Distributed Digital Twins are designed to enhance the intelligence, predictability, and optimization of industrial assets by actively engaging, synchronizing, and collaborating with their physical counterparts, i.e., the systems they model, in near real time. This interoperability allows for seamless connections between real systems and their virtual counterparts, thereby facilitating the flow of data while aggregating vital information for comprehensive insights across large entities. However, the constant exchange of data and dependency on the information technology and operations technology process integrations in these complex distributed systems give rise to various cyber-security challenges. These include threats to data, unauthorized accesses, as well as threats to the integrity and reliability of the digital tools and the services they offer, among others. In this paper, we discuss the relevant cyber-threats within distributed Digital Twins ecosystems, which we then analyze while outlining different strategies to mitigate such threats. As a result, we present key takeaways toward a secure and reliable Digital Twin platform. Finally, different challenges are raised to highlight the status quo on the security of Digital Twins and areas for improvement.
According to the initial goals of Industry 4.0, the integration of new information technology (IT)
and operation technology (OT) paradigms into manufacturing, healthcare, automation, production, and
logistics is currently underway [
Up to this point, the practical usefulness of DTs has been demonstrated in several situations, such
as industry [
To shed more light on possible solutions regarding the plethora of cyberthreats that can inflict irreparable damage to DTs, in this paper, we analyze the different cyberthreats within the DT ecosystem while outlining some recent mitigation strategies. Moreover, we itemize key takeaways from our exploration to ensure a secure and hitch-free deployment of DTs. In light of this, the remaining sections of the paper are organized as follows: Section 2 provides background on the use of DT in industrial domains as well as the role of cybersecurity in such DT in industrial environments. Section 3 discusses the potential cybersecurity threats in industrial DT while delineating the recent strategies put forward to mitigate such threats. Section 4 provides key takeaways from this overview to pave the way for future research. In addition, different challenges will be given in Section 5, while Section 6 finally draws the conclusion.
Since the Second World War, when computer simulation was first used [17], it has developed and
become a powerful tool and a key enabler in productivity, safety, optimization, and decision making in
industry today. In the early 2000s, Grieves introduced the concept of digital DT, a more complex near
real-time simulation model, representing the actual physical system in the virtual world throughout its
lifecycle [18]. From the time when Digital Twin was first defined, it has taken different definitions
depending on the application and the specific use cases. The three essential characteristics in these
definitions are: the physical object in the real world, the virtual representation of the object, and the
bidirectional exchange of data between the two objects [19]. In general, a Digital Twin can be defined
as “a virtual representation of a physical system (and its associated environment and processes) that is
updated through the exchange of information between the physical and virtual systems” [20]. Moreover,
depending on the automation level of data exchange between the two systems, DTs can also be further
categorized as digital models (manual data flow), digital shadows (one-way automatic data flow), and
Digital Twins (complete automatic data flow) [
Technological advancements, especially in Internet of Things (IoT) and sensor technologies, have aided in realization of DT with automatic data flow as evident by the rapid growth in research and application of DTs during the past decade [21]. Today, many industries benefit from the implementation of DTs such as: aerospace, energy, agriculture, and manufacturing [22, 23]. The aerospace field, driven by NASA, was the first industry to use DTs in performance optimization [24]. Energy and healthcare industries have also utilized the capabilities of DTs for maintenance monitoring [25], and productivity improvements [26]. More significantly, the vast applications of DTs in manufacturing industry show DTs‘ pivotal role as a key catalyst in moving the industry towards smart manufacturing and Industry 4.0 [27]. DTs facilitate manufacturing testing and validation [28], as well as automation integration [29] leading to production optimization and reliability improvements. Figure 1 illustrates DT’s concept. 2.2.
As increasing number of industries transition to the digital environment and integrate their operations online, cybersecurity is expanding into a more comprehensive and broader discipline. This indicates that, apart from cybersecurity representing an industry on its own, there are cybersecurity requirements in virtually every industrial sector. The notion of security has evolved beyond the typical hacker symbolism of constantly crafting malicious code and breaking into systems; cybersecurity as a discipline now incorporates a vast range of interdependent skills that collaborate harmoniously. These skills include information security, network security, digital forensics, cloud security, critical infrastructure security, and application security, among others.
With the growing interconnectedness of IT-OT paradigms in industry, the potential vulnerabilities to
cyberattacks also expands. It is worth noting that such IT-OT integration is exacerbated by the
transformation of traditional industrial manufacturing systems into contemporary industrial
cyberphysical systems, which consequently necessitates the adoption of new technologies such as Digital
Twins that are complex and distributed in nature. Reports on industrial risk evaluations indicate that
this transformation has resulted in the emergence of several novel threats [30], which has further been
proven by the recent analysis on the distribution of cyberattacks across industries worldwide [31].
According to the study, manufacturing sector experienced the greatest proportion of cyberattacks
among the world's leading industries in 2022, with cyberattacks targeting manufacturing industries
comprising approximately 25% of the overall attacks during the year under study, with a total global
market size expected to project at an annual compound growth rate of 12.3% from 2023 to 2030 [32].
To establish an intelligent manufacturing setting based on automated decision making and problem
solving according to Industry 4.0 requirements, it is necessary to use innovative technologies that can
provide spontaneous connections between all industrial tools and devices within a manufacturing
ecosystem and the web, such as distributed DT, AI, IIoT, CPS, big data, etc. Moreover, Industry 4.0
promotes the use of these technologies to facilitate decentralized communication between systems
rather than just depending on conventional cloud servers or other centralized frameworks [
Recent works on cybersecurity vulnerabilities in various industries and their possible defense mechanisms include the works of Aoun et al. [35], where the authors explore the future prospects of blockchain technology as a means of strengthening I4.0 industrialization. With the addition of novel functionalities, they determine which domains of blockchain technology can enhance the implementation and efficiency of I4.0, especially in terms of cybersecurity. In [36], the authors elucidate the need for adopting a dynamic cybersecurity approach in response to the demands of Industry 4.0. They analyze the limitations of present techniques and highlight the growing significance of integrating novel approaches.
Smart manufacturing
AI, cobots, IIoT, DT, Augmented reality, etc IT
Convergence • Data analytics • Network connectivity • Data processing • Computing devices • Storage devices • Event management
OT
Laghari et al. [37] present a security system that utilizes digital signatures to provide authentication, integrity, and defense against cyber breaches. The method is evaluated based on industrial Semiconductor Equipment Communication Standard/Generic Equipment Model (SECS/GEM) design and implementation protocols [36] against cyberattacks. The findings suggest that SECS/GEM successfully blocked unauthorized organizations from initiating connections with legitimate industrial devices while also ensuring the integrity of communications by rejecting fake signals. Likewise, attacks such as replay, Denial-of-Service, and False-Data-Injection are mitigated by the SECS/GEM. In [38], Ghimire et al. adopt a federated learning framework for cybersecurity within the IoT, with a main emphasis on security. They also provide different advances to tackle performance concerns in terms of accuracy, resource constraints, and latency. Latino et al. [39] proposed a cybersecurity reference framework for the food and beverage industry. They compare the progress made in cybersecurity within the food and beverage sector with suggested advancements in the I4.0 framework. Given the fact that I4.0 has diverse implementations in the food and beverage supply chain, it also establishes unique circumstances that necessitate ad hoc cybersecurity solutions. As such, the authors proposed future directions to mitigate these challenges. Peña Zarzuelo [40] highlights some crucial cybersecurity challenges in the ports and maritime industry. The author stressed the recent smart transformation of the port sector (smart port) due to the increasing adoption of I4.0 and emphasized the importance of ensuring its security as a critical infrastructure. Other proposed frameworks for different cybersecurity application domains include space [41], automated vehicles [42, 43], process control and operations [44], FinTech [45], energy [46], construction [47], railway [48], smart electric vehicle charging [49], unmanned aerial systems [50], bioprinting [51], SMEs [52], medical devices, [53] etc.
In [54], the authors combined deep learning and blockchain to secure critical health care data in CPS. The authors present a secure pattern-proof malware validation technique that was developed specifically for ICPS using blockchain and reinforcement learning techniques on the LSTM deep learning model. The method is able to improve system performance, mitigate cyber threats, and detect known and unknown attacks. Other applications of blockchain-based techniques for industrial cybersecurity include the works on blockchain-secured smart manufacturing [55], blockchain in internet of things [56], blockchain in industrial context [57], blockchain-enabled smart operations [58], blockchain and DT empowered self-healing [59], and blockchain-based data-driven control system [60].
The advancements brought by DTs in industry are undeniably huge. However, the interactions, interconnectedness, and incorporation of DTs with other novel technologies, such as AI, big data, IIoT, CPS, and enhanced visualization, present a variety of cybersecurity challenges. Moreover, given its huge dependency on data, DTs are also vulnerable to multifaceted threats due to lack of data protection, confidentiality, integrity, availability, and privacy.
Hence, as we progressively embrace and incorporate distributed DTs in various industrial sectors and everyday routines, it is crucial to fully understand and investigate their cybersecurity impacts. By acquiring this insight, we will be able to fully utilize the advantages of DTs while averting the related cybersecurity vulnerabilities. The main objective of this section is to examine the potential cybersecurity threats in distributed DTs and outline methods to reduce them, with a specific emphasis on the manufacturing sector. 3.1.
Distributed DT ecosystems may, in fact, be more susceptible to cybersecurity breaches and threats than conventional systems. This is due to the fact that platforms containing distributed DTs generally exhibit different attributes, which include. • The infrastructure is characterized by extensive network connectivity and redistribution. • Distributed DT allows for remote management and exchange of vital information. • Distributed DT connects to cloud, edge, and fog devices located at the perimeter that require a highly secure system setting. • Distributed DT platforms make use of open standards of operation.
Additionally, security is a critical concern that needs to be taken into account within the distributed
DT platform. An important factor contributing to this is the expansion of the DTs to encompass a variety
of applications, such as data analytics, predictive maintenance, monitoring, etc., a significant number
of which are considered crucial. Moreover, each of these services is highly dependent on software
components including algorithms, models, and applications, which are frequently vulnerable to various
threats stemming from defects. Also, these services depend on numerous interfaces, interconnections,
and frameworks [
Furthermore, under the assumption that DTs can be modified to function in critical settings such as
the manufacturing industry, it becomes obligatory to provide additional protection for industrial systems
in conjunction with their DTs. Nevertheless, this requirement also prompts an investigation into whether
the integration of security protocols into the DT could subsequently introduce additional hardware and
software intricacies that could impact the functioning of the DT. One potential drawback is that the
integration of security measures may impede critical functions in DTs that encounter substantial
challenges in generating and analyzing models and data without having the choice to transfer resources
to more powerful platforms [
Clearly, inadequate security setup within the distributed DT paradigm can also present a crucial
threat. Given that DTs are regarded as reflections of the real-physical world, they contain duplicate
confidential data from the whole IT-OT paradigm [61]. This data might comprise sensitive information
such as the operational procedures of OT components, the functional attributes of confidential
processes, the particulars of the operational setting and its connections, or the crucial security
credentials required to access vital resources. Consequently, DTs also encompass vital information,
enabling malicious actors to extract and generate a mapping of the complete system or a specific
component thereof, in addition to deriving confidential data or identifying patterns through the analysis
of databases, system structures, and resources [
In line with this, it is imperative that every security evaluation of DTs considers the four featured
functional layers [
Cyberthreats in the DT platform Layer 1
Layer 2 & 3
Layer 4 Cyber Physical Systems/IIoT
Computing Infrastructure
Visual Systems
Computing Techniques
HMIs
The vulnerability of data poses a greater risk to the reliability and accuracy of DT. This can compromise the integrity of the data it gets from the real physical system. Any tampering or distortion of this data can result in inaccurate modeling and analysis, which in turn leads to flawed conclusions. Data integrity-focused cyberattacks, such as tampering, sniffing, and manipulation through different software attacks, present a substantial risk to distributed DTs [63] as they cover a large number of attack surfaces that can be initiated at any of the four layers in Figure 3.
Data confidentiality pertains to securing data from illicit access, disclosure, or unauthorized intrusion, exposure, and misappropriation. An unsecure DT ecosystem will certainly fall short of achieving the required confidentiality due to the huge attack surface. The significance of confidentiality in distributed DTs cannot be overstated, particularly in ensuring the preservation of information and data privacy. In essence, when establishing connections between DTs, particular confidentiality threats from Layers 1 to 4 that the current security measures might not be able to adequately handle can arise [64]. Therefore, a lot of considerations, such as architectural requirements, standard risk control, and other novel security measures and policies from credible institutions, must be put in place [64].
Data privacy in distributed DTs is one of the most alarming threats. The DT paradigm presents a significant security concern regarding data privacy, primarily due to its emphasis on safeguarding the proprietary information stored within its servers. Entities capable of infiltrating breached servers or associated infrastructures may extricate sensitive data, including but not limited to login information, setups, services, and other processes. They can harvest data for the purpose of industrial cyber espionage or discover primary weaknesses in DTs to enhance data penetration or other components. Threats related to software attacks and privacy leakage are some of the vulnerabilities surrounding data privacy. In Figure 3, Layers 2 and 3 depict such threats within the DT platform.
DTs generally handle sensitive and confidential data, which makes them a desirable target for attackers with the intent of gaining illegal access to the systems. This action may also be undertaken with the purpose of covertly acquiring data for other malicious intents, such as withholding critical data for financial reasons and impersonation [65]. Consequently, the use of weak access restrictions or the lack of authentication and access control mechanisms in distributed DT will lead to several other vulnerabilities, including software attacks, privilege escalation, and different tampering scenarios. These threats also cover a large attack surface in Figure 3, particularly from Layer 1, Layer 2 (computing infrastructure), and Layer 4 (HMIs). Figure 4 depicts the threat landscape in a distributed DT paradigm.
The IT and OT realms provide the backbone through which data exchange between different tools and services is carried out within the DT paradigm. This brings yet another large attack surface to the DT. Any form of threat that intercepts the edge connecting both realms will be devastating. Moreover, ensuring the physical security of OT devices is crucial since they are susceptible to potential harm, destruction, or theft by adversaries. The real physical replica of the DT also utilizes various technologies, such as tamper-proof and tamper-resistant hardware, to safeguard their data against unauthorized manipulation [66]. However, it still remains feasible for attackers to penetrate and modify the data via rogue CPS/IIoT, digital threat tampering, man-in-the-middle and poisoning attacks, or by leveraging other flaws within the infrastructure, applications, and communication channels. By monitoring and assessing various physical traits, such as processing times, supply flow, and various production channels, the attacker can also exploit confidential information through a physical device. This threat also exhibits a large attack surface, especially in Layers 1, 2, and 4, indicated to Figure 3.
Given that the distributed DT logic is distributed across a whole network of computing ecosystems
through clouds, fog, and at the edge, rogue DT servers and devices can function as an attack surface
[67], allowing DT communication to flow through. Similarly, such servers whose nodes carry a portion
of the logic have the potential to introduce inaccuracies within the information under processing, modify
or breach the storage systems under monitoring, and alter the generated knowledge or representation
meant for the end user [
Cloud-fog-edge computing, Visualization
Cyber physical systems, Protocols, Control
systems, Communication technology
Similar to other new technologies and infrastructures, distributed DTs face the same cybersecurityrelated risks. They need to effectively tackle the various types of cyberthreats mentioned earlier and establish strong security regulations and frameworks. A complete cybersecurity strategy and plan should include security measures in several facets, including secure network connectivity, software security, hardware security, secure communication channels, etc. All with the aim of ensuring data protection, network protection, access control, identity control and authentication, cloud protection, databases and cloud infrastructure protection, mobile protection, endpoint protection, and training of personnels/OT systems.
In the following, we provide defense mechanisms against cyberthreats that DTs are susceptible to. These mechanisms are based on recent novel techniques put forward by researchers and cybersecurity experts to mitigate different cyberthreats in DT.
Data
Information
OT
Authentication and access control: Strong authentication and access control mechanisms are
essential in the DT to ensure that access is only granted to authorized entities. Through the
implementation of authentication, users will have the ability to safely access various data processing
services within the DT paradigm through secure login credentials. Also, considering the complex
nature of distributed DTs in terms of their heterogeneous interconnectivity with other technologies,
authentication and access control will provide a secure and seamless DT interaction with other
interfaces and processes. Some novel state-of-the-art data authentication mechanisms in the
communication space of DT in different manufacturing sectors include the works in [68-72]. The
authors mainly emphasize privacy-preserving authentication between different communication
scenarios. Pervez et al. [69] specifically adopt the concept of ‘Verifiable Credentials’ in their
proposed framework to ensure the verifiable authenticity of Digital Twin data by encompassing
provenance, transparency, and dependability via the use of verifiable representation. Likewise,
Chen et al. [68] and Jiang et al. [73] utilize framework models at different layers of their
architecture, which provide flexible support for internal expansion of the DT and enable multi-DT
connectivity to more accurately represent intricate systems within the digital realm. This also
ensures the proper management of many internal and external interfaces within the ecosystem. In
terms of access control, techniques such as [74] and [75] provide frameworks for preventing
unauthorized access to multiple systems that exist together within the DT paradigm. Other access
control-based blockchain schemes include [76, 77]. Both techniques ensure the secure exchange of
DT data while preserving privacy. This gives adequate protection and access to resources by
granting the right privileges to the right parties involved.
2. Real-time monitoring and incidence response: The real-time virtualization and monitoring of
security-related events generated by DTs and the prompt response to incidents are essential steps
in ensuring the optimal functionality and security of the DT platform. This is to guarantee early
detection of potential threats, exploits, and vulnerabilities within the entire ecosystem. The security
operation center (SOC) [78] is the centralized entity responsible for the real-time monitoring and
detection of cyberthreats on IIoT platforms. Security Information and Event Management systems
(SIEM) are crucial tools utilized in SOCs. They gather critical events from various sources within
the entire DT network, standardize the events to a uniform format, and store the standardized events
to promptly detect illicit activity [78]. To enhance proactive security measures and improve
situational awareness in DTs, SIEMs and SOCs can additionally include Cyber Threat Intelligence
(CTI) protocols [
Generally, our review yields the following findings that are worth further research: (a) The security of OT systems is not sufficiently investigated in both academia and industry. (b) Security guidelines, policies, and preventive methods are often ignored. (c) Inadequately adaptive security measures lose their efficacy over time. (d) DTs for cybersecurity and (e) Cyber-immune systems for DTs are worth exploring. a) OT systems security needs more research: Considering the security of OT is not as mature as that of IT [30], present security measures [34] are insufficient in providing the required protection due to the integration of OT systems with diverse characteristics and interconnectivity with the IT industrial systems. Therefore, OT security solutions are worth investigating in both academia and industry. b) Security policies and preventive measures are often ignored: Implementing security policies is the first and most viable approach to mitigating attacks and human errors. Recent research, however, indicates that academia places a strong emphasis on intrusion detection systems after an attack occurs, but lacks focus on studies based on security policies before an attack occurs [30]. Therefore, it is important that the research community also focus on preventive measures through security policies before an attack happens. c)
Cybersecurity solutions that are not adaptable become invalid: Traditional industrial systems
were constructed with a fixed architecture that consisted of modules designed to operate reliably
for a specific period of time without substantial modifications. Nevertheless, the IIoT architectures
exhibit dynamism since they include different technologies, and such dynamism comes with a
plethora of solutions, some of which are novel and efficient [
Cyber-immune system for DT: As the integration of OT and IT exposes industrial technologies
to more vulnerability and increases the attack surface, the implementation of a robust cybersecurity
system that aims to replicate the adaptive immune system of humans is an effective approach to
safeguarding against existing and emerging cyberthreats [97]. Cyber-immune systems can survive
various types of attacks, particularly those caused by novel malware and viruses that cannot be
detected by traditional cybersecurity solutions [98, 99]. As a result, using cyber-immune systems
could be the key to making sure that data processing on the DT platforms is more secure than ever,
considering the immense vulnerability and large attack surface of DTs.
5. Challenges
a) Expansive attack surface: Due to the expansive interconnectivity landscape covered by
distributed DT, its attack surface happens to be one of the largest (if not the largest) among the
recent novel technologies. Also, the creation of a DT visual replica essentially multiplies the
potential attack surface, as adversaries can target either the physical system or its digital
counterpart. This poses a huge challenge to researchers and cybersecurity experts in reaching the
required protection, considering the immense performance-security tradeoff this expansive attack
surface could lead to.
b) Lack of standardized assessment criteria: Cybersecurity research for distributed DT and other
novel technologies lacks a standard framework for assessing vulnerabilities. Most proposals and
security measures focus on adopting conventional security solutions. However, evaluating the
framework implementation of DT through the modeling of attacks and defenses while studying the
system for a certain duration may provide the most accurate assessment. The major drawback,
however, is that it is not permissible to interfere with the real system owing to the crucial nature of
its activities and the need for constant supervision. Even though some solutions [
A distributed DT is built upon the integration of several technologies, including CPS, IIoT, AI, edge computing, big data, etc. The convergence of these novel technologies, in tandem with the inherent DT interconnections and data sharing with its corresponding physical counterpart, gives rise to several security concerns that have not been adequately investigated. To propose efficient solutions, this paper provides an in-depth discussion and analysis of the impact and significance of cybersecurity in distributed industrial DTs. The work first examined the importance of DT and cybersecurity in industry, followed by potential cyberthreats in industrial DTs. The analysis of threats within the large attack surface of the DT platform is covered in terms of the four functional layers of the DT. Additionally, the defense mechanisms for mitigating such threats are discussed in terms of recent technical approaches. Finally, we provide important key takeaways toward the realization of a secure and privacy-preserving industrial DT as well as challenges to existing DT security. Future work will focus on exploring the key takeaways of our findings to uncover efficient methods of securing key architectures for distributed DTs in the industrial domain.
The authors extend their thanks for the funding received from the ONE4ALL project funded by the European Commission, Horizon Europe Programme under the Grant Agreement No. 101091877.
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