Zero-Trust Architecture and Blockchain-Based Security Models for IoT-Integrated Industrial Power Electronics Systems

The integration of the Industrial Internet of Things (IIoT) with sixth-generation (6G) networks offers revolutionary connectivity and ultra-low latency, although it also increases cybersecurity vulnerabilities due to resource-limited devices and dynamic network structures. This research presents a novel privacy-preserving intrusion detection system (IDS) for IIoT in 6G settings, using Graph Neural Networks (GNNs) and Homomorphic Encryption (HE). The proposed system utilizes GNNs to represent intricate inter-device interactions and temporal traffic patterns, facilitating the effective identification of advanced threats, such as Advanced Persistent Threats (APTs), Distributed Denial of Service (DDoS) attacks, and Mirai botnet attacks. HE enables secure distributed training and inference by executing computations on encrypted data, thereby obviating the necessity for centralized data aggregation and assuring adherence to rigorous privacy standards like as GDPR. Experimental results indicate outstanding performance, with detection accuracies over 98%, low false positive rates, and little computing cost, rendering it appropriate for resource-constrained IIoT devices. The GNN + HE model surpasses state-of-the-art approaches in accuracy, scalability, and privacy protection, effectively tackling the dynamic difficulties of 6G-IIoT ecosystems. This study lays the groundwork for secure, scalable, and privacy-compliant IIoT systems, with future efforts directed towards adversarial robustness, real-time implementation, and integration with federated learning to improve resilience in highly interconnected industrial settings.

Blockchain assisted clustering with Intrusion Detection System for Industrial Internet of Things environment

Architectural changes are happening in the modern industries due to the adaption and the deployment of ‘Internet of Things (IoT)’ for monitoring and controlling various devices remotely from the external world. The most predominant place where the IoT technology makes the most sense is the industrial automation processes in smart industries (Industry 4.0). In this paper, a reliable ‘Next Generation Cyber Security Architecture (NCSA)’ is presented for Industrial IoT (IIoT) environment that detects and thwarts cybersecurity threats and vulnerabilities. It helps to automate the processes of exchanging real-time critical information between devices without any human intervention. It proposes an analytical framework that can be used to protect entities and network traffics involved in the IIoT wireless communication. It incorporates an automated cyber-defense authentication mechanism that detects and prevents security attacks when a network session has been established. The defense mechanism accomplishes the required level of security protection in the network by generating an identity token which is cryptographically encrypted and verified by a virtual gateway system. The proposed NCSA improves security in the IIoT environment and reduces operational management cost.

CMAF-IIoT: Chaotic map-based authentication framework for Industrial Internet of Things

There is a large amount of real-time data, e.g., measurement data and instructions, among controllers, sensors, and actuators in the Industrial IoT. These data are vulnerable to unauthorized access and tampering. In addition, once the controller is controlled by malicious code, it may send out dangerous instruction that is not compliant with the preset control process, which seriously interferes with the industrial control process. To achieve correct and undisturbed control based on real-time data, we propose an attribute-based access control (ABAC) method for real-time data in the Industrial IoT to mitigate unauthorized access and tampering, and noncompliant operation. First, we analyze the abnormal behaviors of real-time data interaction in the Industrial IoT. Second, we propose the multilevel hash identity authentication method to identify and block unauthorized access and tampering with real-time data. And, then we model the timing relationship and task logic relationship of the control process into the attribute fields of the ABAC method to identify and block noncompliant operations. Further, we design an access control module and display the lightweight deployment under the availability constraints of the control service. Finally, the proposed access control method is analyzed, proved, and experimented with. The results show that the proposed method can prevent unauthorized and noncompliant behaviors to field real-time data, meanwhile, it has a controllable delay and better scalability.

The Industrial Internet of Things (IIoT) plays an important role in Industry 4.0, but the existing IIoT systems could be vulnerable to a single point of failure and malicious attacks, failing to provide reliable services. IIoT devices have some particularities, such as mobility, limited performance, and distributed deployment, which are challenging to traditional centralized access control methods in the large-scale IIoT environment. To resolve the challenges, we propose an access control system for the Industrial Internet of Things. The system contains three smart contracts: device contract (DC), policy contract (PC), and access contract (AC). The device contract provides a method of storing the URL of the resource data generated by the equipment and a query method. The policy contract provides the function of managing the attribute-based access control (ABAC) of the administrator user. The access contract is the core program that implements the access control method for ordinary users. Combining ABAC and blockchain technology provides decentralized, fine-grained, and dynamic access control management for IIoT.

A comparative evaluation of intrusion detection systems on the edge-IIoT-2022 dataset

Industrial Internet of Things (IIoT) is present in many participants from the energy, health, manufacturing, transport, and public sectors. Many factors catalyze IIoT, such as robotics, artificial intelligence, and intelligent decentralized manufacturing. However, the convergence between IT, OT, and IoT environments involves the integration of heterogeneous technologies through protocols, standards, and buses. However, this integration brings with it security risks. To avoid the security risks, especially when systems in different environments interact, it is important and urgent to create an early consensus among the stakeholders on the IIoT security. The default Common Vulnerability Scoring System (CVSS) offers a mechanism to measure the severity of an asset's vulnerability and therefore a way to characterize the risk. However, CVSS by default has two drawbacks. On the one hand, to carry out a risk analysis, it is necessary to have additional metrics to the one established by CVSSv3.1. On the other hand, this index has been used mostly in IT environments and although there are numerous efforts to develop a model that suits industrial environments, there is no established proposal. Therefore, we first propose a survey of the main 33 protocols, standards, and buses used in an IIoT environment. This survey will focus on the security of each one. The second part of our study consists of the creation of a framework to characterize risk in industrial environments, i.e., to solve both problems of the CVSS index. To this end, we created the Vulnerability Analysis Framework (VAF), which is a methodology that allows the analysis of 1,363 vulnerabilities to establish a measure to describe the risk in IIoT environments.

Mobile robot is an important developing direction in the field of robotics, it is widely used in Industrial Internet of Things (IIoT) environment, agriculture, military, transportation, services with the coming of 5G wireless communication technology. Automatic navigation control technology is the core in these research areas, which is also the key technology for mobile robot to achieve intelligentization and autonomation.The article discusses and researches the neural network technology and its application in mobile robot navigation control. For the characteristics and research of mobile robot navigation problem, it finds the way to improve the mobile robot intelligentization, level of the self-organization, self-learning and adaptive capability. The combination of neural network with other intelligent algorithms solves autonomous navigation problem of the mobile robot in the complex uncertain environments and unknown variable environments. The mobile robot navigation control problem using fuzzy neural network can achieve a more effective real-time navigation control performance through amending the network weights by self-study according to the navigation priori knowledge of human experts.

Network Access Control, a critical element in protecting IT and OT environments, plays a vital role in determining who or what can access network resources and what features the device that will provide this connection must have. The primary function of NAC is to enforce security policies at the layer 2 and layer 3 of the enterprise network, control access to network resources, and prevent unauthorized access to the network. It acts as a security guard by examining every device that attempts to connect to the network and ensuring compliance with established security policies and protocols. In an age where insider threats pose a significant risk, NAC’s ability to manage, monitor and restrict network access based on user credentials and device compatibility is invaluable. Blockchain technology, renowned for its robust security features such as decentralization, immutability and transparency, enhances the capabilities of NAC solutions. When integrated with NAC, blockchain offers a new layer of security and trust. It ensures that the access control and monitoring activities are not only well-documented and well-controlled but also immutable and transparent. This integration significantly reduces the chances of unauthorized access and internal threats, thereby fortifying the network’s defense against advanced persistent threats (APT) and sophisticated cyber attacks. This study proposes the integration of NAC within the Hyperledger Fabric network architecture. Both NAC and Hyperledger Fabric are compatible for private networks so blockchain’s decentralized structure and its ability to maintain an immutable record of network access events complement NAC’s robust access control mechanisms. This integration is designed to enhance cybersecurity, establish trust, and improve accountability. Furthermore, this study is set to make a contribution to the academic literature and ongoing research in the realm of cybersecurity.

Industrial Internet of Things (IIoT) and Industry 4.0/5.0 offer several interconnections between machinery, equipment, processes, and personnel in diverse application areas namely logistics, supply chain, manufacturing, transportation, and healthcare. The conventional security-based solutions in IIoT environment get degraded due to the third parties. Therefore, the recent blockchain technology (BCT) can be employed to resolve trust issues and eliminate the need for third parties. Therefore, this paper presents a novel blockchain enabled secure optimal lightweight cryptography based image encryption (BC-LWCIE) technique for industry 4.0 environment. In addition, the BC-LWCIE technique involves the design of an optimal LWC based hash function with optimal key generation using chicken swarm optimization (CSO) algorithm. Moreover, the CSO algorithm derives a fitness function with the maximization of peak signal to noise ratio (PSNR). The BC-LWCIE technique stores the cryptographic pixel values of the encrypted image in the BCT to ensure secrecy in the IIoT environment. In order to highlight the enhanced security performance of the BC-LWCIE technique, a series of simulations were carried out and the results demonstrated the betterment of the BC-LWCIE technique over the recent techniques.

This paper presents a three-stage optimization mechanism designed to enhance Federated Learning (FL) in Industrial Internet of Things (IIoT) networks. Traditional FL optimizations, which typically focus on a single aspect, fall short in IIoT environments. Our approach integrates a multi-criteria enhancement: first, an Ensembled Client Selection Mechanism (ECSM) selects participants based on accuracy, reputation, and randomness. Second, Adaptive Distributed Client Training (ADCT) dynamically adjusts based on participant performance. Lastly, a Secure and Efficient Communication Channel (SECC), backed by blockchain, meets IIoT’s stringent security demands. The evaluation shows TSFed outperforms baseline methods, enhancing FL performance by increasing accuracy and F1-score. Importantly, TSFed improves the efficiency of achieving 80% accuracy on the MNIST dataset by 29.09% over baseline methods, showcasing significant gains in both security and efficiency. This mechanism also exhibits robustness against malicious attacks, setting a new benchmark for FL in IIoT environments.

Industrial Internet of Things (IIoT) finds use in several industrial applications like robots, medical devices, and software-defined manufacturing processes. Apart from the promising benefits of the IIoT networks, several challenging issues need to be resolved, such as network connectivity, security, privacy, heterogeneity, scheduling, and energy efficiency. Due to the large-scale deployment and heterogeneity of the nodes in IIoT networks, some energy-limited nodes have existed in the IIoT networks, resulting in reduced network lifetime. At the same time, security and privacy are also considered as the major issues that exist in the design of IIoT, which can be addressed using secure routing techniques. In this view, this study develops a trust-aware multiobjective metaheuristic optimization-based secure clustering with route planning (TAMOMO-SCRP) technique for cluster-based IIoT environment. The presented TAMOMO-SCRP technique mainly focuses on the design of bald eagle search (BES) algorithm for clustering and routing processes. The proposed TAMOMO-SCRP model derives a fitness function for accomplishing maximum energy efficiency and security. For an effective clustering process, the TAMOMO-SCRP model designs an objective function involving four parameters such as trust level (TL), communication cost (CC), residual energy (RE), and node degree (ND). Besides, the route selection process is based on the fitness function with two variables, namely, queue length and link quality. For assessing the enhanced performance of the TAMOMO-SCRP model, a wide range of experiments were carried out to get the outcomes of network life time(NLT) as 39451,half network die (HND) as 25950 and Stability period (SP) 8000 time calculated no. of alive nodes. The achieved outcomes make sure the better performance of the TAMOMO-SCRP technique against the other recent approaches.

The Industrial Internet of Things (IIoT) is central to smart manufacturing, enabling real-time automation, data exchange, and system intelligence. However, the convergence of cyber-physical systems with legacy software and heterogeneous architectures introduces significant security challenges. This paper explores how software engineering principles can be strategically employed to enhance IIoT security by integrating threat modeling into the development lifecycle. In this study, we review classic models such as STRIDE, DREAD, and STPA-Sec, and evaluate their effectiveness when applied at various phases of the Secure Software Development Life Cycle (SSDLC). STRIDE focuses on classifying security threats, DREAD helps score the severity of risks, and STPA-Sec provides a safety-oriented approach to identifying unsafe control actions in IIoT environments. Additionally, we propose a secure development process to embed continuous security assurance during IIoT software deployment. This research highlights design-driven security patterns, model-driven engineering strategies, and secure API development best practices. This paper aims to support developers and architects in designing scalable and threat-aware IIoT systems through the alignment of software engineering with IIoT-specific threat vectors.

Internet of Things (IoT)-enabled devices are becoming integrated into a significant and increasing proportion of critical infrastructures, changing the cybersecurity-risk landscape. Risk is being introduced to industry sectors such as transport, energy, and manufacturing, with new attack surfaces exposed and potential for increased harm. Furthermore, risk and harm arising in the Industrial IoT (IIoT) could propagate across interconnected organisations and sectors, resulting in systemic risk. Aspects of this changing risk landscape are not addressed by current cybersecurity approaches, leaving cybersecurity-capability gaps. In this article, we show how current and emerging cybersecurity needs in the IIoT align with a key industry cybersecurity standard, the NIST Cyber Security Framework. The key capability gaps emerging in the IIoT are identified based on our findings from a series of workshops with over 100 expert participants. We present a comprehensive research agenda to enable researchers to prioritise research focus to address these gaps; this research agenda covers the full lifecycle of IIoT development (design, implementation, use and decommission). Furthermore, we conclude that there is a significant gap in understanding of the nature of systemic risk, which should be a key priority if we are to develop effective solutions for cybersecurity and safety in IIoT environments.