Knowledge Extraction in Cyber-Physical Systems Meta-models:

A Formal Concept Analysis Application

Yasamin Eslami

, Sahand Ashouri

, Chiara Franciosi

and Mario Lezoche

Ecole Centrale Nantes, Nantes, France

Politecnico di Milano, Milan, Italy

Université de Lorraine, CNRS, CRAN, Nancy, France

mario.lezoche@univ-lorraine.fr

Keywords: Cyber-Physical Systems, Meta-models, Industry 4.0, Formal Concept Analysis.

Abstract: Industry 4.0, also known as the “Fourth Industrial Revolution”, “smart manufacturing”, “industrial internet”

or “Factory of the Future” is a trend and highly discussed topic nowadays. Therefore, this topic drew attention

to research and practice and opened many doors to shed light on the future path of engineering approaches.

Cyber-Physical Systems (CPSs) play an important role as one of the core components in the industry 4.0

approach, as they connect the physical objects in production systems to the virtual ones. Indeed, CPSs are the

main sources in Industry 4.0 through which data can be transformed into information and consequently

extracted as knowledge. To be able to derive the required knowledge from the transformed information, it is

essential to excavate the concept of CPS and associate its characteristics by which the system is identified.

However, the current literature lacks a systematic study which analyses the characteristics of CPSs and the

relationships among them. And so forth, this study will focus on CPS meta-models and their characteristics.

Formal Concept Analysis (FCA), as a clustering technique, will be used to investigate any hypothetical

relationship among the characteristics.

1 INTRODUCTION

Industry 4.0 technologies related to Cyber-Physical

Systems (CPSs), Internet of Things (IoT), Big Data

and cloud computing can generate benefits and

positively contribute to the circular economy

paradigm since they allow design for circularity based

on the information gathered from customers as well

as through the whole production process. CPSs

represent more than networking and information

technology or even information and knowledge being

integrated into physical objects. By integrating

perception, communication, learning, behaviour

generation, and reasoning into such systems a new

generation of intelligent and autonomous systems

may be developed. A large-scale CPS can be

envisioned as millions of networked smart devices,

sensors, and actuators being embedded in the physical

world, which can sense, process, and communicate

the data all over the network. The proliferation of

technology-mediated social interactions via these

highly featured and networked smart devices has

allowed many individuals to contribute to the size of

Big Data available. The contextualised form of data

generated by CPS makes the data comprehended as

information, which makes CPSs, in the context of

Industry 4.0, a huge source of information which also

carries, often implicitly, relationships between the

environment and the working domain. This

information and relationships are dormant sources of

knowledge that must be extracted, formalised, and

potentially reused. To do so, it is necessary to extract

knowledge to better understand the characteristics of

the under-examination systems and the methods they

use to employ them according to their potential. This

dormant knowledge can be identified by using

different methods like clustering, relationships

extraction, concept frequency finding, and anything

related to the information retrieval domain.

Therefore, this study focuses on extracting

characteristics from various meta-models presented

in the literature. As the first step, and in investigating

the CPS characteristics, a thorough study of cyber-

physical system meta-models and the characteristics

has been done. The study was to discover more about

CPS knowledge representation in different scientific

domains like manufacturing processes, Informatics,

Eslami, Y., Ashouri, S., Franciosi, C. and Lezoche, M.

Knowledge Extraction in Cyber-Physical Systems Meta-models: A Formal Concept Analysis Application.

DOI: 10.5220/0011536700003329

In Proceedings of the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL 2022), pages 129-136

ISBN: 978-989-758-612-5; ISSN: 2184-9285

129

health, architecture and so on. During the study, two

main issues were investigated: (1) How are CPS

meta-models described and characterized? (2) How is

Knowledge represented in CPS meta-models? The

results were then analysed using the Formal Concept

Analysis (FCA) method to classify and discover

hidden relationships between the inner existing meta

model’s components. The FCA method, in this way,

gives the possibility to extract new implicit

knowledge.

The remainder of the paper is organized as

follows: the next section provides a literature

overview on the CPS meta-models. A short

description of the research methodology is presented

in section 3. Furthermore, section 4 presents the CPS

meta-models’ characteristics, while section 5

provides a clustering assessment of CPS

characteristics using the Formal Concept Analysis

method. A detailed discussion of the results is

presented in section 6. Finally, the main conclusions

of this study are provided in section 7.

2 LITERATURE OVERVIEW ON

CPS META-MODELS

CPS meta-models have been widely discussed in the

literature. They have been designed and proposed to

address various issues in the context of Industry 4.0.

(Yang Liu et al. 2017) thoroughly discusses the

characteristics and architecture of CPSs and then

investigates different research on Information

Processing of CPS, CPS Software Systems, CPS

System Security and CPS System Testbed. Studying

all, they conclude that the most called for challenge

in development of CPS is the limitation on existing

theory and technology of computation,

communications, and control technology.

Furthermore, (Vogel-Heuser et al. 2021) introduced a

comprehensive domain-specific language (DSL) to

design a meta-model to reduce the Cyber-Physical

Production Systems (CPPSs) downtime during the

operation of the glass bottles in a yogurt

manufacturing plant. The proposed DSL,

DSL4hDNCS, will address hardware/software

architectures or network-related delays and

uncertainties and will increase safety, calculation

power, and network transmission time. Therefore, it

can act as a unique method to support the formalized,

cross-disciplinary engineering of distributed CPPS,

including the description of real-time, safety, and

deployment aspects. After an investigation of the

structure of CPS, (Someswara Rao, Shiva Shankar,

and Murthy 2020) makes a comprehensive search on

different domain applications of CPS such as

handling energy, network security and data

transmission and management. Afterwards, they

briefly explored the models and methods driven for

the development of CPSs; domain-specific modelling

(DSM), the prominent model-driven development

(MDD) and model-integrated computing are a few to

mention. On the other hand, (Cheh et al. 2017)

categorizes the application domain of CPS into 10

main categories and discusses the work done in each

category. Agriculture, education, energy

management, environmental monitoring, medical

devices and systems, process control, security, smart

city and smart home, smart manufacturing and

transportation systems are the 10 groups CPSs are

discussed in the mentioned work. CPPS, its design

and application are the focal points of the study run

by (Wu, Goepp, and Siadat 2019). The 5C

architecture of CPS (Smart Connection Level, Data-

to-Information Conversion Level, Cyber Level,

Cognition Level and Configuration Level) is also

deeply discussed regarding the CPPS. (Maidl et al.

2021) defined a taxonomy for relevant attack actions

for the security of CPSs and formed the taxonomies

as a meta-model. This meta-model presents the ways

the taxonomy relates the attack action to the

endangered part of the cyber-physical system. In

addition, it prefilters the attack actions and documents

them in the threat model systematically. Therefore, it

can provide various visions of the threats for the

cyber-physical systems and manages to focus on the

relevant aspects for the verified task.

3 RESEARCH METHODOLOGY

The present study forms a state of the art based on

cyber-physical system metamodels, and the

characteristics represented. The focal point of the

study is based on CPS knowledge representation in

different scientific papers. To do the investigation, a

sequence of questions have been answered through

the work: ‘How CPS metamodels are described and

characterized?’, ‘How Knowledge is represented in

CPS metamodels?’ consequently, papers were

identiﬁed using a structured keyword search on major

databases and publisher websites (Scopus, Elsevier

and ScienceDirect). General keywords such as

“cyber-physical systems” and “metamodel” were

combined using AND. All the searches were applied

in the “Title, Keyword, Abstract” field. At this search

level, no exclusion area was considered, and all CPS

application areas and domains were studied. As for

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the content analysis, the material connection was

conducted as mentioned above and a systematic

analysis was run to assess the papers in terms of what

CPS characteristics are explicitly or implicitly

discussed.

4 OVERVIEW OF CPS

META-MODELS’

CHARACTERISTICS

CPSs are often engineered systems and are

differentiated from other types of engineered systems

as they are built on the integration of cyber and

physical components. It is, therefore, agreed upon

that CPS functionalities come from the tight

integration of the cyber and physical sides and create

CPS characteristics in different terms. On the other

hand, CPSs should be characterized by well-defined

components. They should provide components with

well-known characteristics described using

standardised semantics and syntax. Therefore,

defining and shaping key characteristics of CPSs will

pave the path to better development and

implementation management within and across

various domains of the CPS application (Griffor et al.

2017). However, the literature lacks a systematic

study of the characteristics of CPS meta-models, their

definition and whether there is a relationship among

them. Therefore, and to better investigate how CPS

meta-models are characterised and defined, the focus

point of the present study has been put on exploring

the CPS characteristics in the various domain in

scientific papers. The investigation will get even

deeper by trying to see if they are explicitly connected

or not.

Napoleone et. al (2020) discussed the

technological characteristics of CPSs in

manufacturing emergent from existing literature in

detail. They carried out a structured review to

investigate the CPS characteristics that have been

studied in scientific papers. In the end, they came up

with 19 most cited lower-order characteristics, and

then providing their literature-based descriptions and

explaining the reasoning, they aggregated them to

eight higher-order characteristics. Since the same

need can originate the present study, a base CPS

characteristic list was considered on account of their

work aiming at delineating CPS metamodels.

Therefore, the choice of the content analysis for our

work was established deductive, however, during the

procedure of analysing the papers and digging deeper

into the study, the list of the characteristics that were

gone through for the analysis was modified to what

can be seen in Table 1.

Table 1: CPS characteristics extracted for this study.

CPS Characteristics

Resiliency Modularity

Intelligence/sm

artness

Redundancy Autonomy Cooperation

Complexity Self-Capabilities Collaboration

Heterogeneity

Encapsulation

Integration

Reconfigurabil

Interoperability Virtualization Adaptability

Connectivity

Real-Time

abilit

Scalability

Networking

abilit

Computational

abilit

Diagnosability

Predictability Uncertainty Fault-tolerant

Composability Reliability

Safety and

Securit

Stability

5 CLUSTERING ASSESSMENT

ON CPS CHARACTERISTICS

USING THE FORMAL

CONCEPT ANALYSIS

METHOD

To investigating the main two issues of this study,

"(1) How are CPS metamodels described and

characterized?” and “(2) How is Knowledge

represented in CPS meta-models?” the papers were

gone through whether they discuss, implicitly or

explicitly, the CPS characteristics enlisted in the

previous section.

Hence, Formal Concept Analysis (FCA), as a

clustering technique, was chosen to help us first to

describe the CPS meta-models and then scrutinize the

CPS characteristics and the hidden relationship

between them in the chosen papers.

FCA is a branch of lattice theory (Wille 1982) and

it is best used for knowledge representation, data

analysis, and information management. It detects

conceptual structures in data and consequently

extraction of dependencies within the data by forming

a collection of objects and their properties (Wajnberg

et al. 2018). The FCA method starts with the input

data in a form of a matrix, in which each row

represents an object from the domain of interest, and

each column represents one of the defined attributes.

Knowledge Extraction in Cyber-Physical Systems Meta-models: A Formal Concept Analysis Application

131

Figure 1: Single clustering of CPS Characteristic.

If an object has an attribute, a mark (e.g., symbol "●")

is placed on the intersection of that object’s row and

that attribute’s column. Otherwise, the intersection is

left blank. The matrix is called the “formal context”

and for the present study it was formed as the papers

which implicitly or explicitly investigate the CPS

characteristics in their meta-model as the objects and

the characteristics of CPS as attributes. In general,

FCA results in two sets of output data: a hierarchical

relationship of all the established concepts in the form

of a line diagram called a concept lattice and a list of

all found interdependencies among attributes in the

formal context (Škopljanac-Mačina and Blašković

2014). The latter is what has been used for the

analysis of the CPS characteristics in the present

work.

Figure 1 represents the result of FCA on single

clustering of CPS characteristics. As it is clearly seen,

“Resiliency” was the one characteristic that stood on

the top of the list, with a noticeable difference from

the rest, as the most reflected characteristic in the

literature whether to be explicitly or implicitly

mentioned. Characteristics like “Fault-Tolerant”,

“Diagnosability”, “Redundancy” and “Safety and

Security” come next in the list with a noticeable

difference between Resiliency and ignorable

divergence among themselves. On the other hand,

characteristics like “Reconfigurability”,

“Collaboration”, “Controllability”, and “Self-

Capabilities” are at the end of list, which does not

refer to the lack of importance on the characteristics

though. The main reason might mostly be that they

are the characteristics that are fundamental and taken

for granted in the design and application of CPSs.

Figure 2 on the other hand, shows what was

extracted from the coupling demonstration of

characteristics in the analysed papers through FCA.

Going through the results, the combination of

Resiliency with other characteristics are the ones been

observed the most, which was somehow predictable

by the analysis of the single characteristics. However,

the pair of {Resiliency; Redundancy}, {Resiliency;

safety and security},{Resiliency; Fault-Tolerant} and

{Resiliency; diagnosability} are at the top-ranking

respectively which one way or another can show the

close relationship between the concepts; the outcome

that establishes the backbone of the upcoming

discussion.

As it has been described above, FCA is a

conceptual framework that can make data more

understandable. It is based on the lattice theory and

defines a formal context to represent the relationship

between objects and attributes in the studied domain.

In addition to what was formerly explained, FCA

employs association rule mining which is a method

for discovering interesting relations between

variables.

0 5 10 15 20 25 30 35 40

{reconfigurability}

{Controlability}

{Modularity}

{Reusability}

{Autonomy}

{stability}

{Computational capability}

{Real-time capability}

{heterogeneity encapsulation}

{predictability}

{Interoperability}

{diagnosability}

{safety and security}

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Figure 2: Double clustering of CPS Characteristics.

Table 2: CPS characteristics extracted for this study.

# antecedent => consequence support confidence

1 Resiliency => Redundancy 27.02% 52.63%

2 Resiliency => diagnosability 24.32% 47.36%

3 Resiliency => Fault-Tolerant 21.62% 42.10%

4 Resiliency => safety and security 29.72% 57.89%

5 Fault-Tolerant => Resiliency 21.62% 84.21%

6 diagnosability => Resiliency 24.32% 85.71%

7 safety and security => Resiliency 29.72% 95.65%

8 Redundancy => Resiliency 27.02% 100.00%

0 5 10 15 20 25

{Integration; Intelligence/smartness}

{Autonomy; Integration}

{Reusability; Integration}

{Connectivity; collaboration}

{heterogeneity encapsulation; communication}

{stability; Uncertainty}

{Real-time capability; Reliability}

{Integration; Real-time capability}

{Connectivity; predictability}

{Connectivity; Computational capability}

{communication; Real-time capability}

{Interoperability; predictability}

{Interoperability; Real-time capability}

{Interoperability; communication}

{heterogeneity encapsulation; Intelligence/smartness}

{heterogeneity encapsulation; Real-time capability}

{Complexity; Computational capability}

{Complexity; communication}

{Fault-Tolerent; Interoperability}

{Resiliency; Autonomy}

{Intelligence/smartness; diagnosability}

{Integration; Computational capability}

{communication; diagnosability}

{Complexity; heterogeneity encapsulation}

{Fault-Tolerent; Intelligence/smartness}

{Resiliency; Intelligence/smartness}

{communication; predictability}

{Uncertainty; heterogeneity encapsulation}

{communication; Reliability}

{Complexity; Interoperability}

{Resiliency; Computational capability}

{communication; safety and security}

{Interoperability; Connectivity}

{Resiliency; stability}

{Resiliency; Real-time capability}

{Resiliency; Reliability}

{Resiliency; diagnosability}

{Resiliency; safety and security}

Knowledge Extraction in Cyber-Physical Systems Meta-models: A Formal Concept Analysis Application

133

Let I = {i_1,i_2,...,i_n} be a set of n binary

attributes called items. Let D = {t_1,t_2,...,t_m} be a

set of transactions called the database. Each

transaction in D has a unique transaction ID and

contains a subset of the items in I. A rule is defined as

an implication of form X ⇒ Y where X,Y ⊆ I and X

∩ Y = ∅. The sets of items (for short itemset) X and

Y are called antecedent and consequent of the rule

(Hornik, Grün, and Hahsler 2005). The defined rule

can mean that if X is chosen then it is likely that Y is

also selected. However, to be able to extract rules

measures are defined to help the process of decision

making. The best-known measures are Support and

confidence (Y. Liu and Li 2017) that are used in the

present study. The support supp(X) of an itemset X is

defined as “the proportion of transactions in the data

set which contain the itemset.” For example, if the

support of itemset X is 0.4 it means that the itemset

occurs in 40% of all transactions. On the other hand,

the confidence of a rule is defined conf (X ⇒ Y) =

supp (X ∪ Y )/supp(X) and can be interpreted as “an

estimate of the probability P(Y |X), the probability of

finding the antecedent of the rule in transactions

under the condition that these transactions also

contain the consequent”. For example, if the conf (X

⇒ Y) = 0.5, it means the rule X ⇒ Y is correct in 50%

of the transactions containing X and Y (Hornik et al.,

2005). However, the aim is to find frequent itemset

(the CPS characteristics in the present study) and the

probability of the frequency. To serve the purpose,

the software LATTICE MINER 2.0 was adopted on

the result of the analysis done. The association rules

between the selected CPS characteristics were

extracted considering the minimum support level as

20% and minimum confidence level as 20% and

shown in Table 2. The minimum levels were defined

by a try and error procedure.

Looking through the association rules, the

probability of achieving resiliency through fault

tolerant, diagnosability, safety and security and

finally redundancy goes over 84% which itself

confirms the result for the first step in FCA. It also

worth noting that, resiliency is in all the itemset that

have support levels above 20% and a confident of

50% and above.

6 DISCUSSION ON THE

RESULTS

With reference to the results of FCA achieved in the

previous part, resiliency draws the attention to itself

among other characteristics. Going through the

papers that have investigated the characteristics, 68%

of the papers were recently published (2014 forward)

among which 31% is dedicated only to the interval of

2018-2019 which shows the high rise of the

importance of the concept in the literature. Different

terms were used and established in the literature to

refer to a CPS be ‘resilience’ such as survivable (Wan

and Alagar 2014) or Fail-safe (Chemashkin and

Zhilenkov 2019).

Furthermore, the present study investigated the

CPS characteristics considered and studied in the

papers, whether the characteristic and their effect

were explicitly or implicitly discussed in the scientific

papers. To name a few, Lezoche and Panetto (2018)

tried to reach resiliency through modelling the

functions and also the links between the components

of the meta-model by the help of FCA. Looking at the

hierarchical inclusion of the CPS meta-model and

thanks to the created lattice, they could find control

over redundancy and therefore elevate resiliency of

the system. Sangiovanni-Vincentelli et al., (2012)

addressed the systems engineering of cyber-physical

Contract-Based Design by employing structured and

formal design methodologies to finally increase the

reliability and consequently the resiliency of the CPS

meta-model. Although Zhao and Rao (2017) did not

mention resiliency directly as an objective of their

study, they have had it implicitly targeted through an

integration of the physical layer, the network layer

and the business layer, which finally leads to a better

investigation of the hardware status information,

software, patches and other information to

perception, acquisition and control. The integration

results in a platform by which controllability,

diagnosability and fault-tolerant of the CPS is

increased which will be directed to more survivability

of the system.

Given the importance of the concept, different

paths were taken to reach and increase the resiliency

of a CPS. Due to the results observed, the main two

tracks were passed over the two characteristics:

‘safety and security’ and ‘fault-tolerance’. For

example, (Bakirtzis et al. 2020) believes that only by

unifying safety, security and resiliency it is possible

to reach adaptable and dynamic design patterns that

are able to take into account the intended functions of

a system. Chemashkin and Zhilenkov (2019)

explored fault tolerant control systems (FTCS) and

mentioned that they are able to withstand the failures

and errors of the components of the system itself and

to preserve the system performance to the maximum,

therefore they can survive and be resilient.

Digging a bit deeper, resiliency of a system was

thrown together with recognizing different defies and

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134

risks along with defining proper metrics to protect the

endangered system and estimating plant states in spite

of attacks (Na, Park, and Eun 2019; Lezoche and

Panetto 2018). Encountering such observations,

brought about another level of attempts to elevate

resiliency of the system: revolving around

characteristics like predictability and diagnosability

which also stood at the high ranks of the FCA double

clustering.

Redundancy and reliability were also the

characteristics that coupled well with resiliency in

FCA and were also discussed closely with the concept

in the literature. As mentioned by (Na, Park, and Eun

2019), redundancy is the principle that can be

advantageous in estimating resiliency in majority of

the systems. On the other hand, the intention of

redundancy in the system can be increasing its

reliability since it relies on employing multi-pronged

solutions rather than a single technique which also

improves the security and resiliency of the system

(Lezoche and Panetto 2018).

In addition to all, stability was also a characteristic

that was paid attention to on reaching safety, security

and consequently the resiliency of the system since

fast reconfiguration of attacks can lead to maintaining

the stability of the system which keeps it safe and

helps it retain normal operation (Potteiger, Zhang,

and Koutsoukos 2020).

7 CONCLUSIONS

The paper presented a study on Cyber Physical

Systems meta-models and their representative

characteristics. To this extent, two main steps were

taken to find out about ‘How are CPS metamodels

described and characterized?’, and ‘How is

Knowledge represented in CPS metamodels?’

through which CPS meta-models were profoundly

investigated regarding what characteristics they are

designed to mirror in the metamodels.

Implementing Formal Concept Analysis (FCA) as

the clustering technique, the most aimed

characteristics in CPS meta-models were studied.

Due to the results, “resiliency” was the dominant

characteristic that was targeted implicitly or explicitly

in the scientific paper. “Fault-Tolerant”,

“diagnosability”, “redundancy” and “safety and

security” were the ones followed resiliency in the list

but with noticeable difference. Implementing the

association rules by the clustering technique has also

confirmed the results and showed that with a

probability of 85% and above, resiliency is the one

characteristic looked for in CPS meta-model,

implicitly or explicitly.

In a sequel, the work makes a contribution in the

concept of Cyber Physical Systems characteristics in

a way that it not only lists the characteristics that has

been studied implicitly or explicitly in meta-model

constructions, it also takes care of the road map to the

most focused characteristic in CPS metamodels.

Thanks to FCA and its association rules, it was

possible to find the hidden relationship between the

characteristics that mainly characterize the CPS meta-

model.

The present work can be an initial point of

development of a CPS-family metamodel. The goal is

to improve the actual metamodel with the dynamic

part and all the inner semantics that is mandatory for

an evolutive and adaptive CPS.

REFERENCES

Bakirtzis, Georgios, Tim Sherburne, Stephen Adams, Barry

M. Horowitz, Peter A. Beling, and Cody H. Fleming.

2020. ‘An Ontological Metamodel for Cyber-Physical

System Safety, Security, and Resilience

Coengineering’. ArXiv:2006.05304 [Cs], June.

http://arxiv.org/abs/20 06.05304.

Cheh, C., K. Keefe, B. Feddersen, B. Chen, W.G. Temple,

and W.H. Sanders. 2017. ‘Developing Models for

Physical Atacks in Cyber-Physical Systems’. In , 49–

55. https://doi.org/10.1145/3140241.3140249.

Chemashkin, F.Y., and A.A. Zhilenkov. 2019. ‘Fault

Tolerance Control in Cyber-Physical Systems’. In,

1169–71. https://doi.org/10.1109/EIConRus.2019.8656

639.

Griffor, Edward R, Chris Greer, David A Wollman, and

Martin J Burns. 2017. ‘Framework for Cyber-Physical

Systems: Volume 1, Overview’. NIST SP 1500-201.

Gaithersburg, MD: National Institute of Standards and

Technology. https://doi.org/10.6028/NIST.SP.1500-

201.

Hornik, Kurt, Bettina Grün, and Michael Hahsler. 2005.

‘Arules - A Computational Environment for Mining

Association Rules and Frequent Item Sets’. Journal of

Statistical Software 14 (October). https://doi.org/

10.18637/jss.v014.i15.

Lezoche, M., and H. Panetto. 2018. ‘Cyber-Physical

Systems, a New Formal Paradigm to Model

Redundancy and Resiliency’. Article in Press.

Enterprise Information Systems. Scopus.

https://doi.org/10.1080/17517575.20 18.1536807.

Liu, Y., and X. Li. 2017. ‘Application of Formal Concept

Analysis in Association Rule Mining’. In, 203–7.

https://doi.org/10.1109/ICISCE.2017.52.

Liu, Yang, Yu Peng, Bailing Wang, Sirui Yao, and Zihe

Liu. 2017. ‘Review on Cyber-Physical Systems’.

Knowledge Extraction in Cyber-Physical Systems Meta-models: A Formal Concept Analysis Application

135

IEEE/CAA Journal of Automatica Sinica 4 (1): 27–40.

https://doi.org/10.1109/JAS.2017.7510349.

Maidl, M., G. Münz, S. Seltzsam, M. Wagner, R. Wirtz, and

M. Heisel. 2021. ‘Model-Based Threat Modeling for

Cyber-Physical Systems: A Computer-Aided

Approach’. Communications in Computer and

Information Science 1447: 158–83. https://doi.org/

10.10 07/978-3-030-83007-6_8.

Na, G., J. Park, and Y. Eun. 2019. ‘Attack Resilient State

Estimation by Sensor Output Coding’. In, 2019-

October:1015–20. https://doi.org/10.23919/ICCAS474

43.2019.8971675.

Napoleone, A., M. Macchi, and A. Pozzetti. 2020. ‘A

Review on the Characteristics of Cyber-Physical

Systems for the Future Smart Factories’. Journal of

Manufacturing Systems 54: 305–35.

https://doi.org/10.1016/j.jmsy.2020.01.007.

Potteiger, B., Z. Zhang, and X. Koutsoukos. 2020.

‘Integrated Moving Target Defense and Control

Reconfiguration for Securing Cyber-Physical

Systems’. Microprocessors and Microsystems 73.

https://doi.org/10.1016/j.micpro.2019.102954.

Sangiovanni-Vincentelli, Alberto, Werner Damm, and

Roberto Passerone. 2012. ‘Taming Dr. Frankenstein:

Contract-Based Design for Cyber-Physical Systems*’.

European Journal of Control 18 (3): 217–38.

https://doi.org/10.3166/ejc.18.217-238.

Škopljanac-Mačina, Frano, and Bruno Blašković. 2014.

‘Formal Concept Analysis – Overview and

Applications’. Procedia Engineering, 24th DAAAM

International Symposium on Intelligent Manufacturing

and Automation, 2013, 69 (January): 1258–67.

https://doi.org/10.1016/j.proeng.2014.03.117.

Someswara Rao, C., R. Shiva Shankar, and K.V.S. Murthy.

2020. ‘Cyber-Physical System—An Overview’. Smart

Innovation, Systems and Technologies 160: 489–97.

https://doi.org/10.1007/978-981-32-9690-9_54.

Vogel-Heuser, B., E. Trunzer, D. Hujo, and M. Sollfrank.

2021. ‘(Re)Deployment of Smart Algorithms in Cyber-

Physical Production Systems Using DSL4hDNCS’.

Proceedings of the IEEE 109 (4): 542–55.

https://doi.org/10.1109/JPROC.2021.3050860.

Wajnberg, Mickaël, Mario Lezoche, Alexandre Blondin-

Massé, Petko Valchev, Hervé Panetto, and Louise

Tyvaert. 2018. ‘Semantic Interoperability of Large

Systems through a Formal Method: Relational Concept

Analysis’. IFAC-PapersOnLine, 16th IFAC

Symposium on Information Control Problems in

Manufacturing INCOM 2018, 51 (11): 1397–1402.

https://doi.org/10.10 16/j.ifacol.2018.08.330.

Wan, Kaiyu, and Vangalur Alagar. 2014. ‘Context-Aware

Security Solutions for Cyber-Physical Systems’.

Mobile Networks and Applications 19 (2): 212–26.

https://doi.org/10.1007/s11036-014-0495-x.

Wille, Rudolf. 1982. ‘Restructuring Lattice Theory: An

Approach Based on Hierarchies of Concepts’. In

Ordered Sets, 445–70. NATO Advanced Study

Institutes Series. Springer, Dordrecht.

https://doi.org/10.1007/978-94-009-7798-3_15.

Wu, X., V. Goepp, and A. Siadat. 2019. ‘Cyber Physical

Production Systems: A Review of Design and

Implementation Approaches’. In, 1588–92.

https://doi.org/10.1109/IEEM44572.2019.8978654.

Zhao, Y., and Y. Rao. 2017. ‘A CPS-Based Intelligence-

Awareness Platform for IT Service Management’. In,

2017-January:6668–73. https://doi.org/10.1109/CAC.

20 17.8243978.

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