, trust link control node
NameNode/ backups NameNode and memory node DataNode are together decided on, and client node is in access Hadoop framves first
Before structure cloud platform, trust monitoring node JobTracker and its legitimacy is verified using kerberos authentication agreement, so
Legal access cloud platform ticket authorisation is given afterwards.Client node has only passed through the identification of cloud platform, trusts monitoring
Node JobTracker just carries out assessment calculating to its Behavior trustworthiness.
Further scheme is：
The trust value of client is calculated in the NameNode nodes by the following method：
After n service interaction occurs for client x and high in the clouds y, during client obtains service, it shows as just
The number of Chang Hangwei is u, and improper behavior number is that the successful posterior probability of v, then client x and high in the clouds y direct interactions is obeyed
Beta is distributed, its density function is：
Wherein, Γ (x) is gamma function, is also Euler's second integral, is that factorial function extends on real number and plural number
Certain function summary.
Direct degree of belief is
Wherein, E [x] takes desired value to Beta distributions.
The degree of belief of each client is together decided on by all high in the clouds of Hadoop framework cloud platform.Assuming that it is based on Hadoop
High in the clouds number is m in framework cloud platform, and the importance rate that client behavior is judged in each high in the clouds under normal conditions is identical
, then the NameNode nodes in cloud platform are to the trust value of client x
The present invention is proposed based on Bayes average trust models, the model can prevent Hadoop framework cloud platform other
Legitimate client is subject to the Denial of Service attack of " passive ", improves the fairness that client obtains Service Source, builds at the same time
Dynamic behaviour trust link more can accurately analyze service rows of the client node after Hadoop framework cloud platform is accessed
Can effectively to prevent the collaboration of malicious node from cheating.
Brief description of the drawings
Fig. 1 is the status information stream of the cloud platform trust link of Hadoop framework of the present invention；
Computational methods are trusted in Fig. 2 dynamic behaviours of the present invention.
Embodiment
The present invention is further illustrated with specific embodiment below in conjunction with the accompanying drawings.
In the present invention, under Hadoop framework cloud environment, when malicious client accesses Hadoop framework cloud platform, it may
Storage resource and computing resource are obtained by " improper competition " mode, seriously affect the service of whole Hadoop framework cloud platform
Resource dispatching strategy.Since the trusting relationship that client accesses high in the clouds under Hadoop framework cloud platform has dynamic and does not know
Property, then Bayes deduces the service trust behavior evaluation that the preferable simulant-client of mechanism logs in high in the clouds, i.e., multiple high in the clouds are to every
A client logs in the reliability evaluation of the service behavior of Hadoop framework cloud platform.
After n service interaction occurs for client x and high in the clouds y, during client obtains service, it shows as just
The number of Chang Hangwei is u, and improper behavior number is that the successful posterior probability of v, then client x and high in the clouds y direct interactions is obeyed
Beta is distributed, its density function is：
Direct degree of belief is
The degree of belief of each client is together decided on by all high in the clouds of Hadoop framework cloud platform.Assuming that it is based on Hadoop
High in the clouds number is m in framework cloud platform, and the importance rate that client behavior is judged in each high in the clouds under normal conditions is identical
, then the NameNode nodes in cloud platform are to the trust value of client x
Of the invention is exactly mainly to be participated in jointly using DataNode, NameNode/ backup NameNode and JobTracker
Evaluation to the trusting behavior of client, then the Service Source obtained using evaluation result control client, so as to prevent malice visitor
Family end illicit competition resource, and then reduce the service quality of Hadoop framework cloud security platform.
Hadoop framework cloud security platform trust link by DataNode, NameNode/ backup NameNode and
JobTracker, which is realized, scores the trusting behavior of client node, each DataNode nodes in Hadoop framework cloud platform
Periodic trust service evaluation is carried out to client, oneself is then transferred to NameNode/ to the assessment result of client
Backup NameNode.When NameNode nodes work normally, backup NameNode is in " dormancy " state, otherwise DataNode
The trust data that node returns is received by backup NameNode nodes, then carries out trusting calculating processing.JobTracker is mainly used
In the monitoring function of Hadoop framework cloud platform, node input information is to come from NameNode/ backup NameNode, export to
Each DataNode nodes are used to correct trust value of the DataNode nodes to each client node behavior, then each client
Trust link status information stream it is as shown in Figure 1.
When client node accesses Hadoop framework cloud platform, monitoring node JobTracker and trust link control are trusted
Node NameNode/ backups NameNode processed carries out trust information interaction, after completing to trust calculating initialization,
NameNode/ backups NameNode will select some DataNode nodes to provide client storage and calculate data service, and
The DataNode nodes that driving is providing service are formed one by one to the row of existing customer end node according to the location order of deployment
For the trust data chain examined, i.e., a upper DataNode node passes to trust value by I/O transmission links next
A DataNode nodes, last end DataNode nodes return to all trust values and give NameNode/ backups NameNode.Work as letter
Appoint link control node NameNode/ backups NameNode when receiving trust evaluation value, trust monitoring node JobTracker and
After trust link control node NameNode/ backups NameNode carries out trust information " exchange " again, monitoring node is trusted
JobTracker determines the trust value of client.
Handled according to Hadoop framework cloud platform trust information streaming and result of calculation, each client node behavior are believed
Value is appointed to determine that their entities include by three participants：Trust monitoring node JobTracker, trust link control node
NameNode/ backups NameNode and memory node DataNode.Client node is in access Hadoop framework cloud platform first
Before, trust monitoring node JobTracker to verify its legitimacy using kerberos authentication agreement, then give
Legal access cloud platform ticket authorisation.Client node has only passed through the identification of cloud platform, trusts monitoring node
JobTracker just carries out its Behavior trustworthiness assessment calculating, and the Behavior trustworthiness value calculation process of client is as shown in Figure 2.
For the client of each access Hadoop framework cloud platform, the present invention participates in evaluation client using all high in the clouds
Trust chain transmit trust value one by one and obtain mode, Behavior trustworthiness assessment situation is finally returned into trust monitoring node
JobTracker, so as to solve " illegal " behavior in access Hadoop framework cloud platform of " legal " client, effectively improves
The service quality of the ecological security system of Hadoop framework cloud platform.
Although reference be made herein to invention has been described for explanatory embodiment of the invention, and above-described embodiment is only this hair
Bright preferable embodiment, embodiments of the present invention are simultaneously not restricted to the described embodiments, it should be appreciated that people in the art
Member can be designed that a lot of other modifications and embodiment, these modifications and embodiment will fall in principle disclosed in the present application
Within scope and spirit.
Claims (4)
- A kind of 1. Hadoop framework method for evaluating trust based on Bayes models, it is characterised in that：Evaluation of the Hadoop framework cloud security platform trust link to the trusting behavior of client mainly is utilized, then using evaluation result The Service Source for controlling client to obtain, so as to prevent malicious client illicit competition resource, and then reduces Hadoop framework cloud The service quality of security platform.
- 2. the Hadoop framework method for evaluating trust based on Bayes models according to claim 1, it is characterised in that：The Hadoop framework cloud security platform trust link include DataNode, NameNode/ backup NameNode and JobTracker, realizes the trusting behavior scoring to client node, specifically includes：Each DataNode nodes in Hadoop framework cloud platform carry out periodic trust service evaluation to client, then Oneself is transferred to the assessment result of client NameNode/ backups NameNode；When NameNode nodes work normally, Backup NameNode is in " dormancy " state, and the trust data that otherwise DataNode nodes return is connect by backup NameNode nodes Receive, then carry out trusting calculating processing；JobTracker is used for the monitoring function of Hadoop framework cloud platform, node input information It is to come from NameNode/ backup NameNode, exports and be used to correct DataNode nodes to each to each DataNode nodes The trust value of client node behavior；When client node accesses Hadoop framework cloud platform, trust monitoring node JobTracker and saved with trust link control Point NameNode/ backups NameNode carries out trust information interaction, and after completing to trust calculating initialization, NameNode/ is standby Part NameNode will select some DataNode nodes to provide client storage and calculate data service, and drive and providing The DataNode nodes of service are examined according to the behavior that the location order of deployment is formed one by one to existing customer end node Trust value is passed to next DataNode by I/O transmission links and saved by trust data chain, i.e., a upper DataNode node Point, last end DataNode nodes return to all trust values and give NameNode/ backups NameNode；Saved when trust link controls When point NameNode/ backups NameNode receives trust evaluation value, trust monitoring node JobTracker and controlled with trust link After node NameNode/ backups NameNode carries out trust information " exchange " again, trust monitoring node JobTracker and determine The trust value of client.
- 3. the Hadoop framework method for evaluating trust based on Bayes models according to claim 2, it is characterised in that：Each client node Behavior trustworthiness value is by trusting monitoring node JobTracker, trust link control node NameNode/ backups NameNode and memory node DataNode are together decided on, and client node is in access Hadoop framves first Before structure cloud platform, trust monitoring node JobTracker and its legitimacy is verified using kerberos authentication agreement, so Legal access cloud platform ticket authorisation is given afterwards；Client node has only passed through the identification of cloud platform, trusts monitoring Node JobTracker just carries out assessment calculating to its Behavior trustworthiness.
- 4. the Hadoop framework method for evaluating trust based on Bayes models according to Claims 2 or 3, it is characterised in that：The trust value of client is calculated in the NameNode nodes by the following method：After n service interaction occurs for client x and high in the clouds y, during client obtains service, it shows as normal row For number be u, improper behavior number is that the successful posterior probability of v, then client x and high in the clouds y direct interactions obeys Beta Distribution, its density function are：<mrow> <mi>B</mi> <mi>e</mi> <mi>t</mi> <mi>a</mi> <mrow> <mo>(</mo> <mi>&theta;</mi> <mo>|</mo> <mi>u</mi> <mo>,</mo> <mi>v</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mi>u</mi> <mo>+</mo> <mi>v</mi> <mo>+</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> <mrow> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mi>u</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>&Gamma;</mi> <mrow> <mo>(</mo> <mi>v</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </mfrac> <msup> <mi>&theta;</mi> <mi>u</mi> </msup> <msup> <mrow> <mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>&theta;</mi> <mo>)</mo> </mrow> <mi>v</mi> </msup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>Wherein, Γ (x) is gamma function；Direct degree of belief is<mrow> <msub> <mi>&theta;</mi> <mrow> <mi>x</mi> <mi>y</mi> </mrow> </msub> <mo>=</mo> <mi>E</mi> <mo>&lsqb;</mo> <mi>B</mi> <mi>e</mi> <mi>t</mi> <mi>a</mi> <mrow> <mo>(</mo> <mi>&theta;</mi> <mo>|</mo> <mi>u</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>v</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>&rsqb;</mo> <mo>=</mo> <mfrac> <mrow> <mi>u</mi> <mo>+</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> <mo>+</mo> <mn>2</mn> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>Wherein, E [x] takes desired value to Beta distributions；The degree of belief of each client is together decided on by all high in the clouds of Hadoop framework cloud platform；Assuming that it is based on Hadoop framework High in the clouds number is m in cloud platform, and the importance rate that client behavior is judged in each high in the clouds under normal conditions be it is identical, then NameNode nodes in cloud platform are to the trust value of client x<mrow> <msub> <mi>&theta;</mi> <mi>x</mi> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>&Sigma;</mi> <mrow> <mi>y</mi> <mo>&Element;</mo> <mo>{</mo> <mn>1</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mi>m</mi> <mo>}</mo> </mrow> </msub> <msub> <mi>&theta;</mi> <mrow> <mi>x</mi> <mi>y</mi> </mrow> </msub> </mrow> <mi>m</mi> </mfrac> <mo>.</mo> </mrow>
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711310134.3A CN107995204A (en) | 2017-12-11 | 2017-12-11 | Hadoop framework method for evaluating trust based on Bayes models |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711310134.3A CN107995204A (en) | 2017-12-11 | 2017-12-11 | Hadoop framework method for evaluating trust based on Bayes models |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107995204A true CN107995204A (en) | 2018-05-04 |
Family
ID=62037429
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711310134.3A Pending CN107995204A (en) | 2017-12-11 | 2017-12-11 | Hadoop framework method for evaluating trust based on Bayes models |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107995204A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111190909A (en) * | 2019-05-17 | 2020-05-22 | 延安大学 | Data credible processing method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101257386A (en) * | 2008-03-11 | 2008-09-03 | 南京邮电大学 | Dynamic accesses control method based on trust model |
| CN103347028A (en) * | 2013-07-15 | 2013-10-09 | 福州大学 | Peer-to-peer network trust degree measurement model based on Bayes under cloud architecture |
| US20160299919A1 (en) * | 2013-06-03 | 2016-10-13 | Zettaset, Inc. | Management of Intermediate Data Spills during the Shuffle Phase of a Map-Reduce Job |
-
2017
- 2017-12-11 CN CN201711310134.3A patent/CN107995204A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101257386A (en) * | 2008-03-11 | 2008-09-03 | 南京邮电大学 | Dynamic accesses control method based on trust model |
| US20160299919A1 (en) * | 2013-06-03 | 2016-10-13 | Zettaset, Inc. | Management of Intermediate Data Spills during the Shuffle Phase of a Map-Reduce Job |
| CN103347028A (en) * | 2013-07-15 | 2013-10-09 | 福州大学 | Peer-to-peer network trust degree measurement model based on Bayes under cloud architecture |
Non-Patent Citations (2)
| Title |
|---|
| 江峰，杨章伟: ""Hadoop云平台中基于行为的信任评估研究"", 《萍乡高等专科学校学报》 * |
| 谢苑: ""基于贝叶斯网络的动态信任模型研究"", 《中国优秀硕士学位论文全文数据库(信息科技辑)》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111190909A (en) * | 2019-05-17 | 2020-05-22 | 延安大学 | Data credible processing method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6918245B2 (en) | Identity verification method and equipment | |
| CN112348204B (en) | Safe sharing method for marine Internet of things data under edge computing framework based on federal learning and block chain technology | |
| CN103944722B (en) | Identification method for user trusted behaviors under internet environment | |
| US11899808B2 (en) | Machine learning for identity access management | |
| WO2021036014A1 (en) | Federated learning credit management method, apparatus and device, and readable storage medium | |
| CN107103473A (en) | A kind of intelligent contract implementation method based on block chain | |
| CN112434280A (en) | Block chain-based federal learning defense method | |
| CN111092910B (en) | Database security access method, device, equipment, system and readable storage medium | |
| CN104009959B (en) | A kind of cloud access control method that can verify that based on XACML | |
| CN105045597B (en) | A kind of JAVA card object reference method and device | |
| CN113779617B (en) | State channel-based federal learning task credible supervision and scheduling method and device | |
| CN115499129A (en) | Multimode trust cross-chain consensus method, system, medium, equipment and terminal | |
| CN109246178B (en) | Online title distribution method and device, server and storage medium | |
| CN107347064A (en) | Cloud computing platform Tendency Prediction method based on neural network algorithm | |
| CN107995204A (en) | Hadoop framework method for evaluating trust based on Bayes models | |
| CN110458570B (en) | Risk transaction management and configuration method and system thereof | |
| CN116170199A (en) | Equipment access verification system based on gateway of Internet of things | |
| CN115168830A (en) | Login method and login device for detecting user login environment | |
| CN114611112A (en) | Excitation system and excitation method for cooperative population intelligence mitigation of undisclosed vulnerabilities | |
| CN112437407A (en) | Method and system for calculating initialized safety state of power internet of things wireless terminal equipment based on mixed information entropy | |
| CN109951448A (en) | Domain name authentic authentication method and device based on block chain | |
| CN117834304B (en) | Autonomous controllable master control network safety protection system | |
| Cui et al. | Trust Evaluation of Topological Nodes in Intelligent Connected Vehicles Communication Network under Zero-Trust Environment | |
| CN111988184B (en) | Broadcast storm detection and processing method based on situation awareness | |
| CN102739690A (en) | Safety data exchange process monitoring method and system |
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: 20180504 |