=Paper=
{{Paper
|id=Vol-3628/short15
|storemode=property
|title=Cybersecurity Aspects of Smart Manufacturing Transition to Industry 5.0 Model
|pdfUrl=https://ceur-ws.org/Vol-3628/short15.pdf
|volume=Vol-3628
|authors=Taras Lechachenko,Ruslan Kozak,Yuriy Skorenkyy,Oleksandr Kramar,Olena Karelina
|dblpUrl=https://dblp.org/rec/conf/ittap/LechachenkoKSKK23
}}
==Cybersecurity Aspects of Smart Manufacturing Transition to Industry 5.0 Model==
https://ceur-ws.org/Vol-3628/short15.pdf
Cybersecurity Aspects of Smart Manufacturing Transition to
Industry 5.0 Model
Taras Lechachenko1, Ruslan Kozak1, Yuriy Skorenkyy1, Oleksandr Kramar1 and Olena
Karelina2
1
Ternopil Ivan Puluj National Technical University, 56 Ruska St, Ternopil, UA46001, Ukraine
2
Akamai Technologies Poland, Kraków, ul. Opolska 100, PL31-323, Poland
Abstract
Number and variety of smart manufacturing systems using digital twins for virtualisation and
improved control of production are growing fast. Augmented reality interface may serve as an
enabler for human creativity inclusion into the product lifecycle and simultaneously contribute
to the worker well-being in spirit of the Industry 5.0 principles. Such an integration of a human
into industrial platforms requires careful conceptualisation and development of the secure-by-
design augmented reality-enhanced interface for industrial digital twins, which is the focus of
this research. Threat modeling and vulnerabilities prioritization for AR-enabled industrial
digital twins compliant with the Industry 5.0 are performed by an analytical hierarchy process
within STRIDE threat modeling methodology using the TODIM method. The security controls
and mitigation actions have been identified, the respective threats and vulnerabilities were
ranked to optimize decision-making for the AR-enabled industrial digital twin design.
Keywords 1
Industry 5.0, Industrial Digital Twin, Augmented Reality, IoT Security, Threat Modeling
1. Introduction
Present-day industrial digital twins (IDT) are tools in digitization and optimization of various
industrial systems [1-6], and may benefit from such novelties as augmented reality (AR) and virtual
reality (VR) technologies [7]. Realistic 3D models of products and equipment can be interacted with
and may have rich functionality. Implementation of these technologies offer unique benefits for smart
manufacturing as they can be used to model, control and improve the production processes, enhance
knowledge transfer and collaboration of employees. The industrial internet of things (IIoT) gives
manufacturers a comprehensive view of the current state of the production line, characterizes and
controls the ongoing processes in real time.
Smart manufacturing is expected to assure high performance to justify investments done for
designing, operating and protection of IIoT. In the transformation, digital twins [8] offer many benefits
but also pose some challenges, such as ensuring security, privacy, and ethical standards, as well as
dealing with the complexity and accuracy of the models. Importantly, with the widespread use of smart
technologies in various domains, ensuring information security becomes vital. Software architecture
and information security measures are to be designed appropriately to protect both businesses and
individuals from data breaches that can have serious consequences.
Smart manufacturing can provide a variety of data, including physical material data and visual data,
process control data and machine data, etc. These data types are to be clearly distinguished, as they
require different mechanisms for harvesting, transmitting, pre-processing and storage. To securely
1
Proceedings ITTAP’2023: 3rd International Workshop on Information Technologies: Theoretical and Applied Problems, November 22–24,
2023, Ternopil, Ukraine, Opole, Poland
EMAIL: taras5a@ukr.net (A. 1); ruslan.o.kozak@gmail.com (A. 2); skorenkyy.tntu@gmail.com (A. 3); kramaroitntu@gmail.com (A. 4),
okarelin@akamai.com (A.5)
ORCID: 0000-0003-1185-6448 (A. 1); 0000-0003-1323-0801 (A. 2); 0000-0002-4809-9025 (A. 3); 0000-0002-8153-2476 (A. 4), 0000-0002-
5628-9048 (A.5)
©️ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
manage data from the cyber-physical system of low-resource IoT devices, the streaming large-scale
data exchange platform has to be properly designed [9]. Information security and privacy protection,
which involve ensuring that data is kept confidential, immutable and accessible, and preventing
unauthorized access and manipulation, become the critical requirements and deserve special attention
in the context of Industry 5.0.
2. AR-Enhanced Digital Twin Design for Smart Manufacturing
In transition to the Industry 5.0 model, a manufacturing company necessarily implements a human-
centered approach into all processes. This includes modeling, engineering, production and management,
as well as decision support systems [10, 11]. Human factor determines effectiveness at both design level
and operational level, thus setting specific requirements to technological development of the
manufacturing facility. Among different aspects, which are to be taken into account, the cognitive ones
for human operators and decision-makers are of the utmost importance. These determine, i.a.,
performance of the manufacturing facility, quality of the product, well being and psychological
satisfaction of the personnel. It is crucial to provide, along with collaborative solutions in the workplace,
efficient and intuitive interfaces for human-machine interaction. As such an interface, the AR-enabled
one has unmatched potential [12]. Such an AR interface can be an indispensable enabler for digital twin
implementation (Figure 1) and control of the physical equipment in the factory floor in real time [13,
14].
Figure 1: Block diagram representation of a production facility with AR-enhanced industrial digital
twin.
It may superimpose an information layer with characteristics, not accessible to a human perception
but provided to the industrial digital twin by IoT sensors and display in a timely manner the analytical
layer important for informed decision making. Controls may be integrated into AR interface to steer
production equipment with embedded IoT actuators [15, 16]. AR interface is also a good solution for
making the diffusion of knowledge potential [17-19] more smooth and natural from one professional to
another and enable collaboration in diverse teams. The feedback in trainee-trainer interaction [18] will
be immediately put in context due to connection of the AR-module to both digital twin and the content
of the knowledge base.
We consider modules of the digitized industrial platform and processes of its interaction with the
user and the knowledge base, shown in Figure 2, a minimal necessary set for a smart manufacturing
facility. The detailed composition of these modules may differ, depending on the production system
specification and the stakeholders’ requirements, however, this block diagram allows both designing
the software architecture and analyzing inherent vulnerabilities to mitigate risks inherent to IIoT
components and systems.
Figure 2: Interconnections within smart manufacturing AR-enhanced digital twin.
3. Cybersecurity Analysis for Smart Manufacturing Digital Platform
As already mentioned, the security layer for an industrial digital twin in cloud/edge environments is
to be carefully designed and properly developed. Protection of the IDT as a whole and each IoT device
in particular requires addressing multiple information security and cybersecurity threats. Harvested data
and processed information in IDT is a valuable business asset, therefore proper security measures are
to be designed and enacted.
Since the digital twin operates with sensitive data and privacy data as part of cyber-physical systems,
best security practices that are in compliance with industry standards and laws should be adopted by
default. One of the most crucial phases of the system development life cycle, secure-by-design implies
that security requirements must be identified in order for engineers to create a high-quality,
economically viable, and secure system.
In the information security and cyber security domains, threat modeling is a method for determining
security needs. It allows the identification of security requirements, finding threats and vulnerabilities,
assessing their impact and severity thus making possible the prioritization of viable solutions and
measures. A range of applications of this method includes software and networks, IoT components and
industrial processes. The STRIDE [20] threat modeling methodology has been used to identify and
characterize threats and vulnerabilities inherent to the IDT and to personal data of users. We have
composed the general data flow diagram and the threat model shown in Figure 3 for the industrial data
platform architecture depicted in Figure 2, for which applications and technologies are examined in
paper [21].
For the purposes of this research the specific group of elements (marked in red) have been identified
to be analyzed to address extended attack surfaces that IDT and IoT devices might face due to
incorporating AR-layer into IDT architecture. It has been studied whether there are threats and risks to
the industrial data platform and the data processed in the system imposed by the integrated AR-layer.
Table 1 summarizes descriptions of the corresponding threat and mitigation measures. The suggested
countermeasures will help software engineers and security experts in the processes of design and
improvement of the industrial data platforms.
The types of security threats have been identified for each element of the specific group within the
IDT architecture data flow diagram along with countermeasures that should be put into place to mitigate
security risks following the STRIDE methodology. While the STRIDE methodology has been used as
a high-level approach to identify the threats and define respective countermeasures, the OWASP IoT
Top Ten might be leveraged from low-level perspective to model specific security threats and risks as
well as to guide the selection of tests used to evaluate IoT attack surfaces and associated vulnerabilities
[22].
Considering the Digital Twin architecture data flow diagram depicted on Figure 3, we adapted the
OWASP IoT Top Ten and identified the groups of security practices and controls called to mitigate
security threats and risks the IoT devices might encounter.
Figure 3: Data flow diagram for AR-enabled Industrial Digital Twin architecture.
A left-shift approach to information security in the development and use of IDT and IoT devices
helps ensure that sensitive data and privacy-related information are protected against the ever-increasing
threat of cyber-attacks targeting the IoT-empowered industrial systems. The solution provides the
necessary traceability in cyber security and privacy audits to demonstrate compliance with the relevant
regulations.
Table 1
Security threats and countermeasures for the designed industrial data platform
Type of Security threat Analyzed components Proposed countermeasures
Encryption usage,
AR interfaces Strong cryptographic protocols:
Spoofing (claiming a IoT Devices Interfaces, PGP, AES, SHA-2, TLS 1.2 / 1.3,
false identity ) Digital Twin services, Strong authentication
User mechanisms: MFA, biometric auth,
certificate pinning, OAuth
IoT Device Command / Raw Data, Security Labeling,
Tampering Data for AR Device / Control, Secure communication protocols,
(malicious AR Interface Data / Control Proper authorization
modifications of data AR interfaces, mechanisms,
or process) IoT Devices Interfaces, Data hashing and signing
Digital Twin services
Repudiation AR interfaces
(denial of taking an IoT Devices Interfaces, Logging and audit trails
action or recognising Digital Twin services,
an event occurrence) User
Information AR interfaces Proper authorization
Disclosure IoT Devices Interfaces, mechanisms,
(leakage of the Digital Twin services, Encryption usage,
sensitive data) IoT Device Command / Raw Data, Strong cryptographic protocols: PGP,
Data for AR Device / Control, AES, SHA-2, TLS 1.2 / 1.3,
AR Interface Data / Control Secure coding best practices
Denial of Service AR interfaces
(unavailability of an IoT Devices Interfaces,
Digital Twin services, Antimalware software, Security
asset, service or
IoT Device Command / Raw Data, applications, Redundancy
network resource for
purposive users) Data for AR Device / Control,
AR Interface Data / Control
Proper authorization
Elevation of mechanisms,
AR interfaces
Privilege (gaining Principles of least privilege,
IoT Devices Interfaces,
unauthorized access Logging and audit trails,
Digital Twin services
or privileges) Access certification
Table 2 captures the corresponding vulnerability descriptions and mitigations. By understanding
these vulnerabilities and implementing effective mitigation strategies, engineers can build a robust
security posture that protects their IoT ecosystems.
Implementing additional security controls to handle the extended attack surface for IDT and IoT
devices will require the prioritization of the proposed countermeasures and mitigation actions. The
following chapter provides the approach of tradeoff decision-making regarding the most valuable
security controls during the development process of secure-by-design smart manufacturing which is
empowered by AR-equipments.
Table 2
Security vulnerabilities and mitigations for IoT devices
Vulnerability Mitigation action
Network isolation for IoT devices.
Insecure network
Periodic vulnerability assessment.
services
Secure network protocols.
Strong authentication of IoT endpoints.
Insecure ecosystem
Access control to sensitive APIs and interfaces.
interface
Encrypted communication channels between IoT devices / ecosystem.
Updating and patching all software and components used in IoT devices.
Lack of secure
Vulnerability monitoring components used in the IoT ecosystem.
update mechanism
Hold back from the legacy technologies.
Use of insecure Updating and patching all software and components used in IoT devices.
or outdated Vulnerability monitoring components used in the IoT ecosystem.
components Hold back from the legacy technologies.
Insecure data Using encryption to protect sensitive data during transmission and storage.
transfer and storage Using secure protocols.
IoT devices integration with asset management, bug tracking and patch
Lack of device
management systems.
management
Unique device credentials and enforcing access controls.
Insecure default Changing default configurations during initial IoT devices setup.
settings Disabling unnecessary services and ports.
4. Multi-criteria decision making based on AHP and TODIM methods
Ranking of components captured on the data flow diagram (Figure 3) in terms of the STRIDE threat
model was conducted using the TODIM [24, 25] method and intuitionistic fuzzy sets [26]. The
evaluations for the TODIM method were provided by experts with a minimum of five years of
experience in the field of cybersecurity. Those experts have been asked to utilize a linguistic variable
scale presented in Table 3. For this purpose, linguistic variables on the ranking scale, as presented in
the paper [27], were modified, and the scale defined in Table 3 has been applied.
Table 3
Intuitionistic linguistic variables
Linguistic term IFNs
Critical Impact (CI) [1.00; 0.00; 0.00]
High Impact (HI) [0.85; 0,05; 0.10]
Medium-High Impact (MHI) [0.70; 0.20; 0.10]
Medium Impact (MI) [0.50; 0.50; 0.00]
Low-Medium Impact (LMI) [0.40; 0.50; 0.10]
Low Impact (LI) [0.25; 0.60; 0.15]
Miserable Impact (Msl) [0.00; 0.90; 0.10]
The algorithm of the TODIM [24] method is as follows. Let a1 ,a2 ,...am be a set of alternatives,
c1 ,c2 ,...cn be a set of criteria with their corresponding w1 ,w2 ,...cn weights satisfying the condition
n
wi 0,1 and wi 1 . We construct a matrix a dij mn , dij where represents the evaluation of
i 1
alternative ai ( i 1,2,...m ) based on criterion c j ( j 1,2,...n ) . Let's assume that wjk wj / wk are
the relative weights for each criterion c j , ct where wk max( wj ) k, j 1,2...,n . The TODIM
method consists of the following steps:
1. Normalization a dij into a dij .
m x m x
2. Calculation of alternative a i dominance over a t alternative based on criterion c j . In this case,
consider the factor as a mitigating factor for loss effects. Thus, the calculation is as follows:
n
( ai ,at ) j ( ai ,at )( i,t 1,2...,m )
j 1
n
wik ( dij d tj ) / w jk if d ij d tj 0
j 1 (1)
j ( a i ,at ) 0 if d ij d tj 0
1 (
n
w jk )( d ij d tj ) / w jk if d ij d tj 0
j 1
Where j ( ai ,at )( dij dtj 0 ) represents an advantage and j ( ai ,at )( dij dtj 0 ) represents a
loss.
3. Calculation of the overall evaluation by the formula:
m
m
( a ,a ) min ( a ,a )
i t i t
( ai ) t 1 1
(2)
m m
max ( ai ,at ) min ( ai ,at )
1 1
4. Selection of the best ( ai ) alternative with the highest value.
Table 4
TODIM ranking of components for the designed industrial data platform
Name of component Coefficient Position
Digital Twin Services 1 1
AR interfaces 0.92 2
IoT Devices Interfaces 0.87 3
AR Interface Data / Control 0.53 4
Data for AR Device / AR Device Command 0.35 5
IoT Device Command / Raw Data 0.14 6
User 0 7
Table 4 displays the ranking results of the AR-enabled Digital Twin system components with respect
to the STRIDE threats using the TODIM method. The results of ranking shows that major efforts and
activities within the secure-by-design approach should be made to the process components of IDT
architecture as well as to their respective security controls and mitigations. The implementation of the
countermeasures for the data flow and external user components might be deprioritized or delayed for
the next system release in case of tough budget or project timeline.
Another important separate task in the context of Industry 5.0 is the prioritization of vulnerabilities
for IoT (Table 2) devices, the implementation of which will enable engineers and data architecture
designers in the Industry 5.0 sector to proactively prevent their occurrence through the efficient
allocation of resources for their mitigation. Effective budget allocation in accordance with vulnerability
prioritization will determine the priority of allocating funds to tools and techniques for their reduction.
This work implemented the prioritization of vulnerabilities for IoT devices using the Analytic
Hierarchy Process (AHP) method developed by Thomas Saaty [23]. For the purpose of the vulnerability
prioritization, three experts with specialized education in the field of cybersecurity and a minimum of
5 years of professional experience within companies of this profile were meticulously selected. Table
5 displays vulnerability ranking assessments by three experts using the Analytic Hierarchy Process
methodology.
Table 5
AHP vulnerability ranking assessments for IoT
Vulnerability Expert 1 Expert 2 Expert 3
Insecure network services 0.0000037 0.0000096 0.0000010
Insecure ecosystem interfaces 0.3465259 0.4901828 0.7605106
Lack of secure update mechanism 0.0000095 0.0000104 0.0000126
Use insecure or outdated components 0.6237466 0.4901828 0.1396856
IInsecure data transfer and storage 0.0000005 0.0000010 0.0000019
Lack of device management 0.0000113 0.0000058 0.0000126
Insecure default settings 0.0297022 0.0196073 0.0997754
Table 6 presents the ranking of averaged pairwise comparison values from three experts in the AHP.
Applying the AHP method to the prioritization of vulnerabilities for IoT components within IDT
architecture revealed that the vulnerabilities such as the insecure ecosystems interfaces, useinsecure or
outdated components, and insecure default settings should be treated with the highest priority while
developing AR-enabled IDT systems.
Table 6
AHP ranking of vulnerabilities for IoT
Vulnerability Position Сoefficient
Insecure ecosystem interfaces 1 0.5324064
Use insecure or outdated components 2 0.4178717
Insecure default settings 3 0.0496949
Lack of secure update mechanism 4 0.0000108
Lack of device management 5 0.0000099
Insecure network services 6 0.0000048
Insecure data transfer and storage 7 0.0000012
5. Conclusions
Augmented reality interfaces have immense potential as an enabler for human creativity inclusion
into the product life cycle according to the Industry 5.0 principles. Augmented reality assets can support
virtualisation of manufacturing lines and further implementation of the industrial digital twins. This
may be accompanied with extension of the attack surface for the industrial data platform. Proper
implementation of a secure-based approach to the industrial digital twins design is to be considered a
priority.
An approach for developing a secure-by-design augmented reality-enhanced interface for industrial
digital twins is proposed. As a result of threat modeling the specific group of elements have been
analyzed to address extended attack surfaces that the IDT system might encounter due to incorporating
AR-layer into its architecture. Threat modeling and vulnerabilities prioritization for AR-enabled
industrial digital twins compliant with the Industry 5.0 are performed by an analytical hierarchy process
within STRIDE threat modeling methodology using the TODIM method. The security controls and
mitigation actions have been identified, the respective threats and vulnerabilities were ranked by using
AHP and TODIM methods to optimize decision-making for the AR-enabled digital twin design.
The prioritized vulnerabilities and implementing effective mitigation strategies will let engineers
build a robust digital twin ecosystem as well as aid security experts in speeding up and saving means
on the design and upgrade of the IoT-powered manufacturing systems and its constituent parts.
6. Acknowledgements
This work was partially supported by the European Institute of Technology through the project
“Smart Manufacturing Innovation, Learning-labs, and Entrepreneurship” (HEI grant agreement No
10044).
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