CN115086089B - Method and system for network security assessment prediction - Google Patents
Method and system for network security assessment prediction Download PDFInfo
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- CN115086089B CN115086089B CN202211009427.9A CN202211009427A CN115086089B CN 115086089 B CN115086089 B CN 115086089B CN 202211009427 A CN202211009427 A CN 202211009427A CN 115086089 B CN115086089 B CN 115086089B
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Abstract
The invention provides a method and a system for network security assessment and prediction, which relate to the technical field of network security, and the method comprises the following steps: determining a target network system; acquiring safety index parameter information of a plurality of safety indexes of a target network system to obtain a safety index information set; carrying out weight distribution on the plurality of safety indexes to obtain a weight distribution result; constructing a network security assessment prediction model for assessing the network security of the target network system; inputting the safety index information set and the weight distribution result into a network safety evaluation prediction model to obtain a physical network safety evaluation result, an information network safety evaluation result and a management network safety evaluation result; and weighting by adopting the overall weight distribution result to obtain a network security evaluation prediction result. The invention solves the technical problem that the network security assessment is unilateral and inaccurate in the prior art, and achieves the technical effect of comprehensively and accurately performing the network security assessment.
Description
Technical Field
The invention relates to the technical field of network security, in particular to a method and a system for network security assessment and prediction.
Background
With the development of internet technology, networks have promoted communication efficiency and production efficiency for various industries, and accordingly, network security relates to various aspects such as security, personal privacy, financial security, and the like, and a network system can normally and safely operate and is also valued by various industries.
The network security is affected by a plurality of factors, for example, the physical factors such as the machine room environment, the information factors such as the network firewall, and the management factors such as the machine room operation and maintenance personnel. The existing network security evaluation method is generally carried out based on network communication information security, mainly aims at network illegal attacks and neglects other factors influencing the normal and safe operation of a network system.
In the prior art, network security evaluation generally only focuses on network communication information security, theoretical evaluation is performed only based on the communication security, physical factors and management factors influencing safe and stable operation of a machine room are ignored, and the technical problem of one-sided and inaccurate network security evaluation exists.
Disclosure of Invention
The application provides a method and a system for network security assessment prediction, which are used for solving the technical problems that in the prior art, network security assessment generally only focuses on network communication information security, theoretical assessment is carried out only on the basis of communication security, physical factors and management factors influencing safe and stable operation of a machine room are ignored, and network security assessment is unilateral and inaccurate.
In view of the foregoing, the present application provides a method and system for network security assessment prediction.
In a first aspect of the present application, a method for network security assessment prediction is provided, the method comprising: determining a target network system, wherein the target network system is a network system to be subjected to network security evaluation; acquiring safety index parameter information of a plurality of safety indexes of the target network system to obtain a safety index information set; dividing the safety index information set to obtain a physical safety index information set, an information safety index information set and a management safety index information set; performing weight distribution on the plurality of safety indexes to obtain a weight distribution result, wherein the weight distribution result comprises an overall weight distribution result, a physical safety index weight distribution result, an information safety index weight distribution result and a management safety index weight distribution result; constructing a network security assessment prediction model for assessing the network security of the target network system based on the weight assignment result, wherein the network security assessment prediction model comprises a physical security assessment prediction submodel, an information security assessment prediction submodel and a management security assessment prediction submodel; inputting the physical security index information set, the information security index information set, the management security index information set, the physical security index weight distribution result, the information security index weight distribution result and the management security index weight distribution result into the network security assessment prediction model to obtain a physical network security assessment result, an information network security assessment result and a management network security assessment result; and weighting the physical network security evaluation result, the information network security evaluation result and the management network security evaluation result by adopting the overall weight distribution result to obtain a network security evaluation prediction result.
In a second aspect of the present application, a system for network security assessment prediction is provided, the system comprising: the network system determination module is used for determining a target network system, wherein the target network system is a network system to be subjected to network security evaluation; the safety index information acquisition module is used for acquiring safety index parameter information of a plurality of safety indexes of the target network system to obtain a safety index information set; the safety index information dividing module is used for dividing the safety index information set to obtain a physical safety index information set, an information safety index information set and a management safety index information set; the weight distribution module is used for carrying out weight distribution on the plurality of safety indexes to obtain a weight distribution result, wherein the weight distribution result comprises a total weight distribution result, a physical safety index weight distribution result, an information safety index weight distribution result and a management safety index weight distribution result; the evaluation model building module is used for building a network security evaluation prediction model for evaluating the network security of the target network system based on the weight distribution result, wherein the network security evaluation prediction model comprises a physical security evaluation prediction submodel, an information security evaluation prediction submodel and a management security evaluation prediction submodel; the safety assessment prediction module is used for inputting the physical safety index information set, the information safety index information set, the management safety index information set, the physical safety index weight distribution result, the information safety index weight distribution result and the management safety index weight distribution result into the network safety assessment prediction model to obtain a physical network safety assessment result, an information network safety assessment result and a management network safety assessment result; and the weighting calculation module is used for weighting the physical network security evaluation result, the information network security evaluation result and the management network security evaluation result by adopting the overall weight distribution result to obtain a network security evaluation prediction result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the method, a target network system needing network security assessment prediction is acquired, security index parameter information of a plurality of current security indexes is acquired, a security index information set is acquired, a physical security index information set, an information security index information set and a management security index information set are obtained through division according to rules, the ability of the plurality of security indexes influencing network security is subjected to weight distribution, a weight distribution result is acquired, a network security assessment prediction model for assessing network system security is further constructed, the physical security index information set, the information security index information set, the management security index information set and the weight distribution result are input into the network security assessment prediction model, network security assessment is respectively performed, a physical network security assessment result, an information network security assessment result and a management network security assessment result are respectively acquired, and a final network security assessment prediction result is further obtained through weighting. According to the method, the parameter information of a plurality of safety indexes of the network system is acquired through collection, the network safety evaluation prediction is carried out respectively according to the physical factors, the information factors and the management factors, various factors can be comprehensively considered to carry out the network safety evaluation prediction, and finally, the evaluation prediction result is comprehensively obtained without being limited to the analysis of communication information safety.
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FIG. 1 is a flow chart illustrating a method for network security assessment prediction provided herein;
fig. 2 is a schematic flow chart illustrating a method for predicting network security assessment according to the present application, wherein the method is used for performing weight distribution to obtain a weight distribution result;
FIG. 3 is a schematic flow chart illustrating a method for predicting network security assessment according to the present application for constructing a model for predicting network security assessment;
fig. 4 is a schematic diagram of a system for predicting network security assessment according to the present application.
Description of the reference numerals: the system comprises a network system determination module 11, a safety index information acquisition module 12, a safety index information division module 13, a weight distribution module 14, an evaluation model construction module 15, a safety evaluation prediction module 16 and a weighting calculation module 17.
Detailed Description
The application provides a method and a system for network security assessment prediction, which are used for solving the technical problems that in the prior art, network security assessment generally only focuses on network communication information security, theoretical assessment is carried out only on the basis of communication security, physical factors and management factors influencing safe and stable operation of a machine room are ignored, and network security assessment is unilateral and inaccurate.
Example one
As shown in fig. 1, the present application provides a method for network security assessment prediction, the method comprising:
s100: determining a target network system, wherein the target network system is a network system to be subjected to network security evaluation;
in the embodiment of the present application, the target network system is any network system that needs to perform network security assessment and prediction by using the method provided in the embodiment of the present application, for example, the target network system may be a CRM system leased or purchased by an enterprise, or any other computer network system.
S200: acquiring safety index parameter information of a plurality of safety indexes of the target network system to obtain a safety index information set;
in the embodiment of the application, the plurality of safety indexes are indexes which may affect the network safety and the data safety of the target network system, and specifically include physical safety indexes which affect the machine room environment and the like in which the target network system server operates, information safety indexes which affect the communication information safety of the target network system, and personnel management safety indexes which may affect the data and the authority safety of the target network system.
Illustratively, the physical safety indexes include machine room temperature, humidity, dust amount, electromagnetic field interference condition, power supply stability, equipment stability and the like. The information security index comprises data backup condition, data recovery scheme, communication data encryption, system access authority, system software security, system firewall condition and the like. The management safety indexes comprise training conditions of system operation and maintenance personnel, information safety management systems, operation and maintenance log records, local data encryption conditions and the like.
The method comprises the steps of collecting and obtaining safety index parameter information of a plurality of current safety indexes of a target network system, and obtaining a safety index information set.
S300: dividing the safety index information set to obtain a physical safety index information set, an information safety index information set and a management safety index information set;
specifically, the safety index information set is obtained based on collection. And dividing the safety index information set according to the physical safety index, the information safety index and the management safety index.
Step S300 in the method provided in the embodiment of the present application includes:
s310: the method comprises the steps of obtaining a plurality of physical safety indexes, a plurality of information safety indexes and a plurality of management safety indexes, wherein the plurality of physical safety indexes, the plurality of information safety indexes and the plurality of management safety indexes form the plurality of safety indexes;
s320: and dividing the safety index information set by adopting the plurality of physical safety indexes, the plurality of information safety indexes and the plurality of management safety indexes to obtain a physical safety index information set, an information safety index information set and a management safety index information set.
As described above, a plurality of physical security indexes, a plurality of information security indexes, and a plurality of management security indexes are obtained, and the plurality of physical security indexes, the plurality of information security indexes, and the plurality of management security indexes constitute the plurality of security indexes.
And dividing and classifying the safety index parameter information in the safety index information set by adopting the plurality of physical safety indexes, the plurality of information safety indexes and the plurality of management safety indexes to obtain a physical safety index information set, an information safety index information set and a management safety index information set. The parameter information of different types of network safety indexes is divided to be used as a data basis for evaluating and predicting the network safety based on different safety indexes.
S400: performing weight distribution on the plurality of safety indexes to obtain a weight distribution result, wherein the weight distribution result comprises an overall weight distribution result, a physical safety index weight distribution result, an information safety index weight distribution result and a management safety index weight distribution result;
specifically, each safety index has different ability to affect the network security of the target network system, and therefore, weight distribution needs to be performed according to the ability level of the plurality of safety indexes to affect the network security of the target network system.
Step S400 in the method provided in the embodiment of the present application includes:
s410: acquiring a plurality of same family network systems of the target network system according to the target network system;
s420: acquiring the times of network security problems of the target network system and the plurality of same-family network systems in a preset time range to obtain the time information of the network security problems;
s430: acquiring and acquiring safety index parameter information of a plurality of safety indexes when the target network system and the plurality of same-family network systems have network safety problems within a preset time range, and acquiring a plurality of historical problem safety index information sets;
s440: according to the multiple historical problem safety index information sets and the network safety problem frequency information, obtaining the frequency of problems of the multiple safety indexes and obtaining multiple safety index problem frequency information;
s450: and according to the information of the problem times of the plurality of safety indexes, carrying out weight distribution on the plurality of safety indexes to obtain a weight distribution result.
Specifically, according to the current target network system, a plurality of network systems of the same family of the target network system are obtained. Wherein, the same family network system is the same or similar network system with the target network system. Illustratively, if the target network system is a CRM system purchased by a certain enterprise from a software system facilitator, the family network system is a CRM system provided by the software system facilitator to other enterprises, and the family network system and the target network system have similar or identical network system structures, identical servers and server positions, and the like.
In the embodiment of the application, the situation that the target network system and a plurality of same-family network systems have network security problems in the previous history is used as experience data to evaluate and predict the network system security.
Acquiring the times of network security problems of the target network system and the plurality of network systems of the same family within a preset time range, and acquiring the time information of the network security problems. The preset time range may be a time range of any time length, such as one year. The network security problem is the network security fault conditions that data leakage, tampering, deletion and the like occur in a network system, authority is tampered and the like, network communication is attacked and the like.
And further acquiring and acquiring safety index parameter information of a plurality of safety indexes when the target network system and the plurality of network systems in the same family have the network safety problems, namely acquiring a plurality of historical problem safety index information sets when the network system has the network safety problems. For example, the parameter information before acquiring the plurality of safety indexes may be collected based on a log record of operation and maintenance of the network system.
According to the multiple sets of historical problem security index information, whether the index parameter information of each security index has a problem, such as whether a network firewall is out of order or not, can be known when a network system has a network security problem, and theoretically, the more the security index parameter information having a problem, the more the network security problem is likely to occur. By combining the network security problem frequency information, the frequency of the plurality of security indexes having problems in the preset time range can be obtained, and the plurality of security index problem frequency information of the plurality of security indexes can be obtained.
And performing weight distribution on the plurality of safety indexes based on the plurality of safety index problem frequency information. If the information of the number of times of the network security problems of a certain security index is larger, it is indicated that in many cases, when a network security problem occurs in a network system, the security index has a problem, the security index affects the level of the network security problem occurring in the network system, and the weight value is larger, otherwise, when the network security problem occurs in the network system, no problem occurs in most cases in a certain security index, the level of the network security problem occurring in the network system affected by the security index is lower, and the weight value is smaller.
As shown in fig. 2, step S450 in the method provided in the embodiment of the present application includes:
s451: according to the plurality of safety index problem frequency information, obtaining a plurality of physical safety index problem frequency information, a plurality of information safety index problem frequency information and a plurality of management safety index problem frequency information in a dividing mode;
s452: according to the magnitude of the problem frequency information of the physical safety indexes, carrying out weight distribution on the physical safety indexes to obtain a weight distribution result of the physical safety indexes;
s453: according to the information of the problem times of the information safety indexes, carrying out weight distribution on the information safety indexes to obtain weight distribution results of the information safety indexes;
s454: according to the problem frequency information of the multiple management safety indexes, carrying out weight distribution on the multiple management safety indexes to obtain a management safety index weight distribution result;
s455: according to the multiple pieces of physical safety index problem frequency information, the multiple pieces of information safety index problem frequency information and the multiple pieces of management safety index problem frequency information, respectively adding to obtain physical safety index problem frequency information, information safety index problem frequency information and management safety index problem frequency information;
s456: and performing weight distribution according to the physical safety index problem frequency information, the information safety index problem frequency information and the management safety index problem frequency information to obtain the overall weight distribution result.
Specifically, according to the plurality of safety index problem frequency information, the plurality of safety index problem frequency information is divided according to the plurality of physical safety indexes, the plurality of information safety indexes and the plurality of management safety indexes, and the plurality of physical safety index problem frequency information, the plurality of information safety index problem frequency information and the plurality of management safety index problem frequency information are obtained.
Furthermore, weight distribution is carried out in the plurality of physical safety indexes according to the size of the physical safety index problem frequency information of each physical safety index, if the physical safety index problem frequency information is larger, the weight value of the corresponding physical safety index is larger, and thus, a physical safety index weight distribution result is obtained. The physical safety index weight distribution result comprises a plurality of weight values of the physical safety indexes, and the sum of the weight values of the physical safety indexes is 1.
In the process of weight allocation, experts in the network security field may be used for expert weighting, and a weight allocation method in the prior art, such as an AHP hierarchy analysis method, may also be used for weight allocation.
Similarly, according to the information of the problem times of the information safety indexes, carrying out weight distribution on the information safety indexes to obtain a weight distribution result of the information safety indexes. The information security index weight distribution result comprises a plurality of weight values of the information security index, and the sum of the weight values is 1.
And performing weight distribution on the plurality of management safety indexes according to the problem frequency information of the plurality of management safety indexes to obtain a management safety index weight distribution result. The management safety index weight distribution result comprises a plurality of weight values of the management safety indexes, and the sum of the weight values is 1.
After the weights are respectively distributed in the multiple physical safety indexes, the multiple information safety indexes and the multiple management safety indexes, the weights are also distributed to the influence capacity of the multiple physical safety indexes, the multiple information safety indexes and the multiple management safety indexes on the network safety problem of the network system.
Specifically, the physical safety index problem frequency information of all physical safety indexes, the information safety index problem frequency information of all information safety indexes and the management safety index problem frequency information of all management safety indexes are obtained through calculation and are respectively added according to the multiple physical safety index problem frequency information, the multiple information safety index problem frequency information and the multiple management safety index problem frequency information.
And according to the physical security index problem frequency information, the information security index problem frequency information and the management security index problem frequency information, carrying out weight distribution on all the physical security indexes, all the information security indexes and all the management security indexes, wherein the weight distribution mode is the same as that of the steps S425-S454 in the content, and obtaining a total weight distribution result. The overall weight distribution result comprises three weight values of all physical safety indexes, all information safety indexes and all management safety indexes, and the weight value of all information safety indexes is the largest.
According to the method and the device, based on the collection and acquisition of the number of times of network safety problems appearing before in a target network system and a family network system and the number of times of problems appearing in parameter information of each safety index when the network safety problems appear, the relation between the occurrence of the problems in the safety indexes and the occurrence of the network safety problems is taken as a starting point, the capability level of the network system of the network safety problems caused by the influence of the problems in the safety indexes is analyzed, weight distribution is carried out, a relatively accurate weight distribution result can be obtained, and the weight distribution result is used as a data basis for evaluating and predicting the safety of the network system by combining the parameter information of the safety indexes.
S500: constructing a network security assessment prediction model for assessing the network security of the target network system based on the weight distribution result, wherein the network security assessment prediction model comprises a physical security assessment prediction submodel, an information security assessment prediction submodel and a management security assessment prediction submodel;
and constructing a network security assessment prediction model for assessing and predicting the network security of the target network system based on the weight distribution result. The network security assessment prediction model comprises a physical security assessment prediction submodel, an information security assessment prediction submodel and a management security assessment prediction submodel, and network security assessment prediction can be carried out according to a plurality of physical security indexes, a plurality of information security indexes and a plurality of management security indexes.
As shown in fig. 3, step S500 in the method provided in the embodiment of the present application includes:
s510: acquiring and obtaining safety index parameter information of a plurality of safety indexes of the target network system and the plurality of same-family network systems in a plurality of previous preset time periods to obtain a plurality of historical safety index information sets;
s520: acquiring probability information of network security problems of the target network system and the plurality of same-family network systems in a plurality of preset time periods before, and acquiring a historical network security state information set;
s530: dividing the plurality of historical safety index information sets to obtain a plurality of historical physical safety index information sets, a plurality of historical information safety index information sets and a plurality of historical management safety index information sets;
s540: establishing and obtaining the physical security assessment prediction sub-model by taking the plurality of historical physical security index information sets, the historical network security state information set and the physical security index weight distribution result as a first construction data set;
s550: establishing and obtaining the information security assessment prediction sub-model by taking the plurality of historical information security index information sets, the historical network security state information set and the information security index weight distribution result as a second construction data set;
s560: establishing and obtaining the management security assessment prediction submodel by taking the plurality of historical management security index information sets, the historical network security state information set and the management security index weight distribution result as a third construction data set;
s570: and combining the physical security assessment forecasting submodel, the information security assessment forecasting submodel and the management security assessment forecasting submodel to obtain the network security assessment forecasting model.
Specifically, the method comprises the steps of acquiring and obtaining safety index parameter information of a plurality of safety indexes of a target network system and a plurality of family network systems in a plurality of previous preset time periods, and obtaining a plurality of historical safety index information sets. The preset time period may be a time period of any time length, such as one day, one week, and the like. The method comprises the steps of collecting safety index parameter information of a plurality of safety indexes in a plurality of preset time periods, collecting and obtaining average parameter information of a certain safety index in the preset time period, and collecting and obtaining average parameter information with the most frequent occurrence frequency of the safety index in the preset time period, wherein the safety index parameter information is determined according to the type of the safety index.
The method comprises the steps of acquiring probability information of network security problems of a target network system and a plurality of network systems of the same family in a plurality of preset time periods, wherein the probability information of the network security problems of the network system in the operation process can be calculated and obtained by calculating the ratio of the time of the network security problems of the network system in the preset time periods to the operation time of the network system, and further a historical network security state information set is obtained.
And each historical safety index parameter information set in the plurality of historical safety index information sets corresponds to the historical network safety state information in the historical network safety state information set one by one.
And further, constructing the physical security assessment prediction submodel by taking a plurality of historical physical security index information sets, historical network security state information sets and physical security index weight distribution results as a first construction data set.
And constructing the information security assessment prediction submodel by taking a plurality of historical information security index information sets, historical network security state information sets and information security index weight distribution results as second construction data sets.
Similarly, a plurality of historical management safety index information sets, historical network safety state information sets and management safety index weight distribution results are used as a third construction data set to construct the management safety evaluation prediction submodel.
The following describes the construction process of the information security assessment predictor model in detail by taking the construction of the information security assessment predictor model as an example.
Step S550 in the method provided in the embodiment of the present application includes:
s551: dividing and identifying the plurality of historical information safety index information sets and the historical network safety state information sets, and obtaining a training data set, a verification data set and a test data set by combining the information safety index weight distribution result;
s552: constructing the information security assessment prediction sub-model based on the BP neural network model;
s553: carrying out supervision training on the information safety assessment forecasting submodel by adopting the training data set until the output result of the information safety assessment forecasting submodel is converged or meets the requirement of preset accuracy rate;
s554: and verifying and testing the information security assessment predictor model by adopting the verification data set and the test data set, and if the output result of the information security assessment predictor model meets the preset accuracy requirement, obtaining the information security assessment predictor model.
Specifically, a plurality of historical information safety index information sets and historical network safety state information sets are divided according to the proportion of 6. The input data of the information security assessment and prediction submodel is an information security index information set and an information security index weight distribution result, and the output data is network security state information, namely an information network security assessment and prediction result, wherein the output data comprises probability information of network security problems of a network system under the currently input information security index information set and the information security index weight distribution result.
And constructing a network structure of the information security assessment predictor model based on the BP neural network in machine learning and by combining the input data and the output data. Then, the information safety assessment prediction submodel is supervised and trained by adopting the training data set, in the process of supervised training and learning, parameters such as weight and the like in the safety assessment prediction submodel are continuously corrected and adjusted until the output result of the safety assessment prediction submodel is converged or the preset accuracy requirement is met, and then the supervised training is completed. The preset accuracy requirement can be set according to the requirement in combination with machine learning, and can be 90% for example.
And verifying and testing the information security assessment prediction submodel by adopting the verification data set and the test data set, and if the output result of the information security assessment prediction submodel still meets the preset accuracy requirement under the verification and the test, and the situations such as overfitting do not occur, so that the constructed information security assessment prediction submodel is obtained. If the preset accuracy requirement is not met, the model needs to be updated in parameters or reconstructed until the preset accuracy requirement is met.
Based on the constructed safety assessment prediction submodel, an information safety index information set and an information safety index weight distribution result can be input into the safety assessment prediction submodel, and probability information of network safety problems of a network system, which is obtained based on the information safety index information set assessment prediction, is obtained, namely the information network safety assessment result based on the information safety index.
The construction process of the physical security assessment predictor model and the management security assessment predictor model is the same as that of the information security assessment predictor model, but the input data and the output data are different, so the convergence speed and the construction time in the supervision training process of constructing and obtaining the physical security assessment predictor model and the management security assessment predictor model are also different, and the construction process is the same.
The input data of the physical security assessment predictor model is a physical security index information set and a physical security index weight distribution result, and the output data is network security state information, namely a physical network security assessment result.
The input data of the management security assessment predictor model is a management security index information set and a management security index weight distribution result, and the output data is network security state information, namely a management network security assessment result.
And integrating and combining the three submodels based on the constructed physical security assessment forecasting submodel, the information security assessment forecasting submodel and the management security assessment forecasting submodel to obtain a network security assessment forecasting model.
According to the method and the device, a physical security assessment prediction sub-model, an information security assessment prediction sub-model and a management security assessment prediction sub-model are respectively constructed and obtained by acquiring security index information sets and network security state information of a target network system and a plurality of same-family network systems in a plurality of previous preset time periods, the probability of occurrence of network security problems of the network system can be estimated and predicted respectively on the basis of the physical security index parameter information, the information security index parameter information and the management security index parameter information of the network systems, and then the probability of occurrence of network security problems of comprehensive assessment prediction can be obtained on the basis of a network security assessment prediction model.
S600: inputting the physical security index information set, the information security index information set, the management security index information set, the physical security index weight distribution result, the information security index weight distribution result and the management security index weight distribution result into the network security assessment prediction model to obtain a physical network security assessment result, an information network security assessment result and a management network security assessment result;
specifically, the current physical security index information set, information security index information set, management security index information set, physical security index weight assignment result, information security index weight assignment result, and management security index weight assignment result of the target network system in the above contents are input into the network security assessment prediction model, wherein the physical security index information set and the physical security index weight assignment result are respectively input into the physical security assessment prediction submodel, the information security index information set and the information security index weight assignment result are input into the information security assessment prediction submodel, and the management security index information set and the management security index weight assignment result are input into the management security assessment prediction submodel.
In this way, a physical network security evaluation result, an information network security evaluation result and a management network security evaluation result which are obtained by the physical security evaluation prediction submodel, the information security evaluation prediction submodel and the management security evaluation prediction submodel through evaluation prediction are respectively obtained, wherein the probability information of the network security problem of the current target network system, which is obtained by the physical security evaluation prediction submodel, the information security evaluation prediction submodel and the management security evaluation prediction submodel through evaluation prediction, is respectively included.
S700: and weighting the physical network security evaluation result, the information network security evaluation result and the management network security evaluation result by adopting the overall weight distribution result to obtain a network security evaluation prediction result.
Step S700 in the method provided in the embodiment of the present application includes:
s710: obtaining first prediction probability information, second prediction probability information and third prediction probability information according to the physical network security evaluation result, the information network security evaluation result and the management network security evaluation result;
s720: and performing weighted summation on the first prediction probability information, the second prediction probability information and the third prediction probability information by adopting the overall weight distribution result to obtain overall probability information which is used as the network security evaluation prediction result.
Specifically, based on the physical security assessment prediction submodel, the information security assessment prediction submodel, and the physical network security assessment result, the information network security assessment result, and the management network security assessment result obtained by the management security assessment prediction submodel in the above contents, the first prediction probability information, the second prediction probability information, and the third prediction probability information obtained by the current security index information set assessment prediction are obtained.
And weighting and summing the first prediction probability information, the second prediction probability information and the third prediction probability information to obtain total probability information which is used as a final network security assessment prediction result.
The final network security assessment prediction result comprises probability information obtained by weighted summation, and the probability information can be used as reference data of the probability that the current target network system has network security problems, so that the reference data can be used for users and operation and maintenance personnel of the target network system to carry out relevant network security measures, and the network security level of the network system is improved.
In summary, the embodiment of the present application has at least the following technical effects:
according to the method and the device, parameter information of a plurality of safety indexes of a network system is acquired through collection, the parameters are divided according to physical factors, information factors and management factors, network safety assessment prediction is carried out respectively, various factors can be comprehensively considered to carry out network safety assessment prediction, and finally, assessment prediction results are comprehensively obtained without being limited to analysis of communication information safety.
Example two
Based on the same inventive concept as one of the methods for network security assessment prediction in the foregoing embodiments, as shown in fig. 4, the present application provides a system for network security assessment prediction, wherein the system includes:
a network system determining module 11, configured to determine a target network system, where the target network system is a network system to be subjected to network security evaluation;
a safety index information acquisition module 12, configured to acquire safety index parameter information of multiple safety indexes of the target network system, and acquire a safety index information set;
a safety index information dividing module 13, configured to divide the safety index information set to obtain a physical safety index information set, an information safety index information set, and a management safety index information set;
the weight distribution module 14 is configured to perform weight distribution on the multiple security indicators to obtain a weight distribution result, where the weight distribution result includes an overall weight distribution result, a physical security indicator weight distribution result, an information security indicator weight distribution result, and a management security indicator weight distribution result;
an evaluation model construction module 15, configured to construct, based on the weight assignment result, a network security evaluation prediction model for evaluating network security of the target network system, where the network security evaluation prediction model includes a physical security evaluation prediction sub-model, an information security evaluation prediction sub-model, and a management security evaluation prediction sub-model;
a security assessment prediction module 16, configured to input the physical security index information set, the information security index information set, the management security index information set, the physical security index weight distribution result, the information security index weight distribution result, and the management security index weight distribution result into the network security assessment prediction model, so as to obtain a physical network security assessment result, an information network security assessment result, and a management network security assessment result;
and the weighting calculation module 17 is configured to weight the physical network security evaluation result, the information network security evaluation result, and the management network security evaluation result by using the overall weight distribution result to obtain a network security evaluation prediction result.
Further, the safety index information partitioning module 13 is further configured to implement the following functions:
acquiring a plurality of physical safety indexes, a plurality of information safety indexes and a plurality of management safety indexes, wherein the plurality of physical safety indexes, the plurality of information safety indexes and the plurality of management safety indexes form the plurality of safety indexes;
and dividing the safety index information set by adopting the plurality of physical safety indexes, the plurality of information safety indexes and the plurality of management safety indexes to obtain the physical safety index information set, the information safety index information set and the management safety index information set.
Further, the weight assignment module 14 is further configured to implement the following functions:
acquiring a plurality of same family network systems of the target network system according to the target network system;
acquiring the times of network security problems of the target network system and the plurality of same-family network systems in a preset time range to obtain the time information of the network security problems;
acquiring safety index parameter information of a plurality of safety indexes when the target network system and the plurality of same-family network systems have network safety problems in a preset time range, and acquiring a plurality of historical problem safety index information sets;
according to the multiple historical problem safety index information sets and the network safety problem frequency information, obtaining the frequency of problems of the multiple safety indexes and obtaining multiple safety index problem frequency information;
and according to the information of the problem times of the plurality of safety indexes, carrying out weight distribution on the plurality of safety indexes to obtain a weight distribution result.
Wherein, according to the information of the problem times of the plurality of safety indexes, the weight distribution of the plurality of safety indexes comprises the following steps:
according to the plurality of safety index problem frequency information, obtaining a plurality of physical safety index problem frequency information, a plurality of information safety index problem frequency information and a plurality of management safety index problem frequency information in a dividing mode;
according to the magnitude of the problem frequency information of the physical safety indexes, carrying out weight distribution on the physical safety indexes to obtain a weight distribution result of the physical safety indexes;
according to the problem frequency information of the information safety indexes, carrying out weight distribution on the information safety indexes to obtain a weight distribution result of the information safety indexes;
according to the problem frequency information of the multiple management safety indexes, carrying out weight distribution on the multiple management safety indexes to obtain a management safety index weight distribution result;
according to the multiple pieces of physical safety index problem frequency information, the multiple pieces of information safety index problem frequency information and the multiple pieces of management safety index problem frequency information, respectively adding to obtain physical safety index problem frequency information, information safety index problem frequency information and management safety index problem frequency information;
and performing weight distribution according to the physical safety index problem frequency information, the information safety index problem frequency information and the management safety index problem frequency information to obtain the overall weight distribution result.
Further, the evaluation model building module 15 is further configured to implement the following functions:
acquiring and obtaining safety index parameter information of a plurality of safety indexes of the target network system and the plurality of same-family network systems in a plurality of previous preset time periods to obtain a plurality of historical safety index information sets;
acquiring probability information of network security problems of the target network system and the plurality of same-family network systems in a plurality of preset time periods before, and acquiring a historical network security state information set;
dividing the plurality of historical safety index information sets to obtain a plurality of historical physical safety index information sets, a plurality of historical information safety index information sets and a plurality of historical management safety index information sets;
establishing and obtaining the physical security assessment prediction sub-model by taking the plurality of historical physical security index information sets, the historical network security state information set and the physical security index weight distribution result as a first construction data set;
establishing and obtaining the information security assessment prediction sub-model by taking the plurality of historical information security index information sets, the historical network security state information set and the information security index weight distribution result as a second construction data set;
establishing and obtaining the management security assessment prediction submodel by taking the plurality of historical management security index information sets, the historical network security state information set and the management security index weight distribution result as a third construction data set;
and combining the physical security assessment prediction submodel, the information security assessment prediction submodel and the management security assessment prediction submodel to obtain the network security assessment prediction model.
The method for constructing and obtaining the information security assessment prediction sub-model by using the plurality of historical information security index information sets, the historical network security state information set and the information security index weight distribution result as a second construction data set comprises the following steps:
dividing and identifying the plurality of historical information safety index information sets and the historical network safety state information sets, and obtaining a training data set, a verification data set and a test data set by combining the information safety index weight distribution result;
constructing the information security assessment prediction sub-model based on the BP neural network model;
carrying out supervision training on the information safety assessment forecasting submodel by adopting the training data set until the output result of the information safety assessment forecasting submodel is converged or meets the requirement of preset accuracy rate;
and verifying and testing the information security assessment predictor model by adopting the verification data set and the test data set, and if the output result of the information security assessment predictor model meets the preset accuracy requirement, obtaining the information security assessment predictor model.
Further, the weight calculating module 17 is further configured to implement the following functions:
obtaining first prediction probability information, second prediction probability information and third prediction probability information according to the physical network security evaluation result, the information network security evaluation result and the management network security evaluation result;
and performing weighted summation on the first prediction probability information, the second prediction probability information and the third prediction probability information by adopting the overall weight distribution result to obtain overall probability information which is used as the network security evaluation prediction result.
The specification and figures are merely exemplary of the application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and its equivalent technology, it is intended that the present application include such modifications and variations.
Claims (5)
1. A method for network security assessment prediction, the method comprising:
determining a target network system, wherein the target network system is a network system to be subjected to network security evaluation;
acquiring safety index parameter information of a plurality of safety indexes of the target network system to obtain a safety index information set;
dividing the safety index information set to obtain a physical safety index information set, an information safety index information set and a management safety index information set;
performing weight distribution on the plurality of safety indexes to obtain a weight distribution result, wherein the weight distribution result comprises a total weight distribution result, a physical safety index weight distribution result, an information safety index weight distribution result and a management safety index weight distribution result, and the performing weight distribution on the plurality of safety indexes comprises: acquiring a plurality of same family network systems of the target network system according to the target network system; acquiring the times of network security problems of the target network system and the plurality of same-family network systems in a preset time range to obtain the time information of the network security problems; acquiring and acquiring safety index parameter information of a plurality of safety indexes when the target network system and the plurality of same-family network systems have network safety problems within a preset time range, and acquiring a plurality of historical problem safety index information sets; according to the multiple historical problem safety index information sets and the network safety problem frequency information, obtaining the frequency of problems of the multiple safety indexes and obtaining multiple safety index problem frequency information; according to the plurality of safety index problem frequency information, carrying out weight distribution on the plurality of safety indexes to obtain a weight distribution result, wherein the weight distribution result comprises that according to the plurality of safety index problem frequency information, a plurality of physical safety index problem frequency information, a plurality of information safety index problem frequency information and a plurality of management safety index problem frequency information are obtained in a dividing mode; according to the magnitude of the problem frequency information of the physical safety indexes, carrying out weight distribution on the physical safety indexes to obtain a weight distribution result of the physical safety indexes; according to the information of the problem times of the information safety indexes, carrying out weight distribution on the information safety indexes to obtain weight distribution results of the information safety indexes; according to the problem frequency information of the multiple management safety indexes, carrying out weight distribution on the multiple management safety indexes to obtain a management safety index weight distribution result; according to the multiple pieces of physical safety index problem frequency information, the multiple pieces of information safety index problem frequency information and the multiple pieces of management safety index problem frequency information, respectively adding to obtain physical safety index problem frequency information, information safety index problem frequency information and management safety index problem frequency information; performing weight distribution according to the physical safety index problem frequency information, the information safety index problem frequency information and the management safety index problem frequency information to obtain the overall weight distribution result;
constructing a network security assessment prediction model for assessing the network security of the target network system based on the weight distribution result, wherein the network security assessment prediction model comprises a physical security assessment prediction submodel, an information security assessment prediction submodel and a management security assessment prediction submodel;
inputting the physical security index information set, the information security index information set, the management security index information set, the physical security index weight distribution result, the information security index weight distribution result and the management security index weight distribution result into the network security assessment prediction model to obtain a physical network security assessment result, an information network security assessment result and a management network security assessment result;
weighting the physical network security evaluation result, the information network security evaluation result and the management network security evaluation result by adopting the overall weight distribution result to obtain a network security evaluation prediction result, wherein the network security evaluation prediction result comprises the following steps: obtaining first prediction probability information, second prediction probability information and third prediction probability information according to the physical network security evaluation result, the information network security evaluation result and the management network security evaluation result; and performing weighted summation on the first prediction probability information, the second prediction probability information and the third prediction probability information by adopting the overall weight distribution result to obtain overall probability information which is used as the network security assessment prediction result.
2. The method of claim 1, wherein partitioning the set of security index information comprises:
the method comprises the steps of obtaining a plurality of physical safety indexes, a plurality of information safety indexes and a plurality of management safety indexes, wherein the plurality of physical safety indexes, the plurality of information safety indexes and the plurality of management safety indexes form the plurality of safety indexes;
and dividing the safety index information set by adopting the plurality of physical safety indexes, the plurality of information safety indexes and the plurality of management safety indexes to obtain a physical safety index information set, an information safety index information set and a management safety index information set.
3. The method of claim 1, wherein constructing a network security assessment prediction model for assessing the network security of the target network system based on the weight assignment result comprises:
acquiring and obtaining safety index parameter information of a plurality of safety indexes of the target network system and the plurality of same-family network systems in a plurality of previous preset time periods to obtain a plurality of historical safety index information sets;
acquiring probability information of network security problems of the target network system and the plurality of same-family network systems in a plurality of preset time periods before, and acquiring a historical network security state information set;
dividing the plurality of historical safety index information sets to obtain a plurality of historical physical safety index information sets, a plurality of historical information safety index information sets and a plurality of historical management safety index information sets;
establishing and obtaining the physical security assessment prediction submodel by taking the plurality of historical physical security index information sets, the historical network security state information set and the physical security index weight distribution result as a first construction data set;
establishing and obtaining the information security assessment prediction submodel by taking the plurality of historical information security index information sets, the historical network security state information set and the information security index weight distribution result as a second construction data set;
establishing and obtaining the management security assessment prediction submodel by taking the plurality of historical management security index information sets, the historical network security state information set and the management security index weight distribution result as a third construction data set;
and combining the physical security assessment forecasting submodel, the information security assessment forecasting submodel and the management security assessment forecasting submodel to obtain the network security assessment forecasting model.
4. The method of claim 3, wherein the constructing and obtaining the information security assessment predictor model with the plurality of sets of historical information security index information, the sets of historical network security status information, and the information security index weight assignment result as a second set of construction data comprises:
dividing and identifying the plurality of historical information safety index information sets and the historical network safety state information sets, and obtaining a training data set, a verification data set and a test data set by combining the information safety index weight distribution result;
constructing the information security assessment prediction sub-model based on the BP neural network model;
carrying out supervision training on the information safety assessment prediction submodel by adopting the training data set until the output result of the information safety assessment prediction submodel is converged or meets the requirement of preset accuracy;
and verifying and testing the information security assessment prediction submodel by adopting the verification data set and the test data set, and if the output result of the information security assessment prediction submodel meets the preset accuracy requirement, obtaining the information security assessment prediction submodel.
5. A system for network security assessment prediction, the system comprising:
the network system determination module is used for determining a target network system, wherein the target network system is a network system to be subjected to network security evaluation;
the safety index information acquisition module is used for acquiring safety index parameter information of a plurality of safety indexes of the target network system to obtain a safety index information set;
the safety index information dividing module is used for dividing the safety index information set to obtain a physical safety index information set, an information safety index information set and a management safety index information set;
a weight distribution module, configured to perform weight distribution on the multiple security indicators to obtain a weight distribution result, where the weight distribution result includes a total weight distribution result, a physical security indicator weight distribution result, an information security indicator weight distribution result, and a management security indicator weight distribution result, and performing weight distribution on the multiple security indicators includes: acquiring a plurality of same family network systems of the target network system according to the target network system; acquiring the times of network security problems of the target network system and the plurality of same-family network systems in a preset time range to obtain the time information of the network security problems; acquiring and acquiring safety index parameter information of a plurality of safety indexes when the target network system and the plurality of same-family network systems have network safety problems within a preset time range, and acquiring a plurality of historical problem safety index information sets; according to the multiple historical problem safety index information sets and the network safety problem frequency information, obtaining the frequency of problems of the multiple safety indexes and obtaining multiple safety index problem frequency information; according to the information of the number of the safety index problems, carrying out weight distribution on the plurality of safety indexes to obtain a weight distribution result, wherein the weight distribution result comprises the steps of obtaining information of the number of the physical safety index problems, the information of the number of the safety index problems and the information of the number of the management safety index problems in a dividing mode according to the information of the number of the safety index problems; according to the magnitude of the problem frequency information of the physical safety indexes, carrying out weight distribution on the physical safety indexes to obtain a weight distribution result of the physical safety indexes; according to the information of the problem times of the information safety indexes, carrying out weight distribution on the information safety indexes to obtain weight distribution results of the information safety indexes; according to the problem frequency information of the multiple management safety indexes, carrying out weight distribution on the multiple management safety indexes to obtain a management safety index weight distribution result; according to the multiple pieces of physical safety index problem frequency information, the multiple pieces of information safety index problem frequency information and the multiple pieces of management safety index problem frequency information, respectively adding to obtain physical safety index problem frequency information, information safety index problem frequency information and management safety index problem frequency information; performing weight distribution according to the physical safety index problem frequency information, the information safety index problem frequency information and the management safety index problem frequency information to obtain the overall weight distribution result;
the evaluation model building module is used for building a network security evaluation prediction model for evaluating the network security of the target network system based on the weight distribution result, wherein the network security evaluation prediction model comprises a physical security evaluation prediction submodel, an information security evaluation prediction submodel and a management security evaluation prediction submodel;
the safety assessment prediction module is used for inputting the physical safety index information set, the information safety index information set, the management safety index information set, the physical safety index weight distribution result, the information safety index weight distribution result and the management safety index weight distribution result into the network safety assessment prediction model to obtain a physical network safety assessment result, an information network safety assessment result and a management network safety assessment result;
the weighting calculation module is used for weighting the physical network security evaluation result, the information network security evaluation result and the management network security evaluation result by adopting the overall weight distribution result to obtain a network security evaluation prediction result, and the weighting calculation module comprises: obtaining first prediction probability information, second prediction probability information and third prediction probability information according to the physical network security evaluation result, the information network security evaluation result and the management network security evaluation result; and performing weighted summation on the first prediction probability information, the second prediction probability information and the third prediction probability information by adopting the overall weight distribution result to obtain overall probability information which is used as the network security evaluation prediction result.
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