CN104573395B - Big data platform safety assessment quantitative analysis method - Google Patents
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- CN104573395B CN104573395B CN201510044502.9A CN201510044502A CN104573395B CN 104573395 B CN104573395 B CN 104573395B CN 201510044502 A CN201510044502 A CN 201510044502A CN 104573395 B CN104573395 B CN 104573395B
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Abstract
The invention belongs to the technical field of big data platform safety monitoring and assessment and provides a big data platform safety assessment quantitative analysis method. the method includes establishing component tables in a computer database; reading all component information from the component tables; processing each component and factors, affecting the platform safety, of the component circularly; acquiring all domain identifiers and component safety affecting factors; processing the domain identifiers and component safety affecting factors circularly; acquiring all index service entries and domain safety affecting factors corresponding to the safety domain, and computing each index safety degree, domain safety degree, component safety affecting degree and component safety degree of the domain; computing the platform safety affecting degree of the components according to the platform safety affecting factors of each component; completing the processing of all the components; computing the safety degree of the platform according to the platform safety affecting degree of each component. The method has the advantages that the requirements on safety management in big data environment can be met, and the scalability and flexibility of programs are improved.
Description
Technical field
The invention belongs to big data platform safety monitoring and evaluation technical field, more particularly to a kind of big data platform safety is commented
Assessment analysis method.
Background technology
In big data platform safety monitoring and evaluation field, the final goal of monitoring is to provide for safe big data business
Service, the scope of safety monitoring generally covers the physical environment of big data platform, basic network, system hardware and software, platform group
Part, business datum, service application, data acquisition, data storage, Distributed Calculation, user's access, inside and outside interface, safety are examined
The many levels such as meter, and safety index is typically discrete and isolated, it is difficult to find an effective method can intuitively, very
Reflect the whether normal synthesis to big data platform safety of the monitoring index for being distributed in big data platform every aspect on the spot
Power of influence, this combined influence power finally determines the security evaluation tolerance of big data platform.
The security implication degree of each component that big data platform safety assessment tolerance is included depending on the platform, component
Degree of safety depends on the degree of safety of each security domain that the component is included, and the degree of safety of security domain is wrapped depending on security domain again
The degree of safety of each safety index service entry for containing.For same security domain and safety index service entry, to different components
Disturbance degree is different, such as store crossing the border for security service item in data safety, the HDFS components to providing storage service
For disturbance degree it is very big, and to provide computing resource service YARN components for disturbance degree very little.Equally, same component is come
Say, be also different to the security implication of big data platform, for example, store class HDFS component, it is to based on computing in a distributed manner
Big data platform safety disturbance degree with regard to very little, and to provide data storage based on big data platform disturbance degree it is just very big.
Currently without the safety monitoring appraisal procedure of any big data platform, there is great potential safety hazard.
Therefore, big data platform safety monitoring and evaluation technical field is badly in need of a kind of being applied under big data environment to bursting tube
The needs of reason, improve the big data platform safety assessment quantitative analysis method of application's expansibility and motility.
The content of the invention
The invention provides big data platform safety assesses quantitative analysis method, technical scheme is as follows：
Big data platform safety assesses quantitative analysis method, comprises the steps：
Step one, traffic table, including platform assembly table, security domain table, component and domain are set in the data base of computer
Relation table, domain distribution safety index collection service entry table；
Step 2, from the component table of the step one all component information is read, mainly include component id information, component
The security implication factor information of name information and component to platform；
Step 3, process is circulated to the factor of influence of platform safety to each component and component；
Step 4, all domain identifiers and component safety factor of influence are obtained from component and domain relation table according to component ID；
Step 5, process is circulated to every group of domain identifier and component safety factor of influence；
Step 6, according to safe domain identifier from achievement data collection service entry table in obtain all index service entrys and domain peace
Full factor of influence, calculates the degree of safety of each index in domain, the single index degree of safety=1- domains security implication factor；
Step 7, the degree of safety for calculating individual domain, the degree of safety of individual domain is that the domain corresponds to all index degrees of safety most
Little value；
Step 8, the component safety disturbance degree that domain is calculated according to the component safety factor of influence in each domain, individual domain
Component safety disturbance degree=（The degree of safety of 1- individual domains）× component safety factor of influence；
The component safety factor of influence group in all domains and domain is disposed, then into step 9, otherwise repeat step
Five；
Step 9, the degree of safety of computation module, the maximum of the component safety disturbance degree in component safety degree=all domains of 1-；
Step 10, the platform safety disturbance degree that component is calculated according to the platform safety factor of influence of each component, it is single
Component platform security implication degree=（The degree of safety of 1- single components）× platform safety factor of influence；
All component is disposed, then into step 11, otherwise repeat step 3；
Step 11, the degree of safety that platform is calculated according to the platform safety disturbance degree of each component, platform safety degree=1-
The maximum of the platform safety disturbance degree of all component.
Preferably, in the assessment quantitative analysis method of big data platform safety, the platform assembly table in step one is used for fixed
Various pieces in justice composition big data platform, such as data acquisition, data storage HDFS, distribution are calculated, calculated in real time and big number
According to application；Security domain table is used to define each security-related classification, such as data safety, transmission safety, network security, system
Safety, application safety；Component and security domain relation table are used to define the relation between big data component and security domain, i.e. big data
The security measure of component is calculated by the degree of safety of one or more security domains；Domain distribution safety index collection service entry table
For being finely divided monitoring to security domain, such as include data encryption, sensitive information desensitization, the data safety of backup and recovery
Domain.
Beneficial effects of the present invention：
Because the present invention is by order reading platform module information, obtained from component and security domain relation table according to component ID
All domain identifiers and component safety factor of influence, according to domain identifier all index services are obtained from safety index service entry list
And domain security implication factor list, calculate the degree of safety of each index service entry in domain, single index service entry degree of safety=
1- domains degree of safety factor of influence, individual domain degree of safety=MIN（The degree of safety of index service entry 1, the degree of safety ... of index service entry 2.,
Index service entry n degree of safety）, the component safety disturbance degree in domain is calculated further according to the component safety factor of influence in each domain, it is single
The component safety disturbance degree in individual domain=（The degree of safety of 1- individual domains）× component safety factor of influence, then calculates each component
Degree of safety, each component safety degree=1-max（The component safety disturbance degree of domain 1, the component safety disturbance degree ... of domain 2, domain n component peaces
Umbra loudness）, further according to the platform safety factor of influence of each component the platform disturbance degree of component, the platform of unimodule are calculated
Security implication degree=（The degree of safety of 1- single components）× platform safety factor of influence, finally calculates the degree of safety of big data platform,
Platform safety degree=1-max（The security implication degree of component 1, the security implication degree ... of component 2, component n component safety disturbance degrees）, because
This, the present invention is suitable for the needs under big data environment to safety management, and convenient and safe manager is flexible by the method for configuring
Big data platform and platform assembly are realized, platform is set up in the configuration of the corresponding relation between security domain and safety index service entry
The correlation model of analysis on Security Degree of Retaining between each level, greatly increases extensibility and the motility of program.
Description of the drawings
With reference to the accompanying drawings and detailed description describing the present invention in detail：
Fig. 1 is the flow chart that a kind of big data platform safety of the invention assesses quantitative analysis method.
Specific embodiment
In order that measure, creation characteristic, reached purpose and effect that the technology of the present invention is realized are easy to understand, tie below
Conjunction is specifically illustrating, and the present invention is expanded on further.
Fig. 1 is the flow chart that a kind of big data platform safety of the invention assesses quantitative analysis method.
As shown in figure 1, the invention provides a kind of assessment quantitative analysis method of big data platform safety, walks including following
Suddenly：
Step one, traffic table, including platform assembly table, security domain table, component and domain are set in the data base of computer
Relation table, domain distribution safety index collection service entry table；
Platform assembly table be used for define composition big data platform in various pieces, such as data acquisition, data storage HDFS,
Distribution is calculated, calculated in real time and big data application；Security domain table is used to define each security-related classification, such as data peace
Entirely, safety, network security, system safety, application safety are transmitted；Component and security domain relation table be used to defining big data component and
Relation between security domain, the i.e. security measure of big data component is calculated by the degree of safety of one or more security domains；
Distribution safety index collection service entry table in domain is used to be finely divided security domain monitoring, such as includes that data encryption, sensitive information take off
The data safety domain project of quick, backup and recovery etc.；
Step 2, from the traffic table all component information is read, mainly include component id information, component Name information
And component is to security implication factor of platform etc.；
Step 3, process is circulated to the factor of influence of platform safety to each component and component；
Step 4, all domain identifiers and the domain security implication factor are obtained from component and domain relation table according to component ID；
Step 5, process is circulated to every group of domain identifier and component safety factor of influence；
Step 6, the safety for obtaining all index service entrys from achievement data collection service entry table according to safe domain identifier
Factor of influence, calculates the degree of safety of each index in domain, the single index degree of safety=1- domains security implication factor；
Step 7, the degree of safety for calculating individual domain, the degree of safety of individual domain is that the domain corresponds to all index degrees of safety most
Little value；
Step 8, the component safety disturbance degree that domain is calculated according to the component safety factor of influence in each domain, individual domain
Component safety disturbance degree=（The degree of safety of 1- individual domains）× component safety factor of influence；
The component safety factor of influence group in all domains and domain is disposed, then into step 9, otherwise repeat step
Five；
Step 9, the degree of safety of computation module, the maximum of the component safety disturbance degree in component safety degree=all domains of 1-；
Step 10, the platform safety disturbance degree that component is calculated according to the platform safety factor of influence of each component, it is single
Component platform security implication degree=（The degree of safety of 1- single components）× platform safety factor of influence；
All component is disposed, then into step 11, otherwise repeat step 3；
Step 11, the degree of safety that platform is calculated according to the platform safety disturbance degree of each component, platform safety degree=1-
The maximum of the platform safety disturbance degree of all component.
Below using the safety monitoring of telecommunications big data platform as embodiment specifically introducing the enforcement stream of the inventive method
Journey：
The degree of safety of telecommunications big data platform depends on the peace of HDFS, YARN, HBASE these three components that platform is included
Umbra loudness, and the degree of safety of component depends on safe domain system safety, network security, data safety；The safety of system safety
Disturbance degree depends on system password complexity, cipher change strategy, Viral diagnosis, system vulnerability detection；Network security is depended on
Intrusion detection, Network Isolation, fire wall；Data safety depends on data encryption, data desensitization, data backup.
The state of each safety index that hypothesis is collected is as shown in Table 1：
Table one
（1）HDFS：
The state of password complexity, cipher change strategy and Viral diagnosis in system safety is normal, therefore this three
Degree of safety=1-0%=100%, the state of system vulnerability monitoring is abnormal, its degree of safety=1-50%=50%；For system security domain
Degree of safety=min { 100%, 100%, 100%, 50% }=50%, system security domain to the security implication degree of component=（1-50%）×
50%=25%；
The state of intrusion detection and Network Isolation in network security is normal, therefore this two degree of safety=1-0%=
100%, the state of fire wall is abnormal, its degree of safety=1-40%=60%；For network security domain degree of safety=min 100%,
100%, 60% }=60%, network security domain to the security implication degree of component=（1-60%）×50%=20%；
The state of data encryption is abnormal, its degree of safety=1-80%=20%, data desensitization and data backup in data safety
State is normal, both degree of safety=1-0%=100, for data safety domain degree of safety=min { 20%, 100%, 100% }=
20%, network security domain to the security implication degree of component=（1-20%）×80%=64%；
Degree of safety=the 1-max of HDFS components（25%, 20%, 64%）=1-64%=36%；
The platform safety disturbance degree of HDFS components=（1-36%）×80%=51.2%.
（2）YARN：
The state of password complexity, Viral diagnosis and system vulnerability in system safety is normal, therefore this three peace
Whole step=1-0%=100%, the state of cipher change strategy is abnormal, its degree of safety=1-30%=70%；For the peace of system security domain
Whole step=min { 100%, 70%, 100%, 100% }=70%, system security domain to the security implication degree of component=（1-70%）×50%=
35%；
The state of intrusion detection and fire wall in network security is normal, therefore this two degree of safety=1-0%=100%,
The state of Network Isolation is abnormal, its degree of safety=1-50%=50%；For network security domain degree of safety=min 100%, 100%,
50% }=50%, network security domain to the security implication degree of component=（1-50%）×50%=25%；
The state of data encryption is abnormal, its degree of safety=1-80%=20%, data desensitization and data backup in data safety
State is normal, both degree of safety=1-0%=100%, for data safety domain degree of safety=min { 20%, 100%, 100% }=
20%, network security domain to the security implication degree of component=（1-20%）×50%=40%；
Degree of safety=the 1-max of YARN components（35%, 25%, 40%）=1-40%=60%；
The platform safety disturbance degree of YARN components=（1-60%）*60%=24%.
（3）HBASE：
The state of password complexity, cipher change strategy and system vulnerability in system safety is normal, therefore this three
Degree of safety=1-0%=100%, the state of Viral diagnosis is abnormal, its degree of safety=1-50%=50%；For the peace of system security domain
Whole step=min { 100%, 100%, 50%, 100% }=50%, system security domain to the security implication degree of component=（1-50%）×60%=
30%。
The state of intrusion detection and Network Isolation in network security is normal, therefore this two degree of safety=1-0%=
100%, the state of fire wall is abnormal, its degree of safety=1-50%=50%；For network security domain degree of safety=min 100%,
100%, 50% }=50%, network security domain to the security implication degree of component=（1-50%）×50%=25%；
The state of data encryption is abnormal, its degree of safety=1-40%=60%, data desensitization and data backup in data safety
State is normal, both degree of safety=1-0%=100%, for data safety domain degree of safety=min { 60%, 100%, 100% }=
60%, network security domain to the security implication degree of component=（1-60%）*50%=20%；
Degree of safety=the 1-max of HBASE components（30%, 25%, 20%）=1-30%=70%；
The platform safety disturbance degree of HBASE components=（1-70%）×60%=18%；
Therefore, the degree of safety=1-max of big data platform（51.2%,24%,18%）=1-51.2%=48.8%.
Because the present invention is by order reading platform module information, obtained from component and security domain relation table according to component ID
All domain identifiers and component safety factor of influence, according to domain identifier all index services are obtained from safety index service entry list
And domain security implication factor list, calculate the degree of safety of each index service entry in domain, single index service entry degree of safety=
The 1- domains security implication factor, individual domain degree of safety=MIN（The degree of safety of index service entry 1, the degree of safety ... of index service entry 2., refer to
Mark service entry n degree of safety）, the component influences degree in domain is calculated further according to the component safety factor of influence in each domain, individual domain
Component safety disturbance degree=（The degree of safety of 1- individual domains）× component safety factor of influence, then calculates the degree of safety of each component,
Each component safety degree=1-max（The component safety disturbance degree of domain 1, the component safety disturbance degree ... of domain 2, domain n component safeties affect
Degree）, further according to the platform safety factor of influence of each component the platform disturbance degree of component, the platform safety shadow of unimodule are calculated
Loudness=（The degree of safety of 1- single components）× platform safety factor of influence, finally calculates the degree of safety of big data platform, platform peace
Whole step=1-max（The security implication degree of component 1, the security implication degree ... of component 2, component n component safety disturbance degrees）.
The present invention is suitable for the needs under big data environment to safety management, method of the convenient and safe manager by configuring
Big data platform and platform assembly are flexibly realized, the configuration of the corresponding relation between security domain and safety index service entry is set up
The correlation model of analysis on Security Degree of Retaining between each level of platform, greatly increases extensibility and the motility of program.
Ultimate principle, principal character and the advantages of the present invention of the present invention has been shown and described above.The technology of the industry
Personnel it should be appreciated that the present invention is not restricted to the described embodiments, the simply explanation described in above-described embodiment and description this
The principle of invention, of the invention without departing from the spirit and scope of the present invention also to have various changes and modifications, these changes
Change and improvement is both fallen within scope of the claimed invention.The claimed scope of the invention by appending claims and its
Equivalent is defined.
Claims (2)
1. big data platform safety assessment quantitative analysis method, it is characterised in that comprise the steps：
Step one, traffic table, including platform assembly table, security domain table, component and domain relation are set in the data base of computer
Table, domain distribution safety index collection service entry table；
Step 2, from the traffic table of the step one all component information is read, mainly include component id information, component Name
The security implication factor information of information and component to platform；
Step 3, process is circulated to the factor of influence of platform safety to each component and component；
Step 4, all domain identifiers and component safety factor of influence are obtained from component and domain relation table according to component ID；
Step 5, process is circulated to every group of domain identifier and component safety factor of influence；
Step 6, according to safe domain identifier from achievement data collection service entry table in obtain the safe shadow of all index service entrys and domain
The factor is rung, the degree of safety of each index in domain, the single index degree of safety=1- domains security implication factor is calculated；
Step 7, the degree of safety for calculating individual domain, the degree of safety of individual domain is the minima of all index degrees of safety of domain correspondence；
Step 8, the component safety disturbance degree that domain is calculated according to the component safety factor of influence in each domain, the component of individual domain
Security implication degree=(degree of safety of 1- individual domains) × component safety factor of influence；
The component safety factor of influence group in all domains and domain is disposed, then into step 9, otherwise repeat the step
Five；
Step 9, the degree of safety of computation module, the maximum of the component safety disturbance degree in component safety degree=all domains of 1-；
Step 10, the platform safety disturbance degree that component is calculated according to the platform safety factor of influence of each component, single component
Platform safety disturbance degree=(degree of safety of 1- single components) × platform safety factor of influence；
All component is disposed, then into step 11, otherwise repeat the step 3；
Step 11, the degree of safety that platform is calculated according to the platform safety disturbance degree of each component, platform safety degree=1- institutes
There is the maximum of the platform safety disturbance degree of component.
2. big data platform safety according to claim 1 assesses quantitative analysis method, it is characterised in that the platform group
Part table is used to defining the various pieces in composition big data platform, including data acquisition, data storage HDFS, distribution calculate, it is real
When calculate and big data application；Security domain table is used to define each security-related classification, including data safety, transmission peace
Entirely, network security, system safety, application safety；Component and security domain relation table be used to defining big data component and security domain it
Between relation, i.e. the security measure of big data component is calculated by the degree of safety of one or more security domains；Domain distribution peace
Full index collection service entry table is used to be finely divided security domain monitoring, including data encryption, sensitive information desensitization, data backup
With the data safety domain recovered.
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| CN105069734A (en) * | 2015-08-11 | 2015-11-18 | 广东建邦计算机软件有限公司 | Method and system for analyzing security risk by using big data |
| CN105516189B (en) * | 2016-01-08 | 2018-06-15 | 四川大学 | Network security enforcement system and method based on big data platform |
| CN106096227A (en) * | 2016-05-30 | 2016-11-09 | 重庆大学 | Revolving dial hydraulic system safety in operation quantitative evaluating method |
| CN108763916B (en) * | 2018-06-05 | 2022-05-13 | 创新先进技术有限公司 | Service interface security assessment method and device |
| CN109753805A (en) * | 2018-12-28 | 2019-05-14 | 北京东方国信科技股份有限公司 | A kind of method of big data safety coefficient evaluation and test |
| CN111490990B (en) * | 2020-04-10 | 2021-02-23 | 上海星地通讯工程研究所 | Network security analysis method based on big data platform, big data platform server and computer readable storage medium |
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