ACCESS
Review
Volume 1, Article ID: 2024.0004
Vishnu Vardhan Baligodugula
baligodugula.2@wright.edu
Ashutosh Ghimire
ghimire.18@wright.edu
Fathi Amsaad
fathi.amsaad@wright.edu
1 Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, USA
* Author to whom correspondence should be addressed
Received: 18 Jun 2024 Accepted: 08 Aug 2024 Published: 28 Aug 2024
Network segmentation is a very important approach in enhancing network security. The approach involves breaking down the network into smaller, more manageable segments, each with its own specific security requirements. This strategy supports maintaining stable perimeters and effective access control while safeguarding critical resources, such as database servers, from unauthorized access. The relevance of network segmentation in IIoT comes right with the state-of-the-art and interconnected nature of many devices that may pose extensive safety issues. To address these challenges, the Secure IIoT Network Segmentation Framework was developed as a specialized cybersecurity solution for IIoT environments. This framework includes specific guidelines for growing custom designs that improve safety posture and protect essential records. In IIoT environments, security segmentation is crucial for keeping different business structures separated, each with its own specific protection requirements, and for safeguarding them against the unique risks posed by interconnected devices. Specific problems of the access factors pose precise problems in IIoT networks because they function as convergence nodes for lots of devices, therefore making sure to provide many types of privacy breaches and interactions with different firms. Segmentation provides many benefits, which include accelerated protection, a reduced attack surface, simplified compliance, and improved device management. Still, it also complicates things and adds operational overhead, and there are fee concerns as well. Apart from community segmentation, a lot of techniques have been practiced to reinforce the safety framework: Federation of IDs, Micro-segmentation, Firewall, Network Access Control (NAC). It provides control of unique visitors, enforce security regulations, and deals with community access while supporting segmentation efforts and enhancing universal safety in IIoT structures. One such relevant approach with respect to network segmentation, more particularly in IIoT environments, concerns the enhancement of safety, protection of sensitive statistics, and compliance with enterprise requirements. By using frameworks like SiNeSF and complementing protection techniques, an organization can set up secure obstacle building, access limitation, and hazard restriction on the risks related to networked IIoT devices.
The strategic process that intends to break down a network into smaller Segments often to reinforce safety called network segmentation. This can be achieved through segmentation, which will help isolate internal user traffic. Compared with external visitors and contacts providing better control who has access to the community [1]. This segmentation lets in for discrete areas for specific operations, which include net servers, databases, and team of workers devices, making it less complicated to regulate protection zones. Segmentation ensures compliance with regulatory requirements, protects sensitive data from insider threats, and makes it more difficult to access insecure devices. Although community segmentation can provide advantages beyond protection, including general performance optimization, this article focuses on its shielding functions [1].
In the Industrial Internet of Things (IIoT), community segmentation is crucial as a couple of gadgets engage on the identical community. The complexity of IIoT has led to extensive security vulnerabilities, as seen by the explosion of denial-of-service attacks focusing on those devices [2]. This paper explains the network segmentation pattern, specifically the Abstract Security Pattern (ASP), and then distinguishes between IIoT segmentation patterns. The ASP outlines the conceptual and semantic restrictions of a domain, shielding it from threats without focusing on implementation specifics. It also provides a framework for managing regulatory risks [3]. Realworld case studies will demonstrate practical implementations of information security, emphasizing the crucial necessity for IIoT segmentation as a strong framework for protection. Furthermore, the discussion will differentiate between hierarchical identification patterns and segmentation strategies applicable to both IIoT and traditional non-IIoT scenarios, including network service segmentation [4].
The structure of this paper is as follows. Section 2 provides an overview of the IIoT architecture; Section 3 explores comprehensive IIoT security principles and strategies; Section 4 reviews related works in the field; Section 5 outlines proposed strategies for network segmentation within connected IIoT environments; and Section 6 focuses on IIoT security segmentation, culminating in a conclusion that highlights the key benefits of implementing network segmentation within IIoT frameworks.
The IIoT itself is defined as a network of interconnected devices designed for data collection and exchange, primarily aimed at optimizing industrial processes. IIoT architecture is generally categorized into four essential layers. Edge devices, gateways, data management and cloud, each serving a unique function in the global data processing and handling ecosystem, as illustrated in the accompanying Figure 1.
Figure 1: IIoT Architecture Layers: A Simplified Diagram.
The Internet of Things (IoT) network is fundamentally anchored by the device layer, which functions as the initial layer where various interconnected devices are located at the network’s edge. This layer is primarily composed of industrial Internet of Things (IIoT) devices strategically deployed in proximity to the data source to ensure efficient information gathering. The devices typically involve remote sensors, and coupled with this are actuators that play very critical roles in data acquisition and controls processes. Each cluster of devices is fitted with a processing unit or even a lightweight computing device, which enables local computations, accompanied by a range of monitoring.
At the IoT area, one may additionally come across a wide style of gadgets. These range from legacy devices working inside brownfield environments to advanced IoT sensors, robot cameras, microphones, and numerous meters and video display units. The primary function of IoT sensors is to capture information from the surrounding environment or from specific objects under observation. Upon collecting this information, sensors manner and convert it into usable facts that may be analyzed via human beings or other structures.
In addition to sensing, the actuator element is important for handling and manipulating the physical approaches that occur within the monitored environment. Actuators are responsible for performing actions based on the received data, thereby adjusting the conditions under which measurements are taken. For example, an actuator can additionally manage the opening or closing of a valve, or it can manage a mechanical arm during automatic assembly line operation. This interaction between sensors and actuators not only enhances the efficiency of data collection but also ensures that the monitored environments can be dynamically regulated, ultimately facilitating a more responsive and intelligent IoT ecosystem. Consequently, the machine layer is imperative to set up the muse for powerful facts conversation and operational automation within IoT networks.
Safety segmentation in the IIoT environment of commercial systems lends itself to the isolation of a wide diversity of companies within IIoT systems and their related additives that address several security requirements for each unit. Nowadays, the IIoT landscape has the mixing of a heterogeneous set of devices with different origins, types, and security postures. This immense interconnectivity introduces risks that are not normally anticipated in conventional tool training. The access factors, which provide interconnection nodes for lots of devices, are at the heart of this complexity. These access points often lack adequate privacy protections and frequently communicate with external agencies over the internet, which can create complex situations that pose significant security risks.
Sensor networks are one of the most important addins for any cyber-physically-based system (CPS) device that has major threats to human safety and belongings. The network is an ubiquitous platform for the operational procedures in IIoT devices, and fog computing layers enhance their performance skills. Although fog computing incorporates dispersed sub-layers that provide help to quit devices, this abstraction also introduces new vulnerabilities inside massive-scale interconnected systems. Protecting the critical tools from the systems-related attacks is difficult, mainly because of the heterogeneity of the sources, formats, security schema, and computational capabilities of those devices. The complexity of those systems can also bring conflicts in terms of access and manipulation, which accordingly complicates the strategies to control.
Network segmentation offers numerous benefits in its implementation but also presents significant challenges. In addition, this separation of sensitive information and critical systems from the broader campus enhances security by minimizing the chance of access and capability breaches. This also reduces the attack surface, which means a reduced number of vulnerable sites and thus lesser chances of successful cyberattacks. Segmentation of the network helps to maintain compliance with the requirements of a regulation by only allowing specific access to sensitive documents and systems. This might enhance tool management, thereby defining responsibilities clearly for each device for more common protection and operation management.
On the other hand, the implementation of the community segmentation can bring additional complexity that requires careful planning plans and continuous control to keep a robust structure. Operational overhead can result from segmentation, as it may introduce latency and performance degradation if not managed carefully. In addition, the implementation of a powerful segmentation approach regularly involves cost considerations; companies can also need to spend money on additional hardware and software solutions, which leads to accelerated monetary burdens.
However, deploying network segmentation can be a complex task due to the need for extensive logistical planning and strategy to maintain and operate such a system effectively. One of the consequences of imposing expenditure is that it can spark latency and performance degradation if it is wrongly managed. In addition, strategy of segmentation also has its cost considerations in its implementation. It may require organizations to spend more money to acquire more equipment and software, and that alone can prove very expensive.
Segmentation can be maximized through the use of different methods and other techniques that are associated with the subject. One of them is federated identities, which is a technique that offers a means to dynamically and securely manage identities across segments and systems within an organization. Another strategy is microsegmentation, which enables control of data traffic within subgroups, thus, security is improved by eliminating the possibility of attacks on other parts of the network. The use of firewalls and NAC (Network Access Control) systems is also paramount in the enforcement of security policies, as well as the management of access not only at the network perimeter but also within the structure of the internal organization Through the application of these techniques, companies can set up a strong security structure that will help to reduce the threats which arise from the heterogeneous and intricate nature of IIoT environments.
Partitioning the industrial Internet of Things (IIoT) network into many segments, each including trusted devices behind more secure partitions, is a sensible approach to managing IIoT networks. A typical arrangement is shown in Figure 2, where fog computer systems and organizational equipment are clearly divided into segments.This methodical technique improves the management of information streams and the security organization. To realize the fundamental confinement inside Nearby Zone Systems (LANs), switches and Virtual Neighborhood Zone Systems (VLANs) play a basic part. Gadgets interface to the haze computing design, which in turn interfaces to the bigger organizational arrange and the cloud, as outlined in Figure 2. This division essentially reinforces the overall security posture of the IIoT network, which is crucial for protecting sensitive information and ensuring that each segment can be independently monitored and managed.
Figure 2: A Partitioned Network of IIoT Connected Devices.
Several factors influence the successful implementation of security segmentation in IIoT environments:
In IIoT situations, portals play an important role as central centers where IoT sensor information is collected, digitized, and handled recently being transmitted to the cloud. The portals are set near to the sensors and actuators, encouraging pre-processing assignments at the edge. This approach reduces latency and bandwidth usage by filtering and aggregating data locally before transmission. With portals, the management of the IoT data that comes from the sensors can be almost impossible. They turn the original faintness of the sensor data into digital data that is suitable for A.I. to learn and analyze. Besides this, the recent advancements in portals may include self-diagnostics and builtin security tools to assess and protect incoming data streams in realtime.
Effective segmentation in IIoT means implementing some strategies against the exact challenges of these environments.
Several solutions proposed prove real-world effective IIoT segmentation strategies:
The main advantages that arise among others from implementing segmentation into the IIoT environment are numerous.
Despite the advantages, IIoT segmentation presents some challenges to its implementation:
Processing starts at the sensor level, and this is useful when information is needed immediately. In this context, edge computing is crucial for achieving fast responses by processing data locally at the edge of the network. It enables preliminary processing directly where the data is generated, such as at IIoT sensors, which are positioned at the edge of the network [5]. The method begins once sensor information are captured, digitized, and totaled, making it appropriate to encourage examination through edge IT frameworks. These edge IT frameworks may be either on-premise or inaccessible; in any case, they are regularly arranged in near nearness to the sensors to optimize proficiency. At this point, the digitized and totaled information is utilized for analytics purposes, empowering the application of machine learning calculations and information visualization methods. The experiences extricated from this handled information are then prioritized for transmission, instead of sending all collected information to centralized information centers or cloud stages. It is a concentrated approach that reduces the amount of data in motion, hence eliminating concerns about capacity, security, and the potential for downtime. Edge preparation of information enables organizations to drive operational efficiency and decision steps on the back of easily digestible chunks of information, hence effectively managing information strategies.
Edge gadgets are constrained in their capacity to perform broad pre-processing, requiring dependence on cloud computing for more profound examination and bits of knowledge. The primary objective is to perform as much processing as possible at the edge to conserve local computational resources. However, for complex computations, cloud services become essential. The primary objective is to perform as much processing as possible at the edge to conserve local computational resources. However, for complex computations, cloud services become essential. This doubles the focus on leveraging the speed characteristic to edge computing whereas tapping into the broad explanatory capabilities of cloud stages. In cloud environments, data collected from various sources undergoes integration and analysis, providing insights that are not easily achievable at the edge, particularly for industrial process management.
High-quality data management, storage, and analysis are facilitated by cloud servers, on-premises data centers, or hybrid systems. These cloud servers possess the computational power necessary for thorough and secure data analysis, although the trade-off is longer processing times. Yet, in the cloud computing space, there is more of a focus for data analytics by pulling together insights from multiple data streams which then creates novel knowledge that contributes to operational efficiency.
The IIoT solution is organized into a series of layers, where each level is meant for cloud setups or edge devices, and it works perfectly as the system deals with data processing, analysis, and collection. A thorough understanding of these layers helps the firms to improve their operational processes and to solve some specific security problems that appear at every layer. Working on these key issues gives companies the opportunity to fully use IIoT, resulting in smarter and more efficient industrial operations. Comprehension of this enables not only the improvement of workflows but also the strengthening of security measures, and as a result, the businesses remains competitive in a technology landscape that is rapidly evolving.
The Industrial Internet of Things (IIoT) security scene can be inspected through two urgent regions: Nonexclusive security necessities and particular challenges at different design layers. Adherence to the foundational standards of secrecy, astuteness, and accessibility (CIA) is basic; be that as it may, a few challenges complicate the realization of these targets [3].
Briefly, managing the challenges of IIoT security re- quires a general approach that must include the standards of design securely, appropriate strategies for resource- constrained cases, and proactive management of the secu- rity practices throughout the lifecycle of devices. The fur- ther development of standards and specifications will be the key to efficient and effective protection of the emerg- ing scene of IIoT.
Network segmentation is one of the most important security measures for Industrial Internet of Things (IIoT) contexts, and it includes the logical or physical segmentation of a network into isolated segments. In this way, an advanced form of policy enforcement and security threat monitoring is permitted. This section describes different approaches and best practices for effectively implementing network segmentation in connected IIoT contexts [14].
Operational efficiency with robust security in IIoT environments can be found on a few of the following central tenets:
Network segmentation can be effectively carried out using several techniques illustrated in Figure 3.
Figure 3: Sequence Diagram: Accessing a Subnetwork Example.
Network segmentation can be accomplished using the following facilities.
Figure 3 shows a sequence diagram for one of the use-case scenarios, which handles these will help in meeting a requirement for access to a sub-network in a segmented IIoT setup. All of these segmentation use cases can be taken into account with regard to certain specific requirements, such as security, scalability, and operational efficiency in actual field applications.
Network segmentation is the vital point in the modern cy- bersecurity environment that allows organizations to reap many benefits along with some obstacles that they have to tackle. One of the main benefits of network segmentation is that it strengthens security. By separating necessary systems and confidential data, organizations can almost en- tirely avoid violations of breaches [16]. Through this sep- aration, the exposure of the attack surface is reduced, thus the number of endpoints that may be in the face of a pos- sible threat is minimized. In turn, the probability of suc- cessful cyberattacks is reduced [18]. Additionally, with segmented networks, compliance with various regulatory standards becomes more manageable. Segmentation al- lows organizations to clearly define the boundaries of their networks and restrict access to specific areas, thereby aid- ing in meeting legal requirements [19]. Device manage- ment which is the effective control of devices is also made possible through segmentation, as it makes clear the iden- tity and function of each device, thus, the overall control and visibility on the network is improved.
Nevertheless, with all of these attractive features, organizations still face numerous problems in their adop- tion of network segmentation as shown in Table 1. The most important of these is the complication of network architecture, which now has to be planned very carefully and managed all the way so that the segmentation will be effective. Segmentation that is not properly managed may lead to operational overheads, which, in turn, may be the reason for latency and performance issues that will affect business operations [20]. Besides, the implementation of segmentation often comes with extra costs since organiza- tions might have to purchase new hardware and software to be able to support this strategy leading to the rise of operational expenditures. In order to overcome these hin- drances and fully maximize the advantages of segmenta- tion, institutions can use different safety techniques [21]. For example, federating identities can ensure secure iden- tity management across different segments while micro- segmentation limits the possible attack vectors and en- hances traffic control within those segments. Alongside traffic firewalls and NAC systems, deploying NAC can also assure the compliance of security policies as well as control the access. Also, guaranteeing that the segmenta- tion is not only the protection of the network but is also correctly working as it should be [19].
Table 1: Benefits, Challenges, and Security Mechanisms of Network Segmentation.
Proper strategic segmentation of IIoT networks is crucial for effective management. By creating distinct partitions within the network, organizations can identify and isolate trusted devices, placing them in secure segments. This approach enhances data flow management and overall security through segmented control. An ordinary scenario of such community partitioning is illustrated in Figure 4, which highlights how organizational system and fog computing structures are divided into isolated segments. Within Local Area Networks (LANs), devices such as switches and Virtual Neighborhood Zone Systems (VLANs) play a extensive position in maintaining up this separation. Gadgets are associated with both fog and larger organizational structures, eventually finding their way back to cloud infrastructure—the greater reason being department in the defense of data sagacity.
Figure 4: A Segmented Network of IIoT Devices.
IIoT Segmentation will be successful if the critical factors identified in Table 2 are followed below.
Table 2: Key Factors for IIoT Segmentation.
The following methods can be used to effectively segment the IIoT:
Real-world implementations that show effective IIoT segmentation strategies are included in Tabel 3.
Table 3: Practical IIoT Segmentation Solutions.
The implementation of segmentation in IIoT has the following application-based benefits:
Despite the benefits, IIoT segmentation faces a number of challenges enumerated in Table 4.
Table 4: Challenges of IIoT Segmentation.
To mitigate the challenges of IIoT segmentation, an approach must be structured and best-practice-based with an approach designed specifically to handle the unique security requirements of IIoT environments. This will ensure that all the features that come with segmentation are still retained but with the effectiveness of managing security on organizational networks.
Dividing a network into smaller manageable segments helps to boost security. This approach known as network segmentation plays a key role in protecting networks. This paper has taken a deep look at IIoT network segmentation highlighting why it matters and the best ways to secure IIoT systems.
The segmentation pattern that have been discussed lay the groundwork. It brings together key functions that could be tweaked to fit the specific needs. This basic pattern is not set in stone. Instead, it kicks off the creation of a series of linked patterns. These expanded patterns build on the main ideas of segmentation, which leads to big steps forward in keeping IIoT networks safe and well managed. By putting network segmentation into action, companies can keep different parts of their IIoT systems separate. This separation cuts down the chances of people getting in when they shouldn’t and limits how much damage they can do by keeping security threats in specific areas. Also, each area has its own security rules and ways to control who gets in. This makes it easier to watch what is going on and helps stop threats from spreading, leading to a stronger network setup.
The ability to customize is at the heart of the segmentation method. Companies can adjust the segmentation patterns to meet their specific needs and issues offering a flexible and adjustable structure. As new tech and gadgets pop up, this basic pattern gives a strong base to build extra security steps and management techniques on. It could be deduced that IIoT network segmentation brings big benefits by cutting down on weak spots making monitoring better, and allowing custom security rules for different parts. This method can keep up with tech progress and company changes making it easy to scale up.
V.V.B. and F.A. conceptualized the core ideas and framework of the paper. V.V.B. conducted the primary research and contributed to the initial drafting of the manuscript.
A.G. outlined the structure of the survey and provided critical revisions to enhance the overall quality of the content. F.A. provided significant supervision and expert guidance throughout the process. All authors have read and approved the final version of the manuscript.
The authors declare no conflicts of interest regarding this manuscript.
This research was funded by the Air Force Research Labo- ratory (AFRL) Grant, under the Assured and Trusted Digi- tal Microelectronics Ecosystem (ADMETE) grant number BAA-FA8650-18-S-1201, awarded to Wright State Uni- versity, Dayton, Ohio, USA. This project was carried out under the CAGE number: 4B991 and DUNS: 047814256.
This work was sponsored by the Air Force Research Labo- ratory (AFRL) Grant, under the Assured and Trusted Dig- ital Microelectronics Ecosystem (ADMETE) grant BAA- FA8650-18-S-1201, awarded to Wright State University, Dayton, Ohio, USA. This project was carried out under the CAGE number: 4B991 and DUNS: 047814256.
