CN108650139A - A kind of powerline network monitoring system - Google Patents
A kind of powerline network monitoring system Download PDFInfo
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- CN108650139A CN108650139A CN201810481817.3A CN201810481817A CN108650139A CN 108650139 A CN108650139 A CN 108650139A CN 201810481817 A CN201810481817 A CN 201810481817A CN 108650139 A CN108650139 A CN 108650139A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
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- H—ELECTRICITY
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- H—ELECTRICITY
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- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
- H04L43/0817—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking functioning
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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Abstract
The present invention provides a kind of powerline network monitoring system, including：Data acquisition module includes the Multiple Source Sensor for acquiring different parameters, for obtaining the multi-data source data in powerline network；Data processing module obtains standardized data for collected multi-data source data to be extracted, checked and handled；Data memory module, in the standardized data storage to the file system of big data platform of acquisition；Data analysis module obtains the multidimensional Study on Trend result of powerline network equipment operation for carrying out analyzing processing to the standardized data；Data display module, for showing the analysis result.The present invention obtains the running state data of equipment and network in powerline network from various dimensions, carries out the analysis of various dimensions to collected status data using big data, comprehensive monitoring is carried out to system, improves stability and the safety of powerline network.
Description
Technical Field
The invention relates to the field of electric power, in particular to an electric power communication network monitoring system.
Background
At present, the power communication network is developed towards gridding, complication and distribution, the contact among the communication network nodes is more frequent, and the power equipment is continuously replaced along with the development of the technology. Resulting in an uninterrupted power communications network producing a huge amount of communications data. These data structures are complex, and have a large number of sources and data types. Meanwhile, the attack means suffered by the power communication network is also continuously improved, and the attack means is extended towards the direction of complication and distribution. The safe operation of power communication networks is subject to increasingly severe scrutiny.
In the existing power communication network management system, due to low automation and intelligence degrees, the situation of performance of each aspect in the power communication network cannot be accurately sensed and evaluated, so that the system cannot respond to the attack suffered by the network in real time. Therefore, in a large-scale power communication network, the real-time state and the derivation trend of the whole power communication network are monitored and managed, and the method has great significance for a decision maker to make decisions for various situations at the first time.
Disclosure of Invention
In view of the above problems, the present invention is directed to a power communication network monitoring system.
The purpose of the invention is realized by adopting the following technical scheme:
a power communication network monitoring system comprising: the data acquisition module comprises a multi-source sensor for acquiring different parameters and is used for acquiring multi-data-source data in the power communication network; the data processing module is used for extracting, checking and processing the acquired data of multiple data sources to acquire standardized data; the data storage module is used for storing the acquired standardized data into a file system of the big data platform; the data analysis module is used for analyzing and processing the standardized data to obtain a multidimensional situation analysis result of the operation of the power communication network equipment; and the data display module is used for displaying the analysis result.
Preferably, the multi-source sensor comprises a network sensor, a system state monitoring device and a device state monitoring device in an Intrusion Detection System (IDS), wherein the network sensor is used for acquiring scale data, operation condition data, network performance data and work quality data of the power communication network; the system state monitoring equipment is used for acquiring associated data among equipment in the power communication network; the equipment state monitoring equipment is used for acquiring the operation condition data of the equipment in the power communication network.
Preferably, the data display module further includes an alarm unit, configured to send out corresponding prompt information when the abnormal analysis result is obtained.
The invention has the beneficial effects that: according to the power communication network monitoring system provided by the invention, the running state data of equipment and a network in the power communication network is acquired from multiple dimensions, the acquired state data is subjected to multi-dimensional analysis by adopting big data, the system is monitored in an all-around manner, abnormal conditions in the system are found at the first time, a decision maker can make a response at the first time, and the stability and the safety of the power communication network are improved.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a block diagram of the frame of the present invention;
FIG. 2 is a block diagram of a data analysis module according to the present invention.
Reference numerals:
the system comprises a data acquisition module 1, a data processing module 2, a data storage module 3, a data analysis module 4, a data display module 5, a network security situation analysis unit 41 and a network security situation prediction unit 42
Detailed Description
The invention is further described in connection with the following application scenarios.
Referring to fig. 1, there is shown a power communication network monitoring system comprising: the data acquisition module 1 comprises a multi-source sensor for acquiring different parameters and is used for acquiring multi-data-source data in the power communication network; the data processing module 2 is used for extracting, checking and processing the acquired data of multiple data sources to acquire standardized data; the data storage module 3 is used for storing the acquired standardized data into a file system of a big data platform; the data analysis module 4 is used for analyzing and processing the standardized data to obtain a multidimensional situation analysis result of the operation of the power communication network equipment; and the data display module 5 is used for displaying the analysis result.
Preferably, the multi-source sensor comprises a network sensor, a system state monitoring device and a device state monitoring device in an Intrusion Detection System (IDS), wherein the network sensor is used for acquiring scale data, operation condition data, network performance data and work quality data of the power communication network; the system state monitoring equipment is used for acquiring associated data among equipment in the power communication network; the equipment state monitoring equipment is used for acquiring the operation condition data of the equipment in the power communication network.
Preferably, the data display module 5 further includes an alarm unit, configured to send out corresponding prompt information when the abnormal analysis result is obtained.
According to the embodiment of the invention, the operation state data of the equipment and the network in the power communication network are acquired from multiple dimensions, the acquired state data are subjected to multi-dimensional analysis by adopting big data, the system is monitored in an all-around way, abnormal conditions in the system are found at the first time, a decision maker can make a response at the first time, and the stability and the safety of the power communication network are improved.
Preferably, the data analysis module 4 includes a network security situation analysis unit 41 and a network security situation prediction unit 42; the network security situation analyzing unit 41 is configured to analyze a security situation of the power communication network and detect occurrence of a security event in the network; the network security situation prediction unit 42 is configured to predict a development trend of the network security situation based on the analysis result of the security situation and existing historical analysis data.
In the embodiment of the invention, the network security data acquired from the multi-source sensor is analyzed by the network security situation analyzing unit, so that whether a security event occurs in the network can be accurately judged in real time, and the whole power communication network is monitored; meanwhile, the network security situation prediction unit can predict the trend of the network security situation according to the result of the security situation analysis and historical analysis data, so that the system can be helped to early warn and make preparation measures for the predicted security events, and the security performance and reliability of the power communication network are improved.
Preferably, the network security situation analyzing unit 41 specifically includes:
and the information fusion layer performs data fusion on data sources collected by a plurality of sensors related to the same equipment and respectively acquires the probability that different threats have occurred, wherein the probability function of the occurrence of a single threat is as follows:
wherein,
wherein M (h, t) represents the probability of threat occurrence at time t, Mi(h) Andrespectively representing the probability of occurrence and non-occurrence of a threat, Mi(h, t) represents the probability that the ith data source detects the threat at the time t, and n represents the total number of the data sources;
and the equipment situation analysis layer acquires the security situation value of each equipment by combining the probability of the threat, wherein the adopted equipment security situation value function is as follows:
where SH represents a security posture value of the device, n represents a total number of threats to the device, and MiRepresenting the probability of occurrence of the ith threat, RiRepresenting the severity degree of the threat detected by the ith data source, wherein the severity degree is provided by a user manual prestored by the system, the user manual divides the threat severity degree into three levels of high, medium and low according to the classification of the threat and the caused consequence, and respectively recording R-3, R-2, R-1, and n represents the total number of the devices which are threatened;
the network situation analysis layer acquires a security situation value of the network system, wherein the adopted network system security situation value function is as follows:
wherein,
w＝fhNh+fmNm+flNl
wherein SA represents the security situation value of the network system, n represents the total number of all devices in the network, and wiRepresenting the weight of the ith device in the network, fh，fm，flThe importance degrees of the services provided by the equipment are respectively expressed as high, middle and low quantitative scores, Nh，Nm，NlRepresents the number of high, medium and low grade services provided by the equipment, SHiTo representSecurity posture value of the ith device in the network.
According to the embodiment of the invention, the data acquired from the multiple sensors are fused by adopting the method, so that the threat occurrence probability is calculated more accurately; meanwhile, by establishing a hierarchical multi-source network security situation threat analysis model and calculating a network security situation value, quantitative estimation of a network security situation is realized, the judgment of the system on the network security situation threat is facilitated, the working efficiency of the system is improved, the accuracy and the continuity of the evaluation of the network security threat situation are improved, and the method is suitable for a large-scale power communication network system.
Preferably, the device situation analysis layer further includes: and acquiring a security situation value of the target equipment by combining the probability of the threat occurrence and the risk association degree between the equipment, wherein the adopted security situation value function is as follows:
wherein,
rjA＝max{F[XAp,Xjq]}
of formula (II) to SH'ARepresenting the security posture value of the target device A, n representing the total number of threats suffered by the target device, MiRepresenting the probability of occurrence of the ith threat, RiAnd representing the severity of the ith threat, wherein the severity is provided by a user manual prestored in the system, and the user manual divides the severity of the threat into three grades of high, medium and low according to the classification of the threat and the caused consequence, and respectively records that R is 3, R is 2, R is 1 and R isjARepresenting the risk relevance of the target device A and its associated device j, m representing the total number of associated devices of the target device A, F [ X ]Ap,Xjq]Representing the risk association degree of the p module in the target equipment A and the q module in the association equipment j, wherein the risk association degree is calculated by adopting Dijkstra algorithm, SHjRepresenting associated devices jA security posture value; p is 1,2, …, P denotes the number of modules on the target device a, and Q is 1,2, …, Q denotes the number of modules on the associated device j.
In the above embodiment of the present invention, in the actual implementation of the power communication network, based on the consideration of the performance of the device, one module or multiple modules are usually deployed in the same device, and the modules cooperate with each other to complete the service logic of the whole network system, and there is a certain calling relationship or dependency relationship between the devices in the network, if a certain module is seriously dependent on the input of another module or is corresponding to the input of another module, when the latter is attacked, the former cannot normally complete the task, and it can be said that there is a risk association between the two modules; therefore, when the safety situation value of the target equipment is calculated, the risk association of the equipment is taken as a basis for consideration, the situation that the equipment in the actual power communication network is associated with each other can be effectively adapted, the accuracy of the safety situation estimation of the equipment is improved, and a foundation is laid for a decision maker to make accurate judgment according to an analysis result.
Preferably, the network security situation prediction unit 42 specifically includes:
(1) constructing an SVM security situation prediction model;
(2) generating a security situation sample data set according to the acquired network security situation value and a time sequence method, and dividing the security situation sample data set into a training sample and a test sample, wherein the training sample is used for SVM training to obtain an initial security situation prediction model, and the test sample is used for detecting the prediction precision of the initial prediction model;
(3) performing parameter optimization processing on the SVM security situation prediction model, and training to generate a final security situation prediction model;
(4) and inputting the network security situation value acquired in real time into the final security situation prediction model to acquire a predicted network security situation value, and predicting the development trend of the network security situation according to historical data analysis.
Preferably, in the network security situation prediction unit 42, performing parameter optimization processing on the SVM security situation prediction model specifically includes:
(1) randomly constructing an initial population consisting of i particles, and performing initialization setting, wherein the initialization setting comprises the steps of setting population scale, iteration times and randomly giving initial particlesAnd initial velocity of particlesWherein each particle vector represents an SVM model corresponding to different SVM parameters, comprising: a penalty coefficient C, an insensitive loss coefficient epsilon and a kernel function width parameter sigma;
(2) determining an SVM model by using parameters corresponding to the particle vectors, testing a test sample set z by using the SVM model, and calculating an adaptive value S (x) of each model to reflect the popularization and prediction capability of the SVM model, wherein the adopted adaptive value function is as follows:
wherein S (x) represents an adaptation value of the model, zcDenotes the predicted value, z 'of the c-th sample'cC represents the measured value of the C sample, and C represents the number of the test samples in the test sample set;
(3) the obtained adaptive value S (x) and the self optimal value pbestMaking a comparison if S (x)<pbestReplacing the optimal value of the previous round with the new adaptive value, and replacing the particles of the previous round with new particles;
(4) the best adaptation value p of each particlebestBest fit value g to all particlesbestMaking a comparison if pbest<gbestUsing the best adaptation value of the particleReplacing the original global best fitness value while preserving the current state of the particle;
(5) and (3) judging whether the adaptive value or the iteration number meets the requirement, if not, carrying out a new round of calculation, moving the particles which are not in the stored state to generate new particles, returning to the step (2), if so, finishing the calculation, and outputting the optimal parameters of the SVM model according to the particles with the best adaptive value.
The SVM refers to a support vector machine, is a supervised learning model, and is generally used for pattern recognition, classification, and regression analysis, wherein important parameters of the SVM model include: a penalty coefficient representing tolerance to errors; an insensitive loss coefficient epsilon used for controlling the error range; and the kernel function width parameter sigma is used for controlling the radial action range of the kernel function in the SVM model.
According to the embodiment of the invention, the network security situation is predicted by adopting the method, the nonlinear fitting (prediction) model more suitable for the network security situation is trained by utilizing the mathematical advantages of the SVM model in processing nonlinear data, small sample data and the like, and the key parameters of the SVM model are determined by adopting the method, so that the accuracy and efficiency of the network security situation prediction model can be further improved, and the guarantee is provided for the monitoring system to predict the power communication network security situation.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be analyzed by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (6)
1. A power communication network monitoring system, comprising: the data acquisition module comprises a multi-source sensor for acquiring different parameters and is used for acquiring multi-data-source data in the power communication network; the data processing module is used for extracting, checking and processing the acquired data of multiple data sources to acquire standardized data; the data storage module is used for storing the acquired standardized data into a file system of the big data platform; the data analysis module is used for analyzing and processing the standardized data to obtain a multidimensional situation analysis result of the operation of the power communication network equipment; and the data display module is used for displaying the analysis result.
2. The power communication network monitoring system of claim 1, wherein the multi-source sensors comprise network sensors, system state monitoring devices and device state monitoring devices in an Intrusion Detection System (IDS), wherein the network sensors are used for acquiring power communication network scale data, operation condition data, network performance data and work quality data; the system state monitoring equipment is used for acquiring associated data among equipment in the power communication network; the equipment state monitoring equipment is used for acquiring the operation condition data of the equipment in the power communication network.
3. The power communication network monitoring system according to claim 1, wherein the data display module further comprises an alarm unit for sending out a corresponding prompt message when the abnormal analysis result is obtained.
4. The power communication network monitoring system according to claim 1, wherein the data analysis module comprises a network security situation analysis unit and a network security situation prediction unit; the network security situation analysis unit is used for analyzing the security situation of the power communication network and detecting the occurrence of security events in the network; and the network security situation prediction unit is used for predicting the development trend of the network security situation based on the analysis result of the security situation and the existing historical analysis data.
5. The power communication network monitoring system according to claim 4, wherein the network security situation prediction unit specifically includes:
(1) constructing an SVM security situation prediction model;
(2) generating a security situation sample data set according to the acquired network security situation value and a time sequence method, and dividing the security situation sample data set into a training sample and a test sample, wherein the training sample is used for SVM training to obtain an initial security situation prediction model, and the test sample is used for detecting the prediction precision of the initial prediction model;
(3) performing parameter optimization processing on the SVM security situation prediction model, and training to generate a final security situation prediction model;
(4) and inputting the network security situation value acquired in real time into the final security situation prediction model to acquire a predicted network security situation value, and predicting the development trend of the network security situation according to historical data analysis.
6. The power communication network monitoring system according to claim 5, wherein the network security situation prediction unit performs parameter optimization processing on the SVM security situation prediction model, and specifically comprises:
(1) randomly constructing an initial population consisting of i particles, and performing initialization setting, wherein the initialization setting comprises the steps of setting population scale, iteration times and randomly giving initial particlesAnd initial velocity of particlesWherein each particle vector represents an SVM model corresponding to different SVM parameters, comprising: a penalty coefficient C, an insensitive loss coefficient epsilon and a kernel function width parameter sigma;
(2) determining an SVM model by using parameters corresponding to the particle vectors, testing a test sample set z by using the SVM model, and calculating an adaptive value S (x) of each model to reflect the popularization and prediction capability of the SVM model, wherein the adopted adaptive value function is as follows:
wherein S (x) represents an adaptation value of the model,zcdenotes the predicted value, z 'of the c-th sample'cC represents the measured value of the C sample, and C represents the number of the test samples in the test sample set;
(3) the obtained adaptive value S (x) and the self optimal value pbestMaking a comparison if S (x)<pbestReplacing the optimal value of the previous round with the new adaptive value, and replacing the particles of the previous round with new particles;
(4) the best adaptation value p of each particlebestBest fit value g to all particlesbestMaking a comparison if pbest<gbestReplacing the original global best adaptation value with the best adaptation value of the particle, and simultaneously saving the current state of the particle;
(5) and (3) judging whether the adaptive value or the iteration number meets the requirement, if not, carrying out a new round of calculation, moving the particles which are not in the stored state to generate new particles, returning to the step (2), if so, finishing the calculation, and outputting the optimal parameters of the SVM model according to the particles with the best adaptive value.
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