CN106096789A - A kind of based on machine learning techniques can be from the abnormal industry control security protection of perception and warning system - Google Patents
A kind of based on machine learning techniques can be from the abnormal industry control security protection of perception and warning system Download PDFInfo
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Abstract
The invention discloses a kind of based on machine learning techniques can be from the abnormal industry control security protection of perception and warning system; it includes data acquisition and pretreatment module, data storage cell, abnormality detecting unit, operating unit; wherein data storage cell includes locally stored module and network storage module; abnormality detecting unit includes prediction module, analyzes module, judge module and decision-making module, and operating unit includes alarm module and protection module；This system is arranged in the industrial control system needing monitoring, after gathering signal sends between industrial control system data and carrying out pretreatment, abnormity based on machine learning framework to described data detects, when there being the abnormal situation in routine operation to occur, automatically send and report to the police or produce relevant refusal instruction.
Description
Technical field
The present invention relates to a kind of industry control security protection based on machine learning techniques and warning and system, belong to industry control safety
Technical field.
Background technology
Industrial control system is to use the technology such as control theory, computer science, instrument and meter, various to production process
Information gathering, analyze, process, and carry out optimal control and reasonably scheduling, management, to reach to improve a kind of control of production efficiency
System processed.Industrial control system is segmented into safely three aspects, i.e. functional safety, physical security and information security.Wherein merit
Can be safely to reach equipment and factory safety function, shielded and control equipment security related components must be correct
Perform its function, and when losing efficacy or fault occurs, equipment or system must remain to keep safety condition or enter into safety
State.The data that we can send by gathering the signal between industrial control system carry out abnormality detection, pass through historical pattern
Data exception is judged with model prediction.
Summary of the invention
The present invention provide a kind of based on machine learning techniques can from the abnormal industry control security protection of perception and warning system,
It sends based on machine learning techniques, the signal between monitor control system.When there being the situation differing from routine operation to occur, from
Movable property is raw reports to the police or the equipment refusal instruction that protection is relevant.
This system, comprising:
Data acquisition and pretreatment module, the signal between monitoring control system sends, between acquisition control system
The data that signal sends, and these data are processed into the form of the signal stream of reaction signal sending direction and order, by described
Signal streaming enters abnormality detecting unit；
Abnormality detecting unit, is used for receiving corresponding signal stream, and carries out described signal stream based on machine learning techniques
Abnormity detects, it may be judged whether have the abnormal situation in routine operation to occur；
Data storage cell, is used for storing historical signal flow database sample；
Operating unit, for the conclusion drawn according to abnormality detecting unit, makes normal operating, sends warning or produce
The operation of relevant refusal instruction.
Wherein, described abnormality detecting unit includes prediction module, analyzes module, judge module and decision-making module.
Wherein, described operating unit includes:
Alarm module, when receiving the alarm signal that decision-making module sends, described alarm module can automatically generate warning；
Protection module, when receiving the protection signal that decision-making module sends, described protection module can send refusal operation
Instruction, make control system cannot be carried out associative operation.
Wherein, described prediction module is in the case of hypothesis is N/R, it is provided that the actuarial prediction of signal flow data set
Data.
Wherein, described analysis module is for receiving the actuarial prediction data that prediction module draws and pre-according to described statistics
Survey the signal flow data next time that data estimation obtains from data acquisition and pretreatment module.
Wherein, described judge module is for the predictive value of the flow data set of signal next time according to the estimation of described analysis module
With the actual value of the flow data of signal next time that described data acquisition and pretreatment module obtain, to described signal fluxion next time
Judge according to the abnormity of collection.
Wherein, described decision-making module is for receiving the abnormity judged result that described judge module is made, according to described different
Perseverance judged result is made normal operating, is sent the decision-making of the operation reporting to the police or producing relevant refusal instruction, and by decision-making
Result is sent to operating unit, according to described abnormity judged result, described prediction module is made renewal simultaneously, this is believed
Number flow data set is sent to data storage cell to update historical signal flow database sample.
Wherein, described data storage cell includes:
Locally stored module, for being stored in signal flow data set and sample data in home server main frame；
Network storage module, for taking the mirror image copies regular update of the historical data in home server main frame at cloud
In business device, it is to avoid because home server main frame damages the loss caused.
The beneficial effect comprise that
1, by machine learning techniques, signal transmission between control system is carried out abnormality detection, thus provide and assuming nothing
Under abnormal conditions, the statistical distribution prediction of signal stream mode, improves the anomalous identification rate of industrial control system, has saved a large amount of simultaneously
Manpower.
2, described industry control security protection can be abnormal from perception with warning system, and after noting abnormalities, make and automatically generating
Report to the police or the operation of the equipment refusal instruction that protection is relevant.
3, the network storage module in data storage cell can be secondary by the mirror image of the historical data in home server main frame
This regular update is in Cloud Server, it is to avoid because server host damages the loss caused.
Accompanying drawing explanation
By reading the detailed description of hereafter preferred implementation, various other advantage and benefit common for this area
Technical staff will be clear from understanding.Accompanying drawing is only used for illustrating the purpose of preferred implementation, and is not considered as the present invention
Restriction.And in whole accompanying drawing, it is denoted by the same reference numerals identical parts.In the accompanying drawings:
Fig. 1 is that the present invention is based on machine learning techniques can be from the framework of the industry control security protection of perception Yu warning system
Figure.
Fig. 2 is detecting step schematic block diagram in the present invention.
Fig. 3 is the abnormity determination methods flow chart of signal flow data set in the present invention.
Fig. 4 is the step schematic block diagram that in the present invention, decision-making module carries out decision-making.
Detailed description of the invention
It is more fully described the illustrative embodiments of the disclosure below with reference to accompanying drawings.Although accompanying drawing shows these public affairs
The illustrative embodiments opened, it being understood, however, that may be realized in various forms the disclosure and the reality that should not illustrated here
The mode of executing is limited.On the contrary, it is provided that these embodiments are able to be best understood from the disclosure, and can be by these public affairs
What the scope opened was complete conveys to those skilled in the art.
As it is shown in figure 1, the present invention disclose a kind of based on machine learning techniques can be from the abnormal industry control security protection of perception
With warning system, comprising: data acquisition and pretreatment module, the signal between monitoring control system sends, and gathers control
The data that between system processed, signal sends, and these data are processed into the shape of reaction signal sending direction and the signal stream of order
Formula, is sent to abnormality detecting unit, is used for detecting exception；Abnormality detecting unit, is used for receiving corresponding signal flow data, and
Described data are carried out abnormity detection based on machine learning framework, draws and whether have the abnormal situation in routine operation to occur
Conclusion；Data storage cell, is used for storing historical signal flow database sample；Operating unit, for according to abnormality detecting unit
The conclusion drawn, makes normal operating, sends the operation reporting to the police or producing relevant refusal instruction.
As in figure 2 it is shown, the described based on machine learning techniques of present invention proposition can be anti-from the industry control safety that perception is abnormal
Protecting with warning system, detecting step includes: 1, obtain data and it is carried out pretreatment；2, data it is analyzed and sentences
Disconnected；3, whether decision control system has exception；4, the result of decision is sent to operating unit and carries out associative operation.
The described based on machine learning techniques of present invention proposition with reporting to the police can be from the industry control security protection that perception is abnormal
In system, described abnormality detecting unit includes: prediction module, for providing in the case of hypothesis is N/R, and signal flow data set
Prediction data；Analyze module, for receiving the actuarial prediction data that prediction module draws, and estimate according to described prediction data
The signal flow data next time obtained from data acquisition and pretreatment module；Judge module, for according to signal fluxion next time
According to predictive value and the actual value of collection, the abnormity of the described flow data set of signal next time is judged；Decision-making module, is used for connecing
Receive the abnormity judged result made of judge module, according to described abnormity judged result make normal operating, send warning or
It is the decision-making of the operation producing relevant refusal instruction, and the result of decision is sent to operating unit, simultaneously according to described exception
Described prediction module is made renewal by property judged result, and this signal flow data set is sent to data storage cell with the more new calendar
History signal flow database sample.
As it is shown on figure 3, the described based on machine learning techniques of present invention proposition can be anti-from the industry control safety that perception is abnormal
Protecting with warning system, the method that the abnormity of the described flow data set of signal next time is judged by described judge module includes
Following steps:
3.1, the reality of described predictive value and the flow data of signal next time obtained from data acquisition is compared with pretreatment module
Actual value, draws both difference DELTA；
3.2, obtain from data storage cell and measured signal flow data has identical time step and the system operation back of the body
The historical data base sample of scape, calculates the standard deviation of this sample；
3.3, described difference and standard deviation scope are compared:
If difference DELTA is not within the scope of [-δ, δ], calculate deviation value P, wherein P=| | Δ |-δ |, it is judged that module will
Deviation value P is sent to decision-making module；If difference DELTA is within the scope of [-δ, δ], it is judged that module sends one to decision-making module
Certainly signal, determines situation without exception.
As shown in Figure 4, the described based on machine learning techniques of present invention proposition can be anti-from the industry control safety that perception is abnormal
Protecting with warning system, described decision-making module carries out the method for decision-making and comprises the following steps:
4.1, decision-making module receives the data that judge module sends, if data are deviation value P, then carries out step 4.2, if number
According to for a certainly signal, then carry out step 4.3；
4.2, decision-making module receives deviation value P, will deviate from value P and the decision content λ set compares, if deviation value P
Less than decision content λ, decision-making module sends alarm signal to the alarm module of operating unit；If deviation value P is more than or equal to decision content
λ, decision-making module then sends protection signal to the protection module of operating unit.Meanwhile, decision-making module is the most more
Newly this signal flow data sample, and it is labeled as anomalous event；
4.3, decision-making module receives signal certainly, then by corresponding judge module and the sample number in data storage cell
According to being updated, for detection signal flow data next time.
The described based on machine learning techniques of present invention proposition with reporting to the police can be from the industry control security protection that perception is abnormal
In system, described data storage cell includes: locally stored module, for locally stored signal flow data set, sample data, by institute
State data to be stored in home server main frame；Network storage module, for by the historical data in home server main frame
Mirror image copies regular update is in Cloud Server, it is to avoid because server host damages the loss caused.
The described based on machine learning techniques of present invention proposition with reporting to the police can be from the industry control security protection that perception is abnormal
In system, described operating unit includes: alarm module, when receiving the alarm signal that decision-making module sends, and described alarm module
Warning can be automatically generated；Protection module, when receiving the protection signal that decision-making module sends, described protection module can send refuses
The instruction of operation, makes control system cannot be carried out associative operation absolutely.
The present invention with tradition industry control security protection compared with warning system, by machine learning techniques to control system between
Signal sends and carries out abnormality detection, thus provides the statistical distribution prediction of signal stream mode in the case of hypothesis is without exception, improves
The anomalous identification rate of industrial control system, has saved substantial amounts of manpower simultaneously.
The present invention is with tradition industry control security protection compared with warning system, and described industry control security protection can be certainly with warning system
Perception is abnormal, and after noting abnormalities, makes automatically generating and report to the police or the operation of the equipment refusal instruction that protection is relevant.
The present invention with tradition industry control security protection compared with warning system, by the network storage mould in data storage cell
Block can be by the mirror image copies regular update of the historical data in home server main frame in Cloud Server, it is to avoid because of server master
Machine damages the loss caused.
The above, the only present invention preferably detailed description of the invention, but protection scope of the present invention is not limited thereto,
Any those familiar with the art in the technical scope that the invention discloses, the change that can readily occur in or replacement,
All should contain within protection scope of the present invention.Therefore, protection scope of the present invention answers the described protection model with claim
Enclose and be as the criterion.
Claims (8)
1. an industry control security protection based on machine learning techniques and warning system, it is characterised in that comprising:
Data acquisition and pretreatment module, the signal between monitoring control system sends, signal between acquisition control system
The data sent, and these data are processed into the form of the signal stream of reaction signal sending direction and order, by described signal
Streaming enters abnormality detecting unit；
Abnormality detecting unit, is used for receiving corresponding signal stream, and based on machine learning techniques, described signal stream is carried out exception
Property detection, it may be judged whether have the abnormal situation in routine operation to occur；
Data storage cell, is used for storing historical signal flow database sample；
Operating unit, for the conclusion drawn according to abnormality detecting unit, makes normal operating, sends warning or produce relevant
Refusal instruction operation.
Industry control security protection based on machine learning techniques the most according to claim 1 and warning system, it is characterised in that
Described abnormality detecting unit includes prediction module, analyzes module, judge module and decision-making module.
Industry control security protection based on machine learning techniques the most according to claim 2 and warning system, it is characterised in that
Described operating unit includes:
Alarm module, when receiving the alarm signal that decision-making module sends, described alarm module can automatically generate warning；
Protection module, when receiving the protection signal that decision-making module sends, described protection module can send the finger of refusal operation
Order, makes control system cannot be carried out associative operation.
Industry control security protection based on machine learning techniques the most according to claim 2 and warning system, it is characterised in that
Described prediction module is in the case of hypothesis is N/R, it is provided that the actuarial prediction data of signal flow data set.
Industry control security protection based on machine learning techniques the most according to claim 4 and warning system, it is characterised in that
Described analysis module is used for receiving the actuarial prediction data that prediction module draws, and according to described actuarial prediction data estimation from number
According to the signal flow data next time gathered and pretreatment module obtains.
Industry control security protection based on machine learning techniques the most according to claim 5 and warning system, it is characterised in that
Described judge module is used for the predictive value of the flow data set of signal next time according to the estimation of described analysis module and described data acquisition
The actual value of the flow data of signal next time that collection and pretreatment module obtain, the abnormity to the described flow data set of signal next time
Judge.
Industry control security protection based on machine learning techniques the most according to claim 3 and warning system, it is characterised in that
Described decision-making module, for receiving the abnormity judged result that described judge module is made, is made according to described abnormity judged result
Go out normal operating, send the decision-making reporting to the police or producing the operation that relevant refusal instructs, and the result of decision is sent to operation
Unit, makes renewal according to described abnormity judged result to described prediction module simultaneously, this signal flow data set is sent
To data storage cell to update historical signal flow database sample.
Industry control security protection based on machine learning techniques the most according to claim 1 and warning system, it is characterised in that
Described data storage cell includes:
Locally stored module, for being stored in signal flow data set and sample data in home server main frame；
Network storage module, is used for the mirror image copies regular update of the historical data in home server main frame at Cloud Server
In, it is to avoid because home server main frame damages the loss caused.
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Cited By (9)
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| CN109099511A (en) * | 2017-06-21 | 2018-12-28 | 广西大学行健文理学院 | A kind of indoor air-purification device and its control method |
| CN109189024A (en) * | 2018-09-27 | 2019-01-11 | 鲁班嫡系机器人（深圳）有限公司 | A kind of industrial automation system including monitoring unit, factory and monitoring method |
| DE202019102856U1 (en) | 2019-05-21 | 2019-06-04 | Abb Schweiz Ag | Device for processing alarm messages |
| CN109983412A (en) * | 2016-12-14 | 2019-07-05 | 欧姆龙株式会社 | Control device, control program and control method |
| CN110730156A (en) * | 2018-07-17 | 2020-01-24 | 国际商业机器公司 | Distributed machine learning for anomaly detection |
| US10558863B2 (en) | 2017-07-19 | 2020-02-11 | Pegatron Corporation | Video surveillance system and video surveillance method |
| CN111078757A (en) * | 2019-12-19 | 2020-04-28 | 武汉极意网络科技有限公司 | Autonomous learning business wind control rule engine system and risk assessment method |
| CN112559803A (en) * | 2020-07-08 | 2021-03-26 | 北京德风新征程科技有限公司 | Data anomaly detection method and system based on iteration |
| CN113377806A (en) * | 2021-06-18 | 2021-09-10 | 南通大学 | Embedded machine learning artificial intelligence data analysis processing system |
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| CN113377806A (en) * | 2021-06-18 | 2021-09-10 | 南通大学 | Embedded machine learning artificial intelligence data analysis processing system |
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Application publication date: 20161109 |