CN108920939A - Information security method of discrimination, system and relevant apparatus based on Learner diagnosis device - Google Patents
Information security method of discrimination, system and relevant apparatus based on Learner diagnosis device Download PDFInfo
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Abstract
This application discloses a kind of information security method of discrimination based on Learner diagnosis device, applied to incomplete discrete event system, by introducing there is the Learner diagnosis device of learning ability and candidate state algorithm to handle incomplete models, it is simulated by using that can not only be shifted to the state of hypothesis in candidate state algorithm, and the status information of systematic absence can be passed through under the learning functionality of Learner diagnosis device and continuously attempt to be restored, the precondition of proofing state opacity is complied in such a way that incomplete models are converted to complete model, and then differentiate whether the incomplete discrete event system successfully maintains secrecy to some information realizations, the application field of Learner diagnosis device is widened to this field of proofing state opacity simultaneously.The application further simultaneously discloses a kind of information security judgement system, device and computer readable storage medium based on Learner diagnosis device, has above-mentioned beneficial effect.
Description
Technical field
This application involves information privacy technical field, in particular to a kind of information security differentiation side based on Learner diagnosis device
Method, system, device and computer readable storage medium.
Background technique
Discrete event system (Discrete Event System, DES) is by discrete event according to certain operation rule
A kind of dynamical system of state evolution is interacted and causes, it had not only been used directly for the modeling to discrete system, but also can be with
For to the system modelling after discrete model construction.Currently, discrete event system is in military and national defense, traffic control, computer
There is successful application in the fields such as integrated manufacturing system, electronic communications network (ECN), robot technology.
With being growing for commercial production scale, automation equipment is increasingly enlarged, and system structure also becomes increasingly
Complexity, once the security risk attacked occurs in industrial system, the normal operation of meeting extreme influence system is especially pacified in information
Full field, to the safe and secret particularly important of data.And the opacity in discrete event system has succeeded in digital signature, has protected
It is widely applied in the Information Security Mechanisms such as close communication, authentification of message, intrusion detection, data encryption.And there is state opacity
Discrete event system be required to meet it is claimed below：The path of system secret state is reached for any one, is at least existed another
One reaches the path of non-secret state from same state, so that this two paths projection having the same.
Existing carry out state opacity analysis is all built upon complete discrete event system, and (there is no necessary status informations
Missing) on the basis of, if a complete discrete event system through differentiate have state opacity, it may be considered that it meets
Information privacy requires and (has successfully hidden some information for being not desired to be spied upon by other people), but due to getting a discrete event
Can't often determine whether it complete when system, once and the discrete event system be it is incomplete, be not just available application
In the state opacity discriminant approach of complete discrete event system, if using identical discriminant approach by force, what is obtained sentences
The considerations of other result is also not have reference value, and prior art missing is to such situation.
Therefore, the skill of state opacity analysis can not be carried out to incomplete discrete event system by how filling up the prior art
Art blank is those skilled in the art's urgent problem to be solved.
Summary of the invention
The purpose of the application is to provide a kind of information security method of discrimination based on Learner diagnosis device, be applied to it is incomplete from
Dissipate event system, by introduce have learning ability Learner diagnosis device and candidate state algorithm to incomplete models at
Reason is simulated by using that can not only shift to the state of hypothesis in candidate state algorithm, and can examined in study
The status information of systematic absence is converted to incomplete models by continuously attempting to be restored under the learning functionality of disconnected device
The mode of complete model complies with the precondition of proofing state opacity, and then differentiates the incomplete discrete event system
Whether successfully maintain secrecy to some information realizations, while the application field of Learner diagnosis device being widened to proofing state opacity
This field.
The another object of the application be the provision of a kind of information security judgement system based on Learner diagnosis device, device and
Computer readable storage medium.
To achieve the above object, the information security method of discrimination method based on Learner diagnosis device that this application provides a kind of,
Applied to incomplete discrete event system, this method includes：
Incomplete models are generated according to incomplete discrete event system；Wherein, it is wrapped in the incomplete discrete event system
Status information containing need for confidentiality；
The incomplete models are handled using Learner diagnosis device and candidate state algorithm, obtain complete model deficiency
Information；
Complete model is constructed again according to the incomplete models and the complete model deficiency information generation；
Verifying is described to construct whether the corresponding discrete event system of complete model has state opacity again；
When the discrete event system has state opacity, determine that the status information is in confidential state.
Optionally, the incomplete models are handled using Learner diagnosis device and candidate state algorithm, is obtained complete
Model deficiency information, including：
The Learner diagnosis device is constructed according to the status information of incomplete models output；
Information update is carried out using the candidate state algorithm in each interval between diagnosis of the Learner diagnosis device, is obtained
To the complete model deficiency information.
Optionally, information is carried out using the candidate state algorithm in each interval between diagnosis of the Learner diagnosis device
It updates, including：
When the Learner diagnosis device diagnoses discovery existence deficient phenomena, to including in the candidate state algorithm
State assume collection execute amendment operation.
Optionally, complete model is constructed again according to the incomplete models and the complete model deficiency information generation, wrap
It includes：
By the Learner diagnosis device the complete model deficiency information that each interval between diagnosis obtains successively be integrated in it is described not
On complete model, obtain described constructing complete model again.
Optionally, construct whether the corresponding discrete event system of complete model has state opacity again verifying is described
Before, further include：
Compare true complete model and construct complete model again with described, obtains comparison result；Wherein, the true complete mould
Type is generated by true complete discrete event system, and the incomplete discrete event system is that the true complete discrete system is lost
It is obtained after losing a part of status information；
It is adjusted correspondingly according to Diagnostic parameters of the comparison result to the Learner diagnosis device.
To achieve the above object, it present invention also provides a kind of information security judgement system based on Learner diagnosis device, answers
For incomplete discrete event system, which includes：
Incomplete models generation unit, for generating incomplete models according to incomplete discrete event system；Wherein, described
It include the status information of need for confidentiality in incomplete discrete event system；
Missing information recovery unit, for being carried out using Learner diagnosis device and candidate state algorithm to the incomplete models
Processing, obtains complete model deficiency information；
Complete remodeling builds unit, for being generated again according to the incomplete models and the complete model deficiency information
Construct complete model；
Whether state opacity authentication unit described constructs the corresponding discrete event system of complete model for verifying again
With state opacity；
Secrecy situation judging unit, for determining the shape when the discrete event system has state opacity
State information is in confidential state.
Optionally, the missing information recovery unit includes：
Learner diagnosis device constructs subelement, and the status information for being exported according to the incomplete models constructs the study
Diagnostor；
Candidate state algorithm information updates subelement, for utilizing in each interval between diagnosis of the Learner diagnosis device
The candidate state algorithm carries out information update, obtains the complete model deficiency information.
Optionally, the candidate state algorithm information update subelement includes：
State assumes collection correction module, is used for when the Learner diagnosis device diagnoses discovery existence deficient phenomena,
Collection, which executes amendment operation, to be assumed to the state for including in the candidate state algorithm.
Optionally, the complete remodeling builds unit and includes：
By the Learner diagnosis device the complete model deficiency information that each interval between diagnosis obtains successively be integrated in it is described not
On complete model, obtain described constructing complete model again.
Optionally, which further includes：
Comparing unit, for constructing whether the corresponding discrete event system of complete model has state not again verifying is described
Before the transparency, relatively truer complete model constructs complete model with described again, obtains comparison result；Wherein, described true complete
Standby model is generated by true complete discrete event system, and the incomplete discrete event system is the true complete discrete system
System obtains after losing a part of status information；
Adjustment unit, for being adjusted accordingly according to Diagnostic parameters of the comparison result to the Learner diagnosis device
It is whole.
To achieve the above object, present invention also provides a kind of information security discriminating gears based on Learner diagnosis device, should
Device includes：
Memory, for storing computer program；
Processor is realized as described in above content when for executing the computer program based on Learner diagnosis device
The step of information security method of discrimination.
To achieve the above object, described computer-readable to deposit present invention also provides a kind of computer readable storage medium
It is stored with computer program on storage media, the base as described in above content is realized when the computer program is executed by processor
In the information security method of discrimination of Learner diagnosis device the step of.
Obviously, technical solution provided herein, by introducing the Learner diagnosis device with learning ability and candidate shape
State algorithm handles incomplete models, is carried out by using that can not only shift to the state of hypothesis in candidate state algorithm
Simulation, and can be extensive by continuously attempting to obtain by the status information of systematic absence under the learning functionality of Learner diagnosis device
It is multiple, the precondition of proofing state opacity is complied in such a way that incomplete models are converted to complete model, in turn
Differentiate whether the incomplete discrete event system successfully maintains secrecy to some information realizations, while the application of Learner diagnosis device being led
It widens to this field of proofing state opacity in domain.The application additionally provides a kind of information peace based on Learner diagnosis device simultaneously
Full judgement system, device and computer readable storage medium, have above-mentioned beneficial effect, details are not described herein.
Detailed description of the invention
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of application for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is a kind of flow chart of the information security method of discrimination based on Learner diagnosis device provided by the embodiments of the present application；
Fig. 2 is the process of another information security method of discrimination based on Learner diagnosis device provided by the embodiments of the present application
Figure；
Fig. 3 is a kind of structural schematic diagram of discrete event system；
Fig. 4 is a kind of steel product testing process schematic diagram provided by the embodiments of the present application；
Fig. 5 is that a kind of structure of the corresponding discrete event system of steel product testing process provided by the embodiments of the present application is shown
It is intended to；
Fig. 6 is a kind of diagnostic analysis schematic diagram of discrete event system model provided by the embodiments of the present application；
Fig. 7 is a kind of Learner diagnosis flow diagram provided by the embodiments of the present application；
Fig. 8 is a kind of structural frames of the information security judgement system based on Learner diagnosis device provided by the embodiment of the present application
Figure.
Specific embodiment
The core of the application is to provide a kind of information security method of discrimination based on Learner diagnosis device, system, device and can
Read storage medium, by introduce have learning ability Learner diagnosis device and candidate state algorithm to incomplete models at
Reason is simulated by using that can not only shift to the state of hypothesis in candidate state algorithm, and can examined in study
The status information of systematic absence is converted to incomplete models by continuously attempting to be restored under the learning functionality of disconnected device
The mode of complete model complies with the precondition of proofing state opacity, and then differentiates the incomplete discrete event system
Whether successfully maintain secrecy to some information realizations, while the application field of Learner diagnosis device being widened to proofing state opacity
This field.
To keep the purposes, technical schemes and advantages of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application
In attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is
Some embodiments of the present application, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art
All other embodiment obtained without making creative work, shall fall in the protection scope of this application.
The some nouns and technical background of appearance are explained to subsequent herein here, to understand subsequent content：
One discrete event system can be modeled as finite-state automata G=(X, Σ, δ, an x0, Y, λ), wherein X
For finite state collection；Σ is limited event set, and Σ is typically divided into observable event set Σ hereoNot observable event set
Σuo, i.e. Σ=Σo∪Σuo, wherein event of failure collection ΣfIn event belong to inconsiderable event；δ is transfer function δ:X×
Σ→2X；x0For original state；Y is output collection；λ is output mapping (output map) λ:X→Y；The condition of system is indicated with K
Collect (condition set), i.e. K:={ N, F1,F2,…,Fm}.Meanwhile state set X is carried out according to the condition set of system corresponding
ClassificationWherein symbolIndicate mutually disjoint union.
Complete discrete event system G=(X, Σ, δ, x0, Y, λ) and judge that the opacity of system current state can formalize
It is as follows：IfIt is secrecy collection,It is non-secrecy collection, P is projection, and L is the generation language of system G
Speech, if meeting condition：
The opacity for then claiming G that there is current state.
Intuitively, there is system G current state opacity to show：For reaching each path t of secrecy (i.e.), all there is another and reaches the path t'(of non-secret state i.e., so that this two roads
Diameter projection (i.e. P (t)=P (t')) having the same.Obviously, the system G with current state opacity can make invader
It can not determine whether the current state of system is in secrecy Xs。
It is illustrated below by an example：
Given discrete event system G=(X, Σ, δ, x0, Y, λ) as shown in Figure 3, it is assumed that secrecy integrates as Xs={ 3 }, it is non-
Secrecy integrates as Xns={ 0,1,2,4 }.If Σo={ b } (i.e. event b be considerable event, and event a and event c be can not
Sight event), then G has the opacity of current state, because reaching the path of secrecy and reaching the road of non-secret state
Diameter projects identical, i.e. P (ab)=P (bc)=P (b).But if Σo={ a, b }, then G does not have the opacity of current state,
Because the path for the reaching secrecy state path projection result non-secret with arrival be not identical, i.e. P (ab) ≠ P (bc).
If the phenomenon that state 2 in Fig. 3 lacks, judging discrete event system in above-mentioned research process
The conditions for diagnostics of state opacity can change, to obtain the conclusion of opacity of the system G without current state.
The famous cybernetist Raymond professor of University of Toronto automatically controlled top international periodical in 2011
Object《IEEE Transactions on Automatic Control》On delivered about incomplete discrete event system therefore
Hinder diagnosis research achievement, proposes in a creative way a kind of by constructing the diagnostor realization with self-teaching function to discrete thing
The fault diagnosis of part system, it can ensure that system according to the difference of reality output and prediction output, mentions in incomplete models
A kind of method for diagnosing faults is gone out.
Embodiment one
Below in conjunction with Fig. 1, Fig. 1 is a kind of information security differentiation side based on Learner diagnosis device provided by the embodiments of the present application
The flow chart of method, specifically includes following steps：
S101：Incomplete models are generated according to incomplete discrete event system；
Incomplete discrete event system is configured to an incomplete models first with finite-state automata by this step
(being equal to the process for constructing obtained discrete event system G above), and comprising in need in the incomplete discrete event system
The status information of secrecy, that is to say, that the status information of this part need for confidentiality needs to prevent other people from spying upon.
S102：Incomplete models are handled using Learner diagnosis device and candidate state algorithm, complete model is obtained and lacks
It breaks one's promise breath；
On the basis of S101, this step be intended to by introduce it is former be applied to fault diagnosis field with learning ability
Learner diagnosis device and candidate state algorithm attempt to restore the transinformation of the status information lacked in incomplete models.
Wherein, the Learner diagnosis device with learning ability is the above-described diagnostor proposed by professor Raymond,
The Learner diagnosis device original be applied to fault diagnosis field, by ensure system according to reality output and prediction output difference come into
Row fault diagnosis, the application make it through by the learning ability of the Learner diagnosis device and carry out mould to lack part status information
Quasi- obtained all hypothesis transfer is continuously attempted to, and combines the status information arrived comprising all available in the incomplete models
With the candidate state algorithm comprising all hypothesis collection for assuming transfer to each interval between diagnosis (usual situation of the Learner diagnosis device
One state of lower diagnosis is just divided into an interval between diagnosis) it is diagnosed, and the process diagnosed is actually namely constantly tasted
It tries and will think that satisfactory state retains with process to be restored in cut-and-try process, it is more numerous to implement step
It is trivial, subsequent embodiment is referred to the detailed description of this part.
S103：Complete model is constructed again according to incomplete models and the generation of complete model deficiency information；
On the basis of S102, complete model deficiency information and be originally present not that this step is intended to be obtained according to processing
Complete model is integrated, and is obtained one with building and is constructed complete model again by what missing information restored.
It under normal conditions, can be by this step in the corresponding true complete discrete event system of the unknown incomplete models
It constructs the obtained corresponding discrete event system of complete model that constructs again and regards as complete discrete event system, but is complete by one
When status information progress artificial removal in standby discrete event system manufactures to obtain an incomplete discrete event system, by this
Apply the obtained incomplete discrete event system of manufacture is carried out to restore and the discrete event system that constructs again often still
It is had differences with true complete discrete event system, therefore in order to further enhance the recovery accuracy of miss status information,
Can be with a certain number of incomplete discrete event systems of artificial manufacture early period, and will be obtained through reset mode provided by the present application
To the corresponding discrete event system of complete model that constructs again be compared with true complete discrete event system, it is poor with determination
Different and difference Producing reason, carrys out the setting of regularized learning algorithm diagnostor and parameter and candidate state algorithm, restores quasi- to be promoted
True property.
A kind of implementation for including but not limiting as：
Compare true complete model and construct complete model again, obtains comparison result；Wherein, true complete model is by true
Complete discrete event system generates, and incomplete discrete event system is that true complete discrete system loses a part of status information
After obtain；
It is adjusted correspondingly according to Diagnostic parameters of the comparison result to Learner diagnosis device.
S104：Verifying constructs whether the corresponding discrete event system of complete model has state opacity again；
On the basis of S103, this step is intended to carry out the analysis of state opacity to the obtained complete model that constructs again,
Obtain analysis result.Due to constructing again by S103, it is believed that this constructs the corresponding discrete event system of complete model again
It has been a complete discrete event system, therefore has met by whether existing way verifies a discrete event system
Precondition with state opacity.
S105：When discrete event system has state opacity, determine that status information is in confidential state.
I.e. the application can not analyze this technology to incomplete discrete event system progress state opacity for solution and lack
It falls into, provides a kind of complete discrete to be converted by making up the Partial State Information lacked in incomplete discrete event system
The mode of event system, because after being converted into complete discrete event system state can be carried out not according to existing way
It is transparent to analyze, when constructing the corresponding discrete event system of complete model again with state opacity, so that it may determine former
The status information of need for confidentiality has been in confidential state in incomplete discrete event system.
Certainly, after determining that corresponding discrete event system has state opacity, other aspects, example be can be also used for
Such as digital signature, secret communication, authentification of message, intrusion detection, data encryption, the application focus on how to realize that script can not be right
Incomplete discrete event system carries out the analysis of state opacity, rather than how based on accurate state opacity analysis result
It is applied in conjunction with specific actual scene, those skilled in the art can provide a variety of application modes on this basis, herein no longer
It repeats one by one.
Based on the above-mentioned technical proposal, a kind of information security differentiation side based on Learner diagnosis device provided by the embodiments of the present application
Method by introducing there is the Learner diagnosis device of learning ability and candidate state algorithm to handle incomplete models, by making
It is simulated with that can not only be shifted to the state of hypothesis in candidate state algorithm, and can be in the study of Learner diagnosis device
Incomplete models are converted into complete model by continuously attempting to be restored by the status information of systematic absence under function
Mode complies with the precondition of proofing state opacity, and then differentiates whether the incomplete discrete event system is successfully right
Some information realizations secrecy, while the application field of Learner diagnosis device being widened to this field of proofing state opacity.
Embodiment two
Below in conjunction with Fig. 2, Fig. 2 is that another information security based on Learner diagnosis device provided by the embodiments of the present application differentiates
The flow chart of method, the present embodiment on the basis of example 1, provide a kind of how specifically used with learning ability
The method that diagnostor and state candidate algorithm obtain the Partial State Information lacked in incomplete models, specific embodiment is such as
Under：
S201：Incomplete models are generated according to incomplete discrete event system；
S202：Learner diagnosis device is constructed according to the status information of incomplete models output；
S203：Information update is carried out using candidate state algorithm in each interval between diagnosis of Learner diagnosis device, is obtained
Complete model deficiency information；
It constructs to obtain diagnostor according to the status information that incomplete models export first, be examined later in each of the diagnostor
The disconnected period all using the available mode being contained in candidate state algorithm and assumes that transfer is diagnosed, and sees whether meet the requirements,
And gradually obtain each missing information.
Specifically, when Learner diagnosis device diagnoses discovery existence deficient phenomena, to including in candidate state algorithm
State assume that collection executes amendment operation, with the hypothesis transfer for including in the candidate state that timely updates, remaining assume to turn to be promoted
The correctness of shifting, the purpose of amendment operation have been to reduce the phenomenon that assuming information redundancy in transfer set.
S204：Learner diagnosis device is successively integrated in the complete model deficiency information that each interval between diagnosis obtains incomplete
On model, then constructed complete model；
On the basis of S203, what this step was intended to obtain diagnosis in each interval between diagnosis by the way of successively integrating
Missing information is such as integrated on incomplete models, with finally will be initial incomplete after the diagnosis for completing all interval between diagnosis
Model is changed into constructs complete model again, in case the opaque analysis of subsequent carry out state uses.
S205：Verifying constructs whether the corresponding discrete event system of complete model has state opacity again；
S206：When discrete event system has state opacity, determine that status information is in confidential state.
Embodiment three
The present embodiment proposes a kind of judges the impermeable of system current state under conditions of incomplete discrete event system
The method of bright property, this method are G=(X, Σ, δ, x with automatic machine0, Y, λ) and it is model, point four steps are completed：First according to true mould
Type Gt, the incomplete models G for the missing that must do wellnIn the hypothesis transfer for having current state opacity；Then hypothesis is shifted
Be split, residual error and increase residual error calculation process, obtain generator collectionAgain to incomplete models Gn, waited by introducing
The Learner diagnosis device LD for selecting c and learning functionality, obtains generator collectionFinally compare generator collectionWith generator collection
Judgement system G has current state opacity, and the specific method is as follows：
1, according to true model Gt, the incomplete models G for the missing that must do wellnIn the vacation for having current state opacity
If transfer：Diagnostor is the automatic machine D an of finite state, it is by the output sequence y of system G1y2…ykSequence is inputted as it
Column.Along with ykGeneration, diagnostor D can calculate one group of setWherein x belongs to zk.Specifically, it diagnoses
Device is defined as finite-state automata：
Wherein Z ∪z 0It is state set,z 0:=(z0, 0) and it is original state,Y is event set；ζ is to turn
Move function ζ:Z∪{z 0}×Y→Z；For output collection；K is that the output of diagnostor maps k:
State in diagnostor D shifts zk+1=ζ (zk,yk+1) obtained by following condition：
z1=ζ (z 0,y1)=z0∩λ-1({y1})
zk+1=Ψ (zk)∩λ-1({yk+1}),k≥1
Wherein
But the construction of above-mentioned condition diagnosing device D is to construct on the basis of complete discrete event system one
When occurring state missing (i.e. system G is incomplete models) in denier system, the building method of the diagnostor is just no longer applicable.
For this purpose, being directed to incomplete system model, we introduce two submodels, and one is true model (true-model) Gt=
(X,Σt,δt,x0, Y, λ), another is nominal plant model (nominal-model) Gn=(X, Σn,δn,x0,Y,λ).Because nominal
Model GnIn may lack true model GtIn certain information, so nominal plant model GnIn parameter information meet condition
We are according to nominal plant model GnThe diagnostor of creation is known as nominal diagnostor, is denoted as Dn.Given nominal plant model GnWith
Output sequence y1y2…ykIf state transfer all meets condition for all k >=1Its
Middle zkFor DnIn state estimation, then claim GnWith GtOutput sequence be consistent, i.e., next state estimation zk+1It is specific.
In order to make up GnWith GtBetween difference, we introduce hypothesis transfer (hypothesis transitions)Come
Correct Gn.With orderly state to d=(xsrc,xdst) indicate to assume some transfer process in transfer, it is indicated from state xsrc
To state xdstTransfer, wherein xsrc∈X,xdst∈X.For convenience, it is abbreviated as
2, to assume transfer be split, residual error and increase residual error calculation process, obtain generator collection
Enable g1,g2,…,gnForIn mutually disjoint subsets, GI={ g1,g2,…,gnIt is generator, with [GI] indicate GI
The class of generation, i.e.,
[GI]={ { d1,d2,…,dn}|di∈gi,1≤i≤n}
For generator collection, similarly,It indicatesThe class of generation, GITo generate
Device collectionIn a subset.
If GI={ g1,g2,…,gn,AndIf Qk=(qk1,qk2,…,qkn) and 1≤k≤n,
So Q={ Qk|QkIt is a generator } it is GIWithCutting operation, be denoted asBy cutting operation
It is generalized toIn, definition
Assuming thatIt is generator collection with Q,QJ∈ Q, qj∈QJ, definition：
For QJTo GICutting operation,For QJIt is rightSegmentation.Q pairsSegmentation behaviour
It is defined as：
If GI={ g1,g2,…,gn,AndDefine GIWithResidual error be divided into：
And it is generalized toIn, definition
Similar to cutting operation, generator collection or generator also relate to residual error operation.Assuming thatIt is generator collection with Q,And QJ∈ Q is generator, qj∈QJ, define QJTo GIResidual error operation be：
Indicate QJIt is rightResidual error operation.Q pairsResidual error Operation Definition be：
If GI={ g1,g2,…,gn,AndDefine GIWithIncrease residual error be divided into：
WhereinIt is generalized toIn, definition
It is assumed that being covered in setIn, MIFor known form of expression set, andClassification in
It include about to MIAll explanations.The additional form of expression if it existsWhereinThen correct
Generator collection afterwardsIt is defined as：
Wherein In value illustrate to form of expression MJ=MI∪
{mjExplanation.
Assuming that GI=(g1,…,gn), QJ={ q1,…,qnIt is generator,It is generator collection, behaviour with Q
MakeIndicate generator between by union operation obtain as a result, i.e.
Assuming thatIt is generator collection with Q, thenIt is with (merge) operation that merges of Q：
Intuitively,It indicatesThe result set of not redundancy between Q,
Indicate generator collectionNot common subset between Q,
Indicate generator collectionThe common subset between Q.
3, according to incomplete models Gn, by introducing the Learner diagnosis device LD of candidate c and learning functionality, obtain generator collection
In incomplete system, Learner diagnosis device can be with trial learning true model, it is with the output sequence y of system1y2…
ykAs input, the information lacked in nominal plant model is obtained, and the state that forecasting system lacks in each hypothesis.In order to clear
State and hypothesis transfer in clear description Learner diagnosis device, defining candidate (candidate) isWherein z is to be
The state estimation of system,For generator collection, all hypothesis collection are indicated, the set of all candidate c is indicated with symbol C.
Learner diagnosis device LD is configured to automatic machine by the construction for being configured similarly to diagnostor D of Learner diagnosis device LD：
Wherein W ∪w 0It is state set, Y is event set, and t is transfer function t:W∪{w 0} × Y → W,w 0:=(w0, 0) be
Original state,For output collection, k is output mapping k:
Rule is updated in order to define, forWhereinIndicate candidateDefining μ operation is μ (w1,w2), it is indicated if w1With w2Between there are identical state estimation, need w2In
Duplicate status merging is to w1, i.e.,
It is assumed thatBe Learner diagnosis device in state, y is outgoing event, wherein w'=update (c1,y)
∪…∪update(cn, y) and={ c'1,…,c'n, and define
Finally, the state transition function w in Learner diagnosis devicek+1=t (wk,yk+1) can be obtained by following formula：
In Learner diagnosis device LD, the state w of each step can be related to candidate state c, include institute in candidate state c
Some states and hypothesis transfer.Simultaneously during Learner diagnosis, assume that information redundancy shows in transfer set to reduce
As, it is every there is a phenomenon where being modified operation to assuming to collect when the missing of a next state, and for the shape in Learner diagnosis device LD
State carries out ld_update operation, and the bottom operation of ld_update is union operation, can be obtained by the above operation
To generator collection
4, generator collection obtained in step 2 is comparedWith generator collection obtained in step 3Judge whether G has
The opacity of current state：
Second step is split by the transfer of the hypothesis that obtains to the first step, residual error and increase the calculation process such as residual error it
Afterwards, the complete system generator collection with current state opacity is obtainedAnd in the third step in incomplete system G
GnBy the study of Learner diagnosis device LD, the generator collection of another complete system is obtainedIfThen illustrate this
Two complete systems are consistent, and the transfer of the hypothesis of the first step is obtained under the premise of system has current state opacity
Out, therefore, incomplete system G has current state opacity., whereas ifWithIt is unequal, then illustrate the two
Complete system does not match, and G has the precondition of current state opacity wrong, i.e. it is opaque that G does not have current state
Property.
Above content is illustrated below by the application example of an incomplete discrete event system：
Certain enterprise produces a collection of speciality steel, in order to promote the market competitiveness of steel, needs to carry out different journeys three times
The compressive property of degree is tested：Low voltage experiment, Hi-pot test, super-pressure test.Assuming that these three pressure tests in no particular order sequence,
Testing sequence does not influence result, and only by three kinds test and steel up to standard just allow market circulation, otherwise prohibit
Fluid stopping is logical.Harmful competition between peer enterprises in order to prevent needs to maintain secrecy to certain state points of working process.
Assuming that specific test process as shown in figure 5, it indicate to need to carry out from the steel that the area A is produced low pressure, high pressure,
Different condition is handled super-pressure three times, is cased after cooling by 8 harbours, is transported two areas B, C to and is carried out pressure test.
The steel of low pressure processing carry out natural cooling under normal circumstances, transport No. 4, No. 5,6 numbering heads after cooling to, and HIGH PRESSURE TREATMENT
Pressure cooling is carried out with the steel of ultra high pressure treatment, the steel of HIGH PRESSURE TREATMENT transport No. 6, No. 7,8 numbering heads to, and ultra high pressure treatment is cold
But the steel after transport No. 8,14 numbering heads to.The means of transportation that No. 9 transfer stations are reached by harbour is water transport, reaches No. 10 transhipments
The means of transportation stood is land transportation, and the means of transportation for eventually arriving at B or the area C is air transportion.The steel for transporting the area B to are needed again
The area A is returned to, carries out the processing of other two pressure conditions, so different pressure treatment operation in triplicate.Transport the area C to
Steel any processing is not carried out due to cause specific, the area D has been transported to, wherein 4 numbering heads and 8 numbering heads are in maintenance shape
State, loss of learning therein are imperfect.
In Fig. 4, since steel need to carry out pressure tests different three times, and each test data information is not right
Outer announcement improves the competitiveness of product scope.Therefore, the area B can be considered as to secrecy in this process, and at this time
The area C is considered as the non-secret state in contrast handled with the area B.
For this purpose, the product processing of such as Fig. 4 is modeled as discrete event system G shown in fig. 5, wherein each event is shown in Table
1, X=0,1 ..., and 14 }, Xs={ 11 },Σuo={ a, b }.
1 event of table and its sign change table
Since the state in system is after the generation of certain events, identical state can be both reached, difference can also be reached
State, therefore selected event stringOutput sequence as system G.
Step 1：Since 4 numbering heads and 8 numbering heads are in maintenance state, in true model GtIn conversionWith?
Nominal plant model GnIn information be missing from, it is believed that output sequenceOccur in true model GtIn, because
For in GnIn can not occur.
System is after occurring λ α μ, nominal diagnostor DnState be z={ 4,5,6 }.In next outputAfter generation, shape
State reaches { 11,12 } indirectly, then what may be occurred at this time is converted toIn current state, system table
The opacity of current state is revealed, because there is the paths of projection having the sameConfirm that the external world can not
The current state of system.If event a and event b is considerable event, above-mentioned condition not can guarantee system with current state
Opacity.
After next output λ occurs, state 1. is reached thus it can be inferred that the state that previous step reaches is state 11, vacation
If being converted toSimilarly, to remaining sequenceSame judgement is made, obtained current state is not
Transparent hypothesis is respectivelyWithWhat the conversion that therefore nominal plant model is lost occurred comes fromOrIn one of path.At this time if will assumeAdd
It is added to nominal plant model GnWhen, wherein Obtained model
It will be with true model GtOutput sequence is consistent.Conversion in diagnostor is as shown in Figure 6.
Step 2：PCT is introduced in incomplete model, is explained to above-mentioned analysis.Under initial conditions, it is assumed that system
The form of expressionWithOutput sequence is occurringAfterwards, form of expression m1,
At this timeIt obtainsM1={ m1}.Work as hair
Raw output sequenceAfterwards, it derivesAs shown in Figure 5, the generation of event μ and event η
State 6 will be reached, the result set generated at this time is：
WhereinM2=M1∪{m2}。
In output sequenceAfter generation, the hypothesis transfer of generation isIt is duplicate due to occurring
Conversion, soM3={ m1,m2,m3It is possible thereby to
Know, output sequence occurs in systemWhen, the opacity for the current state that system is shown,
Obtain generator
Step 3：The Learner diagnosis device with learning functionality is introduced, the study of state opacity in incomplete models is verified
Function.Under primary condition, it is assumed that state estimation be X andTherefore, initial candidateIn the event of generation
After string λ α μ, the state estimation z of appearance3={ 4,5,6 }.Since output sequence formula is continuous in nominal plant model, soIn eventAfter generation, z4={ 11,12 }.Due to Xs={ 11 } and event a, event b are inconsiderable
, there is identical projection result, the external world can not determine the current state of system, i.e.,
With
Since next outgoing event is λ, system at this time is in state 0, thus judges the state that previous step occurs
It is unlikely to be 12, it will causeTherefore after exporting λWhen
After output sequence β η occurs,
Since system has the opacity of current state, and Xs={ 11 }, Σuo={ a, b }, when event occurs againWhen, there are two states in identical projection result, makes the external world that can not sentence the state for knowing that system currently reaches.It is then nominal at this time
Model deficiency is converted toWithThus candidate is obtained
And
Similarly, after event λ occurs, system is in shape 0.Conclude therefrom that state transfer z (6,7,8) → z of previous step
(11),The hypothesis transfer generated at this timeIt cannot merge, because successively
Reaching the event of state 6 twice is μ and η respectively.SoAfter being finished, obtained generator collection isFinally, having been obtained as shown in Figure 7 according to the Learner diagnosis device of construction
Flow chart.
Therefore, it is in output sequenceWhen, generator identical with step 2 is obtained, i.e.,Show that incomplete system has current state opacity.Intuitively, show can be to this crowd of spy of enterprise for the conclusion
Whether matter steel, which " transport the area B to " after working process, maintains secrecy.
Because situation is complicated, it can not enumerate and be illustrated, those skilled in the art should be able to recognize according to the application
The basic skills principle combination actual conditions of offer may exist many examples, in the case where not paying enough creative works,
It should within the scope of protection of this application.
Fig. 8 is referred to below, and Fig. 8 sentences for a kind of information security based on Learner diagnosis device provided by the embodiment of the present application
The structural block diagram of other system, the system may include：
Incomplete models generation unit 100, for generating incomplete models according to incomplete discrete event system；Wherein,
It include the status information of need for confidentiality in incomplete discrete event system；
Missing information recovery unit 200, for being carried out using Learner diagnosis device and candidate state algorithm to incomplete models
Processing, obtains complete model deficiency information；
Complete remodeling builds unit 300, for being constructed again according to incomplete models and the generation of complete model deficiency information
Complete model；
State opacity authentication unit 400, for verifying whether construct the corresponding discrete event system of complete model again
With state opacity；
Secrecy situation judging unit 500, for determining status information when discrete event system has state opacity
In confidential state.
Wherein, missing information recovery unit 200 may include：
Learner diagnosis device constructs subelement, and the status information for being exported according to incomplete models constructs Learner diagnosis device；
Candidate state algorithm information updates subelement, for utilizing candidate in each interval between diagnosis of Learner diagnosis device
State algorithm carries out information update, obtains complete model deficiency information.
Wherein, candidate state algorithm information update subelement may include：
State assumes collection correction module, is used for when Learner diagnosis device diagnoses discovery existence deficient phenomena, to time
The state for including in state algorithm is selected to assume that collection executes amendment operation.
Wherein, complete remodeling builds unit 300 and may include：
Learner diagnosis device is successively integrated in incomplete models in the complete model deficiency information that each interval between diagnosis obtains
On, then constructed complete model.
Further, which can also include：
Comparing unit, for constructing whether the corresponding discrete event system of complete model has state opaque again in verifying
Property before, relatively true complete model and construct complete model again, obtain comparison result；Wherein, true complete model is by true
Complete discrete event system generates, and incomplete discrete event system is that true complete discrete system loses a part of status information
After obtain；
Adjustment unit, for being adjusted correspondingly according to Diagnostic parameters of the comparison result to Learner diagnosis device.
Based on the above embodiment, present invention also provides a kind of information security discriminating gears based on Learner diagnosis device, should
Device may include memory and processor, wherein have computer program in the memory, which calls the memory
In computer program when, step provided by above-described embodiment may be implemented.Certainly, which can also include various necessity
Network interface, power supply and other components etc..
Present invention also provides a kind of computer readable storage mediums, have computer program thereon, the computer program
Step provided by above-described embodiment may be implemented when being performed terminal or processor execution.The storage medium may include：U
Disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access
Memory, RAM), the various media that can store program code such as magnetic or disk.
Each embodiment is described in a progressive manner in specification, the highlights of each of the examples are with other realities
The difference of example is applied, the same or similar parts in each embodiment may refer to each other.For device disclosed in embodiment
Speech, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is referring to method part illustration
?.
Specific examples are used herein to illustrate the principle and implementation manner of the present application, and above embodiments are said
It is bright to be merely used to help understand the present processes and its core concept.For those skilled in the art,
Under the premise of not departing from the application principle, can also to the application, some improvement and modification can also be carried out, these improvement and modification
It falls into the protection scope of the claim of this application.
It should also be noted that, in the present specification, relational terms such as first and second and the like be used merely to by
One entity or operation are distinguished with another entity or operation, without necessarily requiring or implying these entities or operation
Between there are any actual relationship or orders.Moreover, the terms "include", "comprise" or its any other variant meaning
Covering non-exclusive inclusion, so that the process, method, article or equipment for including a series of elements not only includes that
A little elements, but also other elements including being not explicitly listed, or further include for this process, method, article or
The intrinsic element of equipment.In the absence of more restrictions, the element limited by sentence "including a ...", is not arranged
Except there is also other identical elements in the process, method, article or equipment for including element.
Claims (10)
1. a kind of information security method of discrimination based on Learner diagnosis device, which is characterized in that be applied to incomplete discrete event system
System, including：
Incomplete models are generated according to incomplete discrete event system；Wherein, include in the incomplete discrete event system
The status information of need for confidentiality；
The incomplete models are handled using Learner diagnosis device and candidate state algorithm, obtain complete model deficiency letter
Breath；
Complete model is constructed again according to the incomplete models and the complete model deficiency information generation；
Verifying is described to construct whether the corresponding discrete event system of complete model has state opacity again；
When the discrete event system has state opacity, determine that the status information is in confidential state.
2. method according to claim 1, which is characterized in that using Learner diagnosis device and candidate state algorithm to described endless
Standby model is handled, and complete model deficiency information is obtained, including：
The Learner diagnosis device is constructed according to the status information of incomplete models output；
Information update is carried out using the candidate state algorithm in each interval between diagnosis of the Learner diagnosis device, obtains institute
State complete model deficiency information.
3. method according to claim 2, which is characterized in that utilized in each interval between diagnosis of the Learner diagnosis device
The candidate state algorithm carries out information update, including：
When the Learner diagnosis device diagnoses discovery existence deficient phenomena, to the shape for including in the candidate state algorithm
State assumes that collection executes amendment operation.
4. method according to claim 1, which is characterized in that believed according to the incomplete models and the complete model deficiency
Breath generates and constructs complete model again, including：
The Learner diagnosis device is successively integrated in the complete model deficiency information that each interval between diagnosis obtains described incomplete
On model, obtain described constructing complete model again.
5. according to claim 1 to any one of 4 the methods, which is characterized in that in verifying, described to construct complete model again corresponding
Discrete event system whether there is state opacity before, further include：
Compare true complete model and construct complete model again with described, obtains comparison result；Wherein, the true complete model by
True complete discrete event system generates, and the incomplete discrete event system is that the true complete discrete system loses one
It is obtained after Partial State Information；
It is adjusted correspondingly according to Diagnostic parameters of the comparison result to the Learner diagnosis device.
6. a kind of information security judgement system based on Learner diagnosis device, which is characterized in that be applied to incomplete discrete event system
System, including：
Incomplete models generation unit, for generating incomplete models according to incomplete discrete event system；Wherein, described endless
It include the status information of need for confidentiality in standby discrete event system；
Missing information recovery unit, for using Learner diagnosis device and candidate state algorithm to the incomplete models at
Reason, obtains complete model deficiency information；
Complete remodeling builds unit, for being constructed again according to the incomplete models and the complete model deficiency information generation
Complete model；
State opacity authentication unit described constructs whether the corresponding discrete event system of complete model has for verifying again
State opacity；
Secrecy situation judging unit, for when the discrete event system has state opacity, determining the state letter
Breath is in confidential state.
7. system according to claim 6, which is characterized in that the missing information recovery unit includes：
Learner diagnosis device constructs subelement, and the status information for being exported according to the incomplete models constructs the Learner diagnosis
Device；
Candidate state algorithm information updates subelement, described for utilizing in each interval between diagnosis of the Learner diagnosis device
Candidate state algorithm carries out information update, obtains the complete model deficiency information.
8. system according to claim 7, which is characterized in that the candidate state algorithm information updates subelement and includes：
State assumes collection correction module, is used for when the Learner diagnosis device diagnoses discovery existence deficient phenomena, to institute
It states the state for including in candidate state algorithm and assumes that collection executes amendment operation.
9. a kind of information security discriminating gear based on Learner diagnosis device, which is characterized in that including：
Memory, for storing computer program；
Processor is realized when for executing the computer program and is based on Learner diagnosis as described in any one of claim 1 to 5
The step of information security method of discrimination of device.
10. a kind of computer readable storage medium, which is characterized in that be stored with computer on the computer readable storage medium
Program is realized when the computer program is executed by processor and is based on Learner diagnosis device as described in any one of claim 1 to 5
Information security method of discrimination the step of.
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