CN111800301A - Network security evaluation method and system in machine type communication - Google Patents
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Abstract
The invention relates to a network security evaluation method and a system in machine type communication, which comprises the steps of constructing a deep learning network model based on predefined network security key characteristic quantity in the machine type communication; the network safety and health state in machine type communication is comprehensively evaluated by combining a deep learning network model and an expert discussion system; and updating the deep learning network model on line according to the evaluation result. The online training and updating of the multilayer extreme learning machine are realized through the feedback iteration of the expert discussion system and the deep learning network, so that the input results of the two are finally consistent, and the calculation result of the network safety and health index is more real and credible.
Description
Technical Field
The invention relates to the technical field of monitoring, in particular to a network security evaluation method and system in machine type communication.
Background
Machine type communication is an important communication facility in modern cities, and the network safety and the quality of users can be directly influenced by the good or bad communication operation state.
In the past, evaluation on machine type communication mainly focuses on indexes such as reliability, economy, safety and quality of machine type communication, and the health state of machine type communication cannot be comprehensively grasped, so that a network safety and health index concept in machine type communication needs to be introduced. The traditional network security evaluation method adopts a calculation formula proposed by the United kingdom EA company, but the method only considers the influence of aging on equipment, cannot comprehensively consider various factors and cannot be expanded to a machine type communication network. In recent years, machine type communication health evaluation methods based on an analytic hierarchy process, a delphire method, an optimization principle, gray correlation analysis, and the like have been proposed. However, some of the methods are only suitable for single equipment, some parameter selections are too subjective, the inheritance and the multiplexing of expert experiences cannot be realized, and the problem of how to carry out network security health index calculation for a large-scale communication network still cannot be solved.
Currently, although the conventional evaluation method can realize the evaluation of the network security health state in the machine type communication, the evaluation error is caused by the lack of instant feedback in the evaluation process, and the consequent negative influence and the severity of economic loss can not be evaluated.
Disclosure of Invention
In order to solve the above problems, the present invention provides a method and a system for evaluating network security in machine type communication, which evaluate a network security health index from a new perspective. The deep learning principle is combined with an expert discussion system, so that the online training and updating of the evaluation model are realized, and the output result is more reasonable; therefore, the problem of safety evaluation of the network safety and health index aiming at large-scale communication equipment and networks is solved, and the inheritance and the reuse of expert experience are facilitated.
The purpose of the invention is realized by adopting the following technical scheme:
a method for network security evaluation in machine type communication, the method comprising:
constructing a deep learning network model based on network security key characteristic quantity in predefined machine type communication;
the network safety and health state in machine type communication is comprehensively evaluated by combining a deep learning network model and an expert discussion system;
and updating the deep learning network model on line according to the evaluation result.
Preferably, the network security critical feature quantities in the predefined machine type communication include: taking machine type communication as an evaluation object, and dividing the machine type communication into power distribution equipment and a machine type communication network according to a self-defined machine type communication classification rule; and acquiring the classified characteristic quantities of all parts, selecting key characteristic quantities for evaluating the network security and health index from the characteristic quantities, and generating a characteristic space.
Further, the selecting key characteristic quantities for evaluating the network security health index comprises: and obtaining key characteristic quantity through oil chromatographic analysis according to the dynamics of a network topological structure and member relation, the unreliability of a wireless channel, the electric quantity, the storage space, the calculation and the communication capacity of the terminal node and the routing node.
Preferably, the deep learning network model is a deep learning network formed based on a plurality of layers of extreme learning machines, and the construction method includes: and mapping the key characteristic quantity as an input sample into a new characteristic space to form a training sample set X ═ Xi,ti},i＝1,...,n；
Wherein x isiWhich represents the input samples of the sample to be tested,tirepresenting the network security health index corresponding to the input sample, wherein n is the number of the samples;
randomly generating connection weights for an input layer to a first hidden layer
And an output matrix H of the first hidden layer1(ii) a Calculating beta by least square method1The expression is as follows:
will be (beta)1)TReplacing the connection weight β of the input layer to the first hidden layer1Randomly generating the connection weight beta of the first hidden layer to the second hidden layer2And an output matrix H of the second hidden layer2；
Obtaining the output weight beta by the following formula calculation by adopting a least square method3：
In the formula, ajAnd bjAnd respectively representing an input weight value and a hidden layer threshold value which are randomly set and are associated with the jth hidden node, wherein z is the number of the hidden nodes, H is a generalized Jacobian matrix, and T is the total quantity of the network security health index corresponding to the sample.
Preferably, the comprehensive evaluation of the network security and health state in the machine type communication by combining the deep learning network model and the expert discussion system comprises; the newly generated sample x to be testednewRespectively inputting the expert discussion system and the deep learning network model, comparing the obtained output results, and judging whether the deep learning network model is consistent with the network security health index output by the expert discussion system; if the two models are consistent, the deep learning network model is good; if not, judging whether to execute the online updating of the deep learning network model according to the difference value; wherein,
the expert discussion system is used for online scoring of the health state of the machine type communication according to expert experience, relevant reference documents or experimental reports so as to achieve the acquisition of the safety and health index of the machine type communication network.
Preferably, the online updating of the deep learning network model according to the evaluation result includes: if the difference value of the output results of the expert discussion system and the deep learning network model is larger than a preset threshold value, the network safety and health index output by the expert discussion system is used as a mark { x) of the current sample to be testednew,tnew}；
Will { xnew,tnewAs a new sample subset X to be testednewWhen the number accumulation of the samples to be tested exceeds a user-defined threshold value, the deep learning network model is updated on line through the following formula:
will be provided with
Substituted for beta1The expression is as follows:
mixing X with XnewInputting the updated deep learning network and respectively outputting the optimal matrix
The expression is as follows:
will be provided with
Substituted for beta3Completing the on-line updating of the deep learning network;
in the formula, I is an identity matrix, P is any one infinite differentiable excitation function,
respectively inputting the connection weight of the input layer to the first hidden layer, the connection weight of the first hidden layer to the second hidden layer and the output weight in the updated deep learning network model;
and outputting the matrix for the updated first hidden layer.
A network security evaluation system in machine type communication, comprising:
the building module is used for building a deep learning network model based on the network security key characteristic quantity in the predefined machine type communication;
the evaluation module is used for comprehensively evaluating the network security and health state in the machine type communication by combining a deep learning network model and an expert discussion system;
and the updating module is used for updating the deep learning network model on line according to the evaluation result.
Preferably, the building block comprises:
the device comprises a determining unit, a judging unit and a judging unit, wherein the determining unit is used for taking the machine type communication as an evaluation object and dividing the machine type communication into power distribution equipment and a machine type communication network according to a self-defined machine type communication classification rule;
and the acquisition unit is used for acquiring the classified characteristic quantities of all the parts, selecting key characteristic quantities for evaluating the network security health index from the characteristic quantities and generating a characteristic space.
Further, the selecting unit includes: and the acquisition subunit is used for acquiring the key characteristic quantity through oil chromatographic analysis according to the dynamics of a network topology structure and member relationship, the unreliability of a wireless channel, the electric quantity, the storage space, the calculation and the communication capacity of the terminal node and the routing node.
Compared with the prior art, the invention has the following beneficial effects:
the network security evaluation method and system in machine type communication provided by the invention embody the combination of human intelligence and artificial intelligence, realize the inheritance and reuse of expert experience and knowledge, adopt a deep learning network, and solve the problem of accurately evaluating the health states of machine type communication equipment and a communication network when human resources are insufficient.
In the scheme of the invention, the evaluation object is selected firstly, and then the key characteristic quantity and the corresponding data information are selected according to the actual situation, so that the evaluation has timeliness. Through the extraction of the key characteristic quantity, the number of input quantities in the evaluation model can be reduced, and meanwhile, the redundant condition of various characteristic quantities to state evaluation is reduced, and the method is important for establishing the evaluation model.
By constructing the evaluation model, the evaluation of the health state of the evaluation object by using the existing evaluation algorithm can be realized. In addition, an expert comprehensive study is constructed, so that the expert can evaluate the health state of the evaluation object on line. The two scores are comprehensively evaluated, and a deep learning method is used in the evaluation process, so that the evaluation is more accurate, the evaluation error caused by the error of one party is avoided, and the threat to the safe operation of the machine type communication network is formed.
Comprehensive evaluation is the core of the method, and has a crucial role in evaluating accuracy. The comprehensive evaluation is to train the deep learning model according to the historical evaluation process to obtain a comparison model, then to input the evaluation score into the model, to compare the evaluation results, through evaluation inconsistency feedback, the model evaluation and the comprehensive study can continue to be carried out, convergence towards consistency is carried out, and finally both parties converge near the real health status value. The comprehensive evaluation enables the method to evaluate the health state of each node of the machine type communication rapidly, comprehensively, reliably and efficiently.
Drawings
FIG. 1 is a flow chart of a method for evaluating network security in machine type communication according to the present invention;
FIG. 2 is a flowchart of a method for comprehensive evaluation of network security and health in machine type communication according to the present invention;
FIG. 3 is a deep learning network model topological graph formed based on a multi-layer extreme learning machine provided by the invention.
Detailed Description
The technical solution in the embodiment of the present invention is described below with reference to the accompanying drawings.
A method for evaluating network security in machine type communication as shown in fig. 1 includes the following steps:
s1, constructing a deep learning network model based on the network security key characteristic quantity in the predefined machine type communication;
s2, combining the deep learning network model and the expert discussion system, carrying out comprehensive evaluation on the network security and health state in the machine type communication;
and S3, updating the deep learning network model on line according to the evaluation result.
1) The predefined network security critical feature quantities in machine type communication include: taking machine type communication as an evaluation object, and dividing the machine type communication into power distribution equipment and a machine type communication network according to a self-defined machine type communication classification rule; and acquiring the classified characteristic quantities of all parts, selecting key characteristic quantities for evaluating the network security and health index from the characteristic quantities, and generating a characteristic space.
The method for evaluating the network security health index comprises the following steps: and obtaining key characteristic quantity through oil chromatographic analysis according to the dynamics of a network topological structure and member relation, the unreliability of a wireless channel, the electric quantity, the storage space, the calculation and the communication capacity of the terminal node and the routing node.
2) Constructing a deep learning network model; the deep learning network model is a deep learning network formed based on a plurality of layers of extreme learning machines, and is shown in FIG. 3. The key characteristic quantities of the power distribution equipment and the network are mapped to the characteristic space of the hidden layer from the original space, a plurality of hidden nodes and activation functions are arranged between each layer, the health state of the current equipment or the machine type communication network can be identified according to the input key characteristic quantities, and the condition of the power distribution equipment and the condition of the network can be matched; and finally outputting the machine type communication network safety and health index.
The construction method comprises the following steps: and mapping the key characteristic quantity as an input sample into a new characteristic space to form a training sample set X ═ Xi,t i1, ·, n; wherein x isiRepresenting an input sample, tiRepresenting the network security health index corresponding to the input sample, wherein n is the number of the samples;
randomly generating connection weights for an input layer to a first hidden layer
And an output matrix H of the first hidden layer1(ii) a Calculating beta by least square method1The expression is as follows:
will be (beta)1)TReplacing the connection weight β of the input layer to the first hidden layer1Randomly generating the connection weight beta of the first hidden layer to the second hidden layer2And an output matrix H of the second hidden layer2；
Obtaining the output weight beta by the following formula calculation by adopting a least square method3：
In the formula, ajAnd bjAnd respectively representing an input weight value and a hidden layer threshold value which are randomly set and are associated with the jth hidden node, wherein z is the number of the hidden nodes, H is a generalized Jacobian matrix, and T is the total quantity of the network security health index corresponding to the sample.
The specific calculation principle for evaluating the health state index of machine type communication is as follows: the worse the health status, the higher the weight occupied by the part, and the more harmful the machine type communication, the higher the weight of the part. The calculation process mainly comprises the steps of firstly calculating a weight coefficient matrix, then multiplying the weight coefficient matrix with each health state index matrix to obtain a unified index value serving as the whole health state index of the machine type communication, and objectively reflecting the health state of the current machine type communication through the index to realize the monitoring of the network safety health state in the machine type communication.
3) The network safety and health state in machine type communication is comprehensively evaluated by combining a deep learning network model and an expert discussion system;
the expert discussion system is used for acquiring expert knowledge and experience, and regarding the network security health index output by the expert discussion system as an accurate value matched with the actual situation for marking a training sample, wherein the sample mark can be obtained from other data (such as reference documents, experimental reports and the like) outside the system; the online training and updating of the multilayer extreme learning machine are realized through the feedback iteration of the expert discussion system and the deep learning network, so that the input results of the two are finally consistent, and the calculation result of the network safety and health index is more real and credible.
The comprehensive evaluation of the network safety and health state in the machine type communication is carried out by combining a deep learning network model and an expert discussion system; the newly generated sample x to be testednewRespectively inputting the expert discussion system and the deep learning network model, comparing the obtained output results, and judging whether the deep learning network model is consistent with the network security health index output by the expert discussion system; if the two models are consistent, the deep learning network model is good; and if the difference values are not consistent, judging whether to execute online updating of the deep learning network model according to the difference values.
4) And updating the deep learning network model on line according to the evaluation result. The evaluation process of the machine type communication network safety and health index is a feedback adjustment process, a multi-layer extreme learning machine network is established by utilizing the principle of deep learning, and the deep learning network is trained according to historical evaluation data; and marking the newly input samples through an expert discussion system, and realizing the rolling update of the multilayer extreme learning machine model. The specific process is as follows:
if the difference value of the output results of the expert discussion system and the deep learning network model is larger than a preset threshold value, the network safety and health index output by the expert discussion system is used as a mark { x) of the current sample to be testednew,tnew}；
Will { xnew,tnewAs a new sample subset X to be testednewWhen the number accumulation of the samples to be tested exceeds a user-defined threshold value, the deep learning network model is updated on line through the following formula:
will be provided with
Substituted for beta1The expression is as follows:
mixing X with XnewInputting the updated deep learning network and respectively outputting the optimal matrix
The expression is as follows:
will be provided with
Substituted for beta3Completing the on-line updating of the deep learning network;
wherein I is an identity matrix, P is any one infinitely differentiable excitation function, P1＝(H1)TH1，
Respectively inputting the connection weight of the input layer to the first hidden layer, the connection weight of the first hidden layer to the second hidden layer and the output weight in the updated deep learning network model;
and outputting the matrix for the updated first hidden layer.
Based on the same technical concept, the invention also provides a network security evaluation system in machine type communication, which comprises:
the building module is used for building a deep learning network model based on the network security key characteristic quantity in the predefined machine type communication;
the evaluation module is used for comprehensively evaluating the network security and health state in the machine type communication by combining a deep learning network model and an expert discussion system;
and the updating module is used for updating the deep learning network model on line according to the evaluation result.
Preferably, the building block comprises:
the device comprises a determining unit, a judging unit and a judging unit, wherein the determining unit is used for taking the machine type communication as an evaluation object and dividing the machine type communication into power distribution equipment and a machine type communication network according to a self-defined machine type communication classification rule;
and the acquisition unit is used for acquiring the classified characteristic quantities of all the parts, selecting key characteristic quantities for evaluating the network security health index from the characteristic quantities and generating a characteristic space.
Further, the selecting unit includes: and the acquisition subunit is used for acquiring the key characteristic quantity through oil chromatographic analysis according to the dynamics of a network topology structure and member relationship, the unreliability of a wireless channel, the electric quantity, the storage space, the calculation and the communication capacity of the terminal node and the routing node.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (9)
1. A method for evaluating network security in machine type communication, the method comprising:
constructing a deep learning network model based on network security key characteristic quantity in predefined machine type communication;
the network safety and health state in machine type communication is comprehensively evaluated by combining a deep learning network model and an expert discussion system;
and updating the deep learning network model on line according to the evaluation result.
2. The method of claim 1, wherein the predefined network security critical feature quantities in machine type communication comprise: taking machine type communication as an evaluation object, and dividing the machine type communication into power distribution equipment and a machine type communication network according to a self-defined machine type communication classification rule; and acquiring the classified characteristic quantities of all parts, selecting key characteristic quantities for evaluating the network security and health index from the characteristic quantities, and generating a characteristic space.
3. The method of claim 2, wherein selecting key feature quantities for evaluating network security health indices comprises: and obtaining key characteristic quantity through oil chromatographic analysis according to the dynamics of a network topological structure and member relation, the unreliability of a wireless channel, the electric quantity, the storage space, the calculation and the communication capacity of the terminal node and the routing node.
4. The method of claim 1, wherein the deep learning network model is a deep learning network constructed based on a plurality of layers of extreme learning machines, and the construction method comprises the following steps: and mapping the key characteristic quantity as an input sample into a new characteristic space to form a training sample set X ═ Xi,ti},i＝1,...,n；
Wherein x isiWhich represents the input samples of the sample to be tested,tirepresenting the network security health index corresponding to the input sample, wherein n is the number of the samples;
randomly generating connection weights for an input layer to a first hidden layer
And an output matrix H of the first hidden layer1；
Calculating beta by least square method1The expression is as follows:
will be (beta)1)TReplacing the connection weight β of the input layer to the first hidden layer1Randomly generating the connection weight beta of the first hidden layer to the second hidden layer2And an output matrix H of the second hidden layer2；
Obtaining the output weight beta by the following formula calculation by adopting a least square method3：
In the formula, ajAnd bjAnd respectively representing an input weight value and a hidden layer threshold value which are randomly set and are associated with the jth hidden node, wherein z is the number of the hidden nodes, H is a generalized Jacobian matrix, and T is the total quantity of the network security health index corresponding to the sample.
5. The method of claim 1, wherein the comprehensive assessment of network security health in machine type communication in conjunction with a deep learning network model and an expert seminar system comprises; the newly generated sample x to be testednewRespectively inputting the expert discussion system and the deep learning network model, comparing the obtained output results, and judging whether the deep learning network model is consistent with the network security health index output by the expert discussion system; if the two models are consistent, the deep learning network model is good; if not, judging whether to execute the online updating of the deep learning network model according to the difference value; wherein,
the expert discussion system is used for online scoring of the health state of the machine type communication according to expert experience, relevant reference documents or experimental reports so as to achieve the acquisition of the safety and health index of the machine type communication network.
6. The method of claim 1 or 4, wherein the online updating of the deep learning network model according to the evaluation result comprises: if the difference value of the output results of the expert discussion system and the deep learning network model is larger than a preset threshold value, the network safety and health index output by the expert discussion system is used as a mark { x) of the current sample to be testednew,tnew}；
Will { xnew,tnewAs a new sample subset X to be testednewWhen the number accumulation of the samples to be tested exceeds a user-defined threshold value, the deep learning network model is updated on line through the following formula:
will be provided with
Substituted for beta1The expression is as follows:
mixing X with XnewInputting the updated deep learning network and respectively outputting the optimal matrix
The expression is as follows:
will be provided with
Substituted for beta3Completion of depthUpdating the learning network on line;
wherein I is an identity matrix, P is any one infinitely differentiable excitation function, P1＝(H1)TH1，
Respectively inputting the connection weight of the input layer to the first hidden layer, the connection weight of the first hidden layer to the second hidden layer and the output weight in the updated deep learning network model;
and outputting the matrix for the updated first hidden layer.
7. A system for evaluating network security in machine type communication, comprising:
the building module is used for building a deep learning network model based on the network security key characteristic quantity in the predefined machine type communication;
the evaluation module is used for comprehensively evaluating the network security and health state in the machine type communication by combining a deep learning network model and an expert discussion system;
and the updating module is used for updating the deep learning network model on line according to the evaluation result.
8. The system of claim 7, wherein the build module comprises:
the device comprises a determining unit, a judging unit and a judging unit, wherein the determining unit is used for taking the machine type communication as an evaluation object and dividing the machine type communication into power distribution equipment and a machine type communication network according to a self-defined machine type communication classification rule;
and the acquisition unit is used for acquiring the classified characteristic quantities of all the parts, selecting key characteristic quantities for evaluating the network security health index from the characteristic quantities and generating a characteristic space.
9. The system of claim 8, wherein the selecting unit comprises: and the acquisition subunit is used for acquiring the key characteristic quantity through oil chromatographic analysis according to the dynamics of a network topology structure and member relationship, the unreliability of a wireless channel, the electric quantity, the storage space, the calculation and the communication capacity of the terminal node and the routing node.
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