CN103699762A - CPS (Cyber-Physical System) attribute verification method based on statistical model checking (SMC) - Google Patents
CPS (Cyber-Physical System) attribute verification method based on statistical model checking (SMC) Download PDFInfo
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Abstract
The invention discloses a CPS (Cyber-Physical System) attribute verification method based on statistical model checking (SMC). The CPS attribute verification method comprises the following steps: S1, setting a communication port to expand a hybrid automata into an expansive hybrid automata, and acquiring an operation track of a CPS model passing through the expansive hybrid automata; S2, constructing a monitor for generating an operation track sample of the CPS model, and taking the track sample generated by the monitor as an input of an SMC statistic verification stage; and S3, collecting evidence for proving that the CPS model meets a specific attribute by using a statistic evaluation technology, and judging whether the CPS model meets the specific attribute by using a preset second algorithm.
Description
Technical field
The invention belongs to Internet of Things and CPS field, be specifically related to model detection technique and statistical estimation technology, especially a kind of CPS attribute verification method detecting based on statistical model.
Background technology
Along with the development of embedded technology, computer technology and network technology, and the continuous lifting of hardware product performance and data-handling capacity, it is information-based, intelligent that computer system is tending towards gradually.Under this demand, information physics emerging system (Cyber-Physical Systems, CPS) arises at the historic moment as a kind of novel intelligent system, and has caused the great attention of national governments, academia and industry member.
CPS is the complicated Embedded network system that has merged calculating and physics process, and it is monitored and control physical equipment by embedded system and network, and influences each other by feedback mechanism.In future, CPS will be widely used in the numerous areas such as the monitoring of important infrastructure and control, Defense Weapon System, health care and intelligent transportation, therefore, guarantees that CPS's is safe and reliable extremely important.Because uncertainty and the physical equipment itself of physical environment may be out of order, so guarantee that the robustness of whole CPS and security are important challenges.
The method that has at present some to verify CPS system, such as software test etc.Although software test can guarantee the reliability of system to a certain extent, can only find wherein obvious mistake, and can only just can carry out in the later stage of software development, there is certain limitation.
In fact, the method for different forms is verified the mistake in just finding to design in design phase of system to system, thereby effectively guarantees the later stage everything goes well with your work to carry out, and greatly reduces the cost of software development.Model detection technique is a kind of more conventional formalization method, and it has supermatic feature, and it comes verification model whether to meet specific attribute stipulations by the state space of Ergodic Theory.Model detects needs three steps conventionally, i.e. modeling, stipulations and checking: system modelling (Modeling) is abstract by system is carried out, set up the formalized model of system, conventionally be modeled as state transition system, M=<L, T, S>.Requirements specification (Specification) be must be satisfied by system demand with sequential logical formula formalization representation, become φ.Modelling verification (Verification) is to given system model M, and system property stipulations φ comes judgment models M whether to meet attribute φ by certain verification algorithm, i.e. M ' φ.
At present, common model detection technique is that the state space by Ergodic Theory comes verification system whether to meet specific attribute, thereby is subject to the big or small restriction of system state space, faces State-explosion problem.And the system state space of CPS is conventionally larger in reality, uncertainty and the equipment itself of adding physical environment may be out of order, thereby traditional model detection technique is just verified and seemed and be pale and weak CPS.
It is a kind of checking large-scale complicated system new technology proposing recently that statistical model detects (Statistical Model Checking, SMC).The core concept of SMC is the emulation that tectonic system is carried out, and then whether the method decision-making system by statistical estimation technology meets specific attribute with a certain degree of confidence.
Although CPS has caused extensive concern both domestic and external, because relevant research is at the early-stage, at present obtained achievement in research is quite limited, has at present following CPS attribute verification methods.
Edward professor A.Lee of California, USA university leader's Ptolemy engineering, studies concurrent, real-time, the modeling of embedded system, simulation and design.Its main focus is the combination of concurrent assembly, and cardinal principle is: multiple computation model is carried out to stratification combination, to solve Heterogeneous Computing model, mix the problem of using.But these work mainly concentrate on the modeling and simulation of CPS, to the checking of the attribute of CPS, relate to less.In addition, Li Lihang etc. be take Timed Automata as instrument, the physical entity that will monitor and control respectively and different types of service Independent modeling, and time attribute is verified.Due to CPS software and the closely-coupled feature of hardware, the restriction of the ability to express of Timed Automata, therefore needs the model that ability to express is stronger.
SMC is proposed and is utilized acceptance sampling to verify the attribute of discrete event system by Younes, and developed statistical model detection model detecting device, the error of having discussed during statistical model detects is controlled, and has studied the statistical testing of business cycles problem about unbounded until operational symbol attribute.In these work, the model adopting is CTMC, DTMC and MDP, due to CPS itself, adopts these models very difficult to CPS modeling.
Paolo Zuliani etc. detect Bayesian statistics student movement for the statistical model of stochastic system, the statistical model detection problem of computing time of causing due to rare event exploding of having utilized coupled wave theory (Coupling) and importance sampling technical research.The researchers such as Clarke are applied to solve statistical model detection computations time blast problem by importance sampling and cross-entropy technology, SMC has been carried out to expansion and made it support uncertain sight, and partial order yojan is applied to comprise pseudo-probabilistic model.
In the middle of the research aspect the checking of above-mentioned various CPS attribute, all made significant headway, but also come with some shortcomings: first, there is some work mainly to pay close attention to the modeling and simulation of CPS, and less to the verification of correctness research of CPS attribute; Secondly, in current statistical model detection algorithm, the model of employing is CTMC, DTMC and MDP mostly, and due to CPS itself, these models can not well be expressed.
In view of the foregoing, the invention provides a kind of CPS attribute verification method detecting based on statistical model, the main hybrid automata network of expansion that adopts carries out modeling to CPS, tectonic model detects the sample needing again, and design a kind of statistical model detection algorithm of hybrid automata network, in order to verify the correctness of CPS attribute.
Summary of the invention
The invention provides a kind of CPS attribute verification method detecting based on statistical model, comprise the following steps:
S1, communication port is set hybrid automata is expanded to expansion hybrid automata, and obtain CPS model by the track of described expansion hybrid automata operation;
S2, be configured to produce the monitor of the track sample of described CPS model running, and the track sample that described monitor is generated is as the input in SMC statistical testing of business cycles stage;
S3, use statistical estimation technology are collected the evidence that described CPS model meets particular community, and are used the second default algorithm to judge whether CPS model meets described particular community.
Preferably, in step S1, described communication port is tuple Port=<pid, value, dom, options>, wherein, the unique identifier that pid is port, the data that value is port, dom is the data type of message communicating, and options is the set that port allows the operation carried out.
Preferably, in step S1, described expansion hybrid automata is expressed as EHA=<HA, P>, wherein, HA is hybrid automata and described HA=<Loc, Var, Lab, E, Act, Inv>, P={p
1, p
2..., p
nit is the set of all ports associated with HA.
Preferably, in step S2, the first algorithm that described monitor arranges according to self generates track sample, and described monitor monitors discrete events all in CPS system.
Preferably, the particular community that the CPS model described in step S3 need be satisfied, adopts probability linear time temporal logic to be described.
Preferably, described step S3 adopts Bayesian statistical model detection method to carry out the checking of CPS attribute.
Preferably, in step S3, if CPS model is M, arbitrary execution track that σ is M, φ is CPS attribute formula to be verified, and θ ∈ (0,1) is probability threshold value, and p represents that the execution track of M meets the probability of formula φ, ought meet M ' P
>=θ(φ), during p>=θ, complete attribute checking.
According to the CPS attribute verification method detecting based on statistical model provided by the invention, by communication port is set, hybrid automata is expanded to after expansion hybrid automata, obtain CPS model by the track of expansion hybrid automata operation, CPS has been made more accurately and having been described.Meanwhile, structure monitor to be to produce the track sample of CPS model running, usings this input as the SMC statistical testing of business cycles stage, and uses statistical estimation technology to collect the evidence that CPS model meets particular community, to judge whether CPS model meets particular community.So, effectively avoided the bottleneck of conventional model detection technique, and the system state space bringing blast problem.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skills, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.
Fig. 1 is the CPS attribute verification method process flow diagram detecting based on statistical model that preferred embodiment of the present invention provides;
Fig. 2 is the hybrid automata model schematic diagram of the thermostat that provides of preferred embodiment of the present invention;
Fig. 3 is the CPS software attributes validation framework schematic diagram that preferred embodiment of the present invention provides.
Embodiment
Hereinafter with reference to accompanying drawing, also describe the present invention in detail in conjunction with the embodiments.It should be noted that, in the situation that not conflicting, embodiment and the feature in embodiment in the application can combine mutually.
Fig. 1 is the CPS attribute verification method process flow diagram detecting based on statistical model that preferred embodiment of the present invention provides.As shown in Figure 1, the CPS attribute verification method detecting based on statistical model that preferred embodiment of the present invention provides comprises step S1～S3.
Step S1: communication port is set hybrid automata is expanded to expansion hybrid automata, and obtain CPS model by the track of described expansion hybrid automata operation.
Particularly, hybrid automata be a kind of by Alur, proposed discrete and formalized model continuous dynamic system can be described simultaneously.Hybrid automata has added the expansion of one group of variable on the basis of finte-state machine, and its position (location) represents the successional evolution of system, and its conversion (transition) represents the discrete transition of system state.Fig. 2 is the hybrid automata model schematic diagram of the thermostat that provides of preferred embodiment of the present invention.As shown in Figure 2, conventionally with digraph, represent hybrid automata, use vertex representation position, with limit, represent discrete migration.
Conventionally, hybrid automata HA is a hexa-atomic group of HA=<Loc, Var, and Lab, E, Act, Inv>, wherein: the set that Loc is summit, is called position (location); Var is the set of real-valued variable, the assignment of variable x is function v (x): Var → R, variable is mapped to real number value, with V, represent the set of all assignment, the state of hybrid automata is <l, v>, wherein l ∈ Loc, v ∈ V, represents the set of all states with ∑; Lab is the set of sync tag; E is the set on limit, limit e=<l, and a, μ, l'> comprises source position l ∈ Loc and target location l' ∈ Loc, sync tag a ∈ Lab, transformational relation μ ∈ V * V; Labeling function Act is each the position a series of activities of l ∈ Loc mark (activity) function Act (l): R
>=0→ V, is mapped to time t the function of V; Labeling function Inv gives each a position l ∈ Loc invariant Inv of mark (l) ∈ V
2.
In CPS, each assembly is not completely isolated, and they are the Unified Globals that connect each other.Therefore, to describe more accurately CPS, need to be expanded classical hybrid automata.The concept that the present embodiment is provided with communication port connects CPS different piece, specifically describes as follows.
Described communication port is tuple Port=<pid, value, dom, options>, wherein, the unique identifier that pid is port, the data that value is port, dom is the data type of message communicating, and options is the set that port allows the operation carried out.
Described communication port only allows to carry out two kinds of operation Read and Write.For example, Read and Write operation can be carried out in that one end that sensor communication port is connected with sensor, and Read operation can only be carried out in this one end being connected with computing unit.Its operation format example is as shown in table 1:
Table 1
In the present embodiment, described expansion hybrid automata is expressed as EHA=<HA, P>, and wherein, HA is hybrid automata, and described HA=<Loc, Var, Lab, E, Act, Inv>, P={p
1, p
2..., p
nit is the set of all ports associated with HA.Conventionally, the track of once carrying out of hybrid automata is a limited or unlimited sequence
σ wherein
i=<l
i, t
i>, l
i∈ Loc, t
i>=0 is illustrated in position l
ithe time stopping.
Step S2: be configured to produce the monitor of the track sample of described CPS model running, and the track sample that described monitor is generated is as the input in SMC statistical testing of business cycles stage.
Particularly, the core concept of SMC be by the emulation of system to obtain the execution sample of system, then utilize statistical estimation technology to analyze the execution sample of collecting, with decision-making system, whether with a certain degree of confidence, meet specific attribute.The execution of the hybrid automata below the present invention being used, the Sample producing process that system is carried out is made an explanation.
Wherein, the validation problem of stochastic system M and logical formula φ will calculate the probability of M ' φ exactly.The method that solves at present this class problem mainly contains two classes: numerical method and statistical method (as SMC).The method of numerical value has very pinpoint accuracy conventionally, but is subject to the restriction of state space size.SMC regards statistical reasoning problem as this validation problem, and utilizes and done reasonably to sample and solve in the emulation path of model.Effectively avoided the restriction of system state space size.Fig. 3 is the CPS software attributes validation framework schematic diagram that preferred embodiment of the present invention provides.
In SMC, conventionally adopt probability linear time temporal logic (Probabilistic Bounded Linear Temporal Logic, PBLTL) formula to be described attribute, wherein PBLTL is the expansion to LTL.For example, the set Var to model M and real-valued variable defines shape as boolean's predicate of y～v on Var, y ∈ Var wherein ,～∈≤, >=,=, v ∈ R.
In this, the syntactic definition of BLTL attribute formula φ is:
the semanteme of described BLTL formula defines on the execution track of M, represents that the execution track of M meets attribute φ with σ ' φ.The present embodiment σ
ithe suffix of the track σ that expression walks since i, especially, σ
0can represent original track σ.The value representation of variable y i step in σ is V (σ, i, y).The semanteme of BLTL formula may be interpreted as: σ
k' y～v, and if only if V (σ, k, y)～v; σ
k' φ
1∨ φ
2, and if only if σ
k' φ
1or σ
k' φ
2; σ
k' φ
1∧ φ
2, and if only if σ
k' φ
1and σ
k' φ
2;
and if only if σ
k' φ
1be false (is σ
k'/φ
1); σ
k' φ
1u
tφ
2, and if only if exists i ∈ N to make (1) Σ
0≤l≤it
k+1≤ t, (2) σ
k+i' φ
2, (3) 0≤j≤i, σ
k+j' φ
1.
Due to impossible, obtain the unlimited sample of carrying out, and verified need to just can be judged whether track meets attribute formula φ in the limited prefix of an execution track.The length of track prefix is to be determined by the # of boundary (φ) of attribute formula.
The # of boundary (φ) of BLTL formula φ is defined as follows: # (y～v) :=0;
# (φ
1∨ φ
2) :=max (# (φ
1), # (φ
2)); # (φ
1∧ φ
2) :=max (# (φ
1), # (φ
2)); # (φ
1u
tφ
2) :=t+max (# (φ
1), # (φ
2)).
Above-mentioned PBLTL formula is defined as: it is a kind of P
>=θ(φ) formula of form, wherein φ is BLTL formula, and θ ∈ (0,1) is called probability threshold value.Model M meets PBLTL attribute P
>=θ(φ) can be expressed as M ' P
>=θ(φ).M ' P
>=θ(φ) probability that meets attribute φ of once carrying out of the M that sets up that and if only if is more than or equal to θ.The present embodiment is only discussed the relation of being more than or equal to (>=), and the relation that is less than accordingly can be by P
< θ(φ)=1-P
>=θ(φ) obtain.
The track that SMC adopts system model to carry out is used as the input in statistical testing of business cycles stage, and the execution sample that therefore how to obtain model is a critical problem in SMC.The execution sample form of CPS model is for example (s
0, t
0), (s
1, t
1), (s
2, t
2) ..., s wherein
i=<l
i, v
i> is the state of CPS model, t
ifor system is at state s
ithe time stopping.
The track sample that the present embodiment comes production model to carry out by design monitor, the first algorithm that described monitor arranges according to self generates track sample, and described monitor monitors discrete events all in CPS system, in order to the variation of the state of register system.Described the first algorithm is as shown in table 2.
Table 2
Step S3: use statistical estimation technology to collect the evidence that described CPS model meets particular community, and use the second default algorithm to judge whether CPS model meets described particular community.
Particularly, as previously mentioned, the particular community that in the present invention, CPS model need be satisfied, adopts probability linear time temporal logic to be described.
Arbitrary execution track that SMC attempts calculating automaton meets the Probability p of PBLTL attribute φ.Clarke proposes the bayes method of two kinds of cores: interval estimation and test of hypothesis.The difference of two kinds of methods and traditional model detection technique is: the track that does not meet φ is not the counter-example of model, but the evidence of p<1.In this step, adopt Bayesian statistical model detection method to carry out the checking of CPS attribute.
If CPS model is M, arbitrary execution track that σ is M, φ is CPS attribute formula to be verified, and θ ∈ (0,1) is probability threshold value, and p represents that the execution track of M meets the probability of formula φ, ought meet M ' P
>=θ(φ), during p>=θ, complete attribute checking.Specific explanations is: M ' P
>=θ(φ), want can obtain p>=θ, M ' P
>=θ(φ) set up, otherwise M ' P
>=θ(φ) be false.
Suppose respectively H
0: p>=θ and H
1: p< θ, a sample d={ σ to CPS model execution track
1, σ
2, σ
3..., use stochastic variable X
irepresent track σ
imeet the result of attribute φ, its value gets 0 or 1,
because these execution tracks all come from same model, can obtain one group of independent identically distributed observation { x
1, x
2, x
3..., x
n.Due to H
0with H
1mutual exclusion, supposes that prior probability has P (H
0)+P (H
1)=1.According to bayesian theory posterior probability, be:
i ∈ 0,1}, and to each sample d, P (d)=P (d|H
0)+P (d|H
1) >0 always sets up.
In the present embodiment, definition sample d, H
0and H
1bayesian Factor be
wherein, Bayesian Factor β regards support H as
0evidence, its inverse
for supporting H
1evidence.The evidence that represents to accept hypothesis needs with threshold value T.There is at present a kind of efficient β computing formula
wherein x is d={x
1, x
2..., x
nin successful sample number, F
(s, t)() is for take the Beta distribution function that s and t be parameter.
In this step, the second algorithm detecting for the statistical model of CPS model is as shown in table 3, and σ ' φ wherein can verify easily.
Table 3
In sum, the CPS attribute verification method detecting based on statistical model that preferred embodiment provides according to the present invention, by communication port is set, hybrid automata is expanded to after expansion hybrid automata, obtain CPS model by the track of expansion hybrid automata operation, CPS has been made more accurately and having been described.Meanwhile, structure monitor to be to produce the track sample of CPS model running, usings this input as the SMC statistical testing of business cycles stage, and uses statistical estimation technology to collect the evidence that CPS model meets particular community, to judge whether CPS model meets particular community.So, effectively avoided the bottleneck of conventional model detection technique, and the system state space bringing blast problem.
Above-mentioned explanation to the disclosed embodiments, makes professional and technical personnel in the field can realize or use the present invention.To the multiple modification of these embodiment, will be apparent for those skilled in the art, General Principle as defined herein can, in the situation that not departing from the spirit or scope of the present invention, realize in other embodiments.Therefore, the present invention will can not be restricted to embodiment illustrated herein, but will meet the widest scope consistent with principle disclosed herein and features of novelty.Above-mentioned explanation to the disclosed embodiments, makes professional and technical personnel in the field can realize or use the present invention.To the multiple modification of these embodiment, will be apparent for those skilled in the art, General Principle as defined herein can, in the situation that not departing from the spirit or scope of the present invention, realize in other embodiments.Therefore, the present invention will can not be restricted to embodiment illustrated herein, but will meet the widest scope consistent with principle disclosed herein and features of novelty.
Claims (7)
1. the CPS attribute verification method detecting based on statistical model, is characterized in that, comprises the following steps:
S1, communication port is set hybrid automata is expanded to expansion hybrid automata, and obtain CPS model by the track of described expansion hybrid automata operation;
S2, be configured to produce the monitor of the track sample of described CPS model running, and the track sample that described monitor is generated is as the input in SMC statistical testing of business cycles stage;
S3, use statistical estimation technology are collected the evidence that described CPS model meets particular community, and are used the second default algorithm to judge whether CPS model meets described particular community.
2. method according to claim 1, it is characterized in that, in step S1, described communication port is tuple Port=< pid, value, dom, options >, wherein, pid is the unique identifier of port, value is the data of port, the data type that dom is message communicating, and options is the set that port allows the operation carried out.
3. method according to claim 1, is characterized in that, in step S1, described expansion hybrid automata is expressed as EHA=< HA, P >, wherein, HA is hybrid automata and described HA=< Loc, Var, Lab, E, Act, Inv >, P={p
1, p
2..., p
nit is the set of all ports associated with HA.
4. method according to claim 1, is characterized in that, in step S2, the first algorithm that described monitor arranges according to self generates track sample, and described monitor monitors discrete events all in CPS system.
5. method according to claim 1, is characterized in that, the particular community that the CPS model described in step S3 need be satisfied adopts probability linear time temporal logic to be described.
6. method according to claim 1, is characterized in that, described step S3 adopts Bayesian statistical model detection method to carry out the checking of CPS attribute.
7. method according to claim 1, is characterized in that, in step S3, if CPS model is M, σ is arbitrary execution track of M, and φ is CPS attribute formula to be verified, θ ∈ (0,1) be probability threshold value, p represents that the execution track of M meets the probability of formula φ, ought meet MP
>=θ(φ), during p>=θ, complete attribute checking.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104951618A (en) * | 2015-07-06 | 2015-09-30 | 广东轻工职业技术学院 | Possibility measurement calculating tree logic detection model optimization method |
| CN109961172A (en) * | 2018-12-28 | 2019-07-02 | 东南大学 | A kind of CPS rare event probability forecasting method examined based on statistical model |
| CN110062009A (en) * | 2019-05-21 | 2019-07-26 | 杭州逍邦网络科技有限公司 | A kind of formalization detection method of information physical emerging system defence |
| CN115801460A (en) * | 2023-01-16 | 2023-03-14 | 四川大学 | Power distribution information physical system security risk assessment method considering network attack vulnerability |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10262143B2 (en) | 2016-09-13 | 2019-04-16 | The Mitre Corporation | System and method for modeling and analyzing the impact of cyber-security events on cyber-physical systems |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102426521A (en) * | 2011-10-28 | 2012-04-25 | 东南大学 | CPS (Cyber Physical Systems) adaptability verification method based on Hybrid UML (Unified Modeling Language) and theorem proving |
| CN102436375A (en) * | 2011-10-28 | 2012-05-02 | 东南大学 | Characters per second (CPS) Modeling and verification method based on model transformation |
| CN102722593A (en) * | 2011-10-28 | 2012-10-10 | 东南大学 | Cyber physical system (CPS) attribute verification method based on differential algebra timing sequence dynamic logic (DATL) |
| US20130204602A1 (en) * | 2012-02-07 | 2013-08-08 | Electronics And Telecommunications Research Institute | Simulation apparatus and method for verifying hybrid system |
-
2014
- 2014-01-15 CN CN201410017878.6A patent/CN103699762B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102426521A (en) * | 2011-10-28 | 2012-04-25 | 东南大学 | CPS (Cyber Physical Systems) adaptability verification method based on Hybrid UML (Unified Modeling Language) and theorem proving |
| CN102436375A (en) * | 2011-10-28 | 2012-05-02 | 东南大学 | Characters per second (CPS) Modeling and verification method based on model transformation |
| CN102722593A (en) * | 2011-10-28 | 2012-10-10 | 东南大学 | Cyber physical system (CPS) attribute verification method based on differential algebra timing sequence dynamic logic (DATL) |
| US20130204602A1 (en) * | 2012-02-07 | 2013-08-08 | Electronics And Telecommunications Research Institute | Simulation apparatus and method for verifying hybrid system |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104951618A (en) * | 2015-07-06 | 2015-09-30 | 广东轻工职业技术学院 | Possibility measurement calculating tree logic detection model optimization method |
| CN109961172A (en) * | 2018-12-28 | 2019-07-02 | 东南大学 | A kind of CPS rare event probability forecasting method examined based on statistical model |
| CN109961172B (en) * | 2018-12-28 | 2023-11-03 | 东南大学 | CPS rare event probability prediction method based on statistical model test |
| CN110062009A (en) * | 2019-05-21 | 2019-07-26 | 杭州逍邦网络科技有限公司 | A kind of formalization detection method of information physical emerging system defence |
| CN115801460A (en) * | 2023-01-16 | 2023-03-14 | 四川大学 | Power distribution information physical system security risk assessment method considering network attack vulnerability |
| CN115801460B (en) * | 2023-01-16 | 2023-04-11 | 四川大学 | Power distribution information physical system security risk assessment method considering network attack vulnerability |
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