CN104573395A - Big data platform safety assessment quantitative analysis method - Google Patents
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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 large data platform safety monitoring assessment technology field, particularly the quantitative analysis method of one large data platform safety assessment.
Background technology
In large data platform safety monitoring evaluation areas, the final goal of monitoring is to provide safe large data service service, the scope of safety monitoring covers the physical environment of large data platform usually, basic network, system hardware and software, platform assembly, business datum, service application, data acquisition, data store, Distributed Calculation, user accesses, inside and outside interface, the many levels such as security audit, and safety index is normally discrete and isolated, being difficult to searching effective way can be directly perceived, reflect the whether normal combined influence power to large data platform safety of the monitoring index being distributed in large data platform every aspect truly, this combined influence power finally determines the safety assessment tolerance of large data platform.
Large data platform safety assessment tolerance depends on the security implication degree of each assembly that this platform comprises, the degree of safety of assembly depends on the degree of safety of each security domain that this assembly comprises, and the degree of safety of security domain depends on again the degree of safety of each safety index service entry that security domain comprises.Concerning same security domain and safety index service entry, different to the disturbance degree of different assembly, crossing the border of such as, storage security service entry in data security, very large concerning disturbance degree the HDFS assembly providing stores service, and very little concerning disturbance degree the service YARN assembly providing computational resource.Equally, concerning same assembly, be also different to the security implication of large data platform, such as storage class HDFS assembly, it is that main large data platform security implication degree is just very little to computing in a distributed manner, and is that main large data platform disturbance degree is just very large to what provide data to store.
At present without any a kind of safety monitoring appraisal procedure of large data platform, there is great potential safety hazard.
Therefore, a kind of needs be applicable to safety management under large data environment are badly in need of in large data platform safety monitoring assessment technology field, improve the large data platform safety assessment quantitative analysis method of application's expansibility and dirigibility.
Summary of the invention
The invention provides the quantitative analysis method of large data platform safety assessment, technical scheme is as follows:
The quantitative analysis method of large data platform safety assessment, comprises the steps:
Step one, traffic table is set in the database of computing machine, comprises platform assembly table, security domain table, assembly and territory relation table, territory distributes safety index and gather service entry table;
Step 2, from the component table of described step one, read all component information, mainly comprise assembly id information, component Name information and assembly are to the security implication factor information of platform;
Step 3, to the factor of influence of platform safety, circular treatment is carried out to each assembly and assembly;
Step 4, from assembly and territory relation table, obtain all domain identifiers and component safety factor of influence according to assembly ID;
Step 5, to often organizing domain identifier and component safety factor of influence carries out circular treatment;
Step 6, to gather service entry table from achievement data according to security domain mark and obtain all index service entrys and the territory security implication factor, calculate the degree of safety of each index in territory, single index degree of safety=1-territory security implication factor;
The degree of safety of step 7, calculating individual domain, the degree of safety of individual domain is the minimum value of the corresponding all index degrees of safety in this territory;
Step 8, calculate the component safety disturbance degree in territory according to the component safety factor of influence in each territory, component safety disturbance degree=(degree of safety of 1-the individual domain) × component safety factor of influence of individual domain;
The component safety factor of influence group in all territories and territory is disposed, then enter step 9, otherwise repeated execution of steps five;
The degree of safety of step 9, computation module, the maximal value of the component safety disturbance degree in all territories of component safety degree=1-;
Step 10, calculate the platform safety disturbance degree of assembly according to the platform safety factor of influence of each assembly, single component platform safety disturbance degree=(degree of safety of 1-single component) × platform safety factor of influence;
All component is disposed, then enter step 11, otherwise repeated execution of steps three;
Step 11, calculate the degree of safety of platform according to the platform safety disturbance degree of each assembly, the maximal value of the platform safety disturbance degree of platform safety degree=1-all component.
Preferably, in the quantitative analysis method of large data platform safety assessment, the platform assembly table in step one is for defining the various piece in the large data platform of composition, and as data acquisition, data store HDFS, distribution calculates, calculate in real time and large market demand; Security domain table for defining each security-related classification, as data security, transmission security, network security, security of system, application safety; Assembly and security domain relation table are for defining the relation between large data package and security domain, and namely the security measure of large data package is calculated by the degree of safety of one or more security domain; Territory is distributed safety index and is gathered service entry table for carrying out segmentation monitoring to security domain, as comprised the data security territory of data encryption, sensitive information desensitization, backup and recovery.
Beneficial effect of the present invention:
Because the present invention is by order reading platform module information, all domain identifiers and component safety factor of influence is obtained from assembly and security domain relation table according to assembly ID, from the list of safety index service entry, all index service entrys and territory security implication is obtained because of sublist according to domain identifier, calculate the degree of safety of each index service entry in territory, single index service entry degree of safety=1-territory degree of safety factor of influence, individual domain degree of safety=MIN(index service entry 1 degree of safety, index service entry 2 degree of safety, ., index service entry n degree of safety), the component safety disturbance degree in territory is calculated again according to the component safety factor of influence in each territory, component safety disturbance degree=(degree of safety of 1-the individual domain) × component safety factor of influence of individual domain, then the degree of safety of each assembly is calculated, each component safety degree=1-max(territory 1 component safety disturbance degree, territory 2 component safety disturbance degree, territory n component safety disturbance degree), the platform disturbance degree of assembly is calculated again according to the platform safety factor of influence of each assembly, platform safety disturbance degree=(degree of safety of 1-the single component) × platform safety factor of influence of unimodule, finally calculate the degree of safety of large data platform, platform safety degree=1-max(assembly 1 security implication degree, assembly 2 security implication degree, assembly n component safety disturbance degree), therefore, the present invention is applicable to the needs to safety management under large data environment, convenient and safe keeper realizes large data platform and platform assembly flexibly by the method for configuration, the configuration of the corresponding relation between security domain and safety index service entry, set up the correlation model of analysis on Security Degree of Retaining between each level of platform, greatly increase extensibility and the dirigibility of program.
Accompanying drawing explanation
The present invention is described in detail below in conjunction with the drawings and specific embodiments:
Fig. 1 is the process flow diagram of a kind of large data platform safety assessment of the present invention quantitative analysis method.
Embodiment
The measure realized to make the technology of the present invention, creation characteristic, reaching object and effect is easy to understand, below in conjunction with concrete diagram, setting forth the present invention further.
Fig. 1 is the process flow diagram of a kind of large data platform safety assessment of the present invention quantitative analysis method.
As shown in Figure 1, the invention provides a kind of large data platform safety assessment quantitative analysis method, comprise the steps:
Step one, traffic table is set in the database of computing machine, comprises platform assembly table, security domain table, assembly and territory relation table, territory distributes safety index and gather service entry table;
Platform assembly table is for defining the various piece in the large data platform of composition, and as data acquisition, data store HDFS, distribution calculates, calculate in real time and large market demand; Security domain table for defining each security-related classification, as data security, transmission security, network security, security of system, application safety; Assembly and security domain relation table are for defining the relation between large data package and security domain, and namely the security measure of large data package is calculated by the degree of safety of one or more security domain; Territory is distributed safety index and is gathered service entry table for carrying out segmentation monitoring to security domain, as comprised the data security territory project of data encryption, sensitive information desensitization, backup and recovery etc.;
Step 2, from described traffic table, read all component information, mainly comprise assembly id information, component Name information and assembly are to the security implication factor etc. of platform;
Step 3, to the factor of influence of platform safety, circular treatment is carried out to each assembly and assembly;
Step 4, from assembly and territory relation table, obtain all domain identifiers and the territory security implication factor according to assembly ID;
Step 5, to often organizing domain identifier and component safety factor of influence carries out circular treatment;
Step 6, gather from achievement data the security implication factor obtaining all index service entrys service entry table according to security domain mark, calculate the degree of safety of each index in territory, single index degree of safety=1-territory security implication factor;
The degree of safety of step 7, calculating individual domain, the degree of safety of individual domain is the minimum value of the corresponding all index degrees of safety in this territory;
Step 8, calculate the component safety disturbance degree in territory according to the component safety factor of influence in each territory, component safety disturbance degree=(degree of safety of 1-the individual domain) × component safety factor of influence of individual domain;
The component safety factor of influence group in all territories and territory is disposed, then enter step 9, otherwise repeated execution of steps five;
The degree of safety of step 9, computation module, the maximal value of the component safety disturbance degree in all territories of component safety degree=1-;
Step 10, calculate the platform safety disturbance degree of assembly according to the platform safety factor of influence of each assembly, single component platform safety disturbance degree=(degree of safety of 1-single component) × platform safety factor of influence;
All component is disposed, then enter step 11, otherwise repeated execution of steps three;
Step 11, calculate the degree of safety of platform according to the platform safety disturbance degree of each assembly, the maximal value of the platform safety disturbance degree of platform safety degree=1-all component.
The implementing procedure of the inventive method is specifically introduced below using the safety monitoring of the large data platform of telecommunications as embodiment:
The degree of safety of the large data platform of telecommunications depends on the security implication degree of these three assemblies of HDFS, YARN, HBASE that platform comprises, and the degree of safety of assembly depends on security domain security of system, network security, data security; The security implication degree of security of system depends on that system password complexity, cipher change strategy, Viral diagnosis, system vulnerability detect; Network security depends on intrusion detection, Network Isolation, fire wall; Data security depends on data encryption, data desensitization, data backup.
Suppose the state of each safety index collected as shown in Table 1:
Table one
（1） HDFS：
The state of the password complexity in security of system, cipher change strategy and Viral diagnosis is normal, therefore this degree of safety=1-0%=100% of three, and the state of system vulnerability monitoring is abnormal, its degree of safety=1-50%=50%; For the degree of safety=min{100% in security of system territory, 100%, 100%, 50%}=50%, security of system territory is to security implication degree=(the 1-50%) × 50%=25% of assembly;
Intrusion detection in network security and the state of Network Isolation are normal, therefore this degree of safety=1-0%=100% of two, and the state of fire wall is abnormal, its degree of safety=1-40%=60%; For the degree of safety=min{100% in network security territory, 100%, 60%}=60%, network security territory is to security implication degree=(the 1-60%) × 50%=20% of assembly;
The state of data encryption in data security is abnormal, its degree of safety=1-80%=20%, data desensitization and data backup status are normal, both degree of safety=1-0%=100, for the degree of safety=min{20% in data security territory, 100%, 100%}=20%, network security territory is to security implication degree=(the 1-20%) × 80%=64% of assembly;
Degree of safety=the 1-max(25% of HDFS assembly, 20%, 64%)=1-64%=36%;
Platform safety disturbance degree=(the 1-36%) × 80%=51.2% of HDFS assembly.
（2） YARN：
The state of password complexity, Viral diagnosis and system vulnerability in security of system is normal, therefore this degree of safety=1-0%=100% of three, and the state of cipher change strategy is abnormal, its degree of safety=1-30%=70%; For the degree of safety=min{100% in security of system territory, 70%, 100%, 100%}=70%, security of system territory is to security implication degree=(the 1-70%) × 50%=35% of assembly;
Intrusion detection in network security and the state of fire wall are normal, therefore this degree of safety=1-0%=100% of two, the state of Network Isolation is abnormal, its degree of safety=1-50%=50%; For the degree of safety=min{100% in network security territory, 100%, 50%}=50%, network security territory is to security implication degree=(the 1-50%) × 50%=25% of assembly;
The state of data encryption in data security is abnormal, its degree of safety=1-80%=20%, data desensitization and data backup status are normal, both degree of safety=1-0%=100%, for the degree of safety=min{20% in data security territory, 100%, 100%}=20%, network security territory is to security implication degree=(the 1-20%) × 50%=40% of assembly;
Degree of safety=the 1-max(35% of YARN assembly, 25%, 40%)=1-40%=60%;
Platform safety disturbance degree=(1-60%) * 60%=24% of YARN assembly.
（3） HBASE：
The state of the password complexity in security of system, cipher change strategy and system vulnerability is normal, therefore this degree of safety=1-0%=100% of three, and the state of Viral diagnosis is abnormal, its degree of safety=1-50%=50%; For the degree of safety=min{100% in security of system territory, 100%, 50%, 100%}=50%, security of system territory is to security implication degree=(the 1-50%) × 60%=30% of assembly.
Intrusion detection in network security and the state of Network Isolation are normal, therefore this degree of safety=1-0%=100% of two, and the state of fire wall is abnormal, its degree of safety=1-50%=50%; For the degree of safety=min{100% in network security territory, 100%, 50%}=50%, network security territory is to security implication degree=(the 1-50%) × 50%=25% of assembly;
The state of data encryption in data security is abnormal, its degree of safety=1-40%=60%, data desensitization and data backup status are normal, both degree of safety=1-0%=100%, for the degree of safety=min{60% in data security territory, 100%, 100%}=60%, network security territory is to security implication degree=(1-60%) * 50%=20% of assembly;
Degree of safety=the 1-max(30% of HBASE assembly, 25%, 20%)=1-30%=70%;
Platform safety disturbance degree=(the 1-70%) × 60%=18% of HBASE assembly;
Therefore, the degree of safety=1-max(51.2% of large data platform, 24%, 18%)=1-51.2%=48.8%.
Because the present invention is by order reading platform module information, all domain identifiers and component safety factor of influence is obtained from assembly and security domain relation table according to assembly ID, from the list of safety index service entry, all index service entrys and territory security implication is obtained because of sublist according to domain identifier, calculate the degree of safety of each index service entry in territory, single index service entry degree of safety=1-territory security implication factor, individual domain degree of safety=MIN(index service entry 1 degree of safety, index service entry 2 degree of safety, ., index service entry n degree of safety), the component influences degree in territory is calculated again according to the component safety factor of influence in each territory, component safety disturbance degree=(degree of safety of 1-the individual domain) × component safety factor of influence of individual domain, then the degree of safety of each assembly is calculated, each component safety degree=1-max(territory 1 component safety disturbance degree, territory 2 component safety disturbance degree, territory n component safety disturbance degree), the platform disturbance degree of assembly is calculated again according to the platform safety factor of influence of each assembly, platform safety disturbance degree=(degree of safety of 1-the single component) × platform safety factor of influence of unimodule, finally calculate the degree of safety of large data platform, platform safety degree=1-max(assembly 1 security implication degree, assembly 2 security implication degree, assembly n component safety disturbance degree).
The present invention is applicable to the needs to safety management under large data environment, convenient and safe keeper realizes large data platform and platform assembly flexibly by the method for configuration, the configuration of the corresponding relation between security domain and safety index service entry, set up the correlation model of analysis on Security Degree of Retaining between each level of platform, greatly increase extensibility and the dirigibility of program.
More than show and describe ultimate principle of the present invention, principal character and advantage of the present invention.The technician of the industry should understand; the present invention is not restricted to the described embodiments; what describe in above-described embodiment and instructions just illustrates principle of the present invention; the present invention also has various changes and modifications without departing from the spirit and scope of the present invention, and these changes and improvements all fall in the claimed scope of the invention.Application claims protection domain is defined by appending claims and equivalent thereof.
Claims (2)
1. large data platform safety assessment quantitative analysis method, is characterized in that, comprise the steps:
Step one, traffic table is set in the database of computing machine, comprises platform assembly table, security domain table, assembly and territory relation table, territory distributes safety index and gather service entry table;
Step 2, from the traffic table of described step one, read all component information, mainly comprise assembly id information, component Name information and assembly are to the security implication factor information of platform;
Step 3, to the factor of influence of platform safety, circular treatment is carried out to each assembly and assembly;
Step 4, from assembly and territory relation table, obtain all domain identifiers and component safety factor of influence according to assembly ID;
Step 5, to often organizing domain identifier and component safety factor of influence carries out circular treatment;
Step 6, to gather service entry table from achievement data according to security domain mark and obtain all index service entrys and the territory security implication factor, calculate the degree of safety of each index in territory, single index degree of safety=1-territory security implication factor;
The degree of safety of step 7, calculating individual domain, the degree of safety of individual domain is the minimum value of the corresponding all index degrees of safety in this territory;
Step 8, calculate the component safety disturbance degree in territory according to the component safety factor of influence in each territory, component safety disturbance degree=(degree of safety of 1-the individual domain) × component safety factor of influence of individual domain;
The component safety factor of influence group in all territories and territory is disposed, then enter step 9, otherwise repeat described step 5;
The degree of safety of step 9, computation module, the maximal value of the component safety disturbance degree in all territories of component safety degree=1-;
Step 10, calculate the platform safety disturbance degree of assembly according to the platform safety factor of influence of each assembly, single component platform safety disturbance degree=(degree of safety of 1-single component) × platform safety factor of influence;
All component is disposed, then enter step 11, otherwise repeats described step 3;
Step 11, calculate the degree of safety of platform according to the platform safety disturbance degree of each assembly, the maximal value of the platform safety disturbance degree of platform safety degree=1-all component.
2. one according to claim 1 large data platform safety assessment quantitative analysis method, it is characterized in that, described platform assembly table, for defining the various piece in the large data platform of composition, comprises data acquisition, data stores HDFS, distribution calculates, calculates in real time and large market demand; Security domain table, for defining each security-related classification, comprises data security, transmission security, network security, security of system, application safety; Assembly and security domain relation table are for defining the relation between large data package and security domain, and namely the security measure of large data package is calculated by the degree of safety of one or more security domain; Territory is distributed safety index and is gathered service entry table for carrying out segmentation monitoring to security domain, comprises the data security territory etc. of data encryption, sensitive information desensitization, backup and recovery.
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