An efficient and provable certificate-based proxy signature scheme for IIoT 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.

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

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

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.

SummaryThrough the Industrial Internet of Things (IIoT), the manufacturing enterprises have significantly enhanced the production efficiency. The transmission of data in IIoT over public channels has brought about information leakage issues. Therefore, it is crucial to agree on a session key among the participants. In recent years, numerous authenticated key exchange (AKE) schemes have been designed. However, those AKE schemes which adopt the certificateless approach to tackle certificate management and key escrow may remain vulnerable to quantum attacks, and other AKE schemes which are based on lattice hard problem so as to achieve the post quantum security may incur key management issues. To simultaneously address the aforementioned challenges, we propose a lightweight certificateless lattice‐based authenticated key exchange (C2LAKE) scheme. Upon the hardness assumptions of the inhomogeneous small integer solution (ISIS) and computational bilateral inhomogeneous small integer solution (CBi‐ISIS) problems, the C2LAKE scheme has been demonstrated to be secure in the eCK model and the random oracle model (ROM). Better trade‐off among security and functionality features, and communication and computation costs make the C2LAKE scheme suitable and applicable in the IIoT environment.

The rapid development of the supply chain of the Internet of Things (IoT) industry may trigger financial risk contagion among IoT manufacturers. This article collects data on listed IoT companies in the Chinese market from 2010 to 2019 and explores the development of the environment for the IoT industry. Two dynamic time wrapping (DTW) networks are created to analyze the topological structures of the IoT industry environment. Both the standard DTW and strongly connected DTW networks are revolutionary in terms of their interconnectedness in the IoT industry. We found that the level of clustering and transitivity of the network continued to decline between 2010 and 2019; i.e., the efficiency of financial risk contagion on IoT networks was significantly reduced. This article contributes to the literature in two aspects. First, it reveals that China's IoT industry is increasingly competitive; financial risks have become more difficult to transfer. The IoT industry has become more robust and exhibits a lower likelihood of financial risk contagion. Second, the article provides empirical evidence for the theory of coevolution, showing that risk contagion ability in an industry setting is decreasing with the development of individual firms.

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.

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.

Multi-factor based session secret key agreement for the Industrial Internet of Things

With the significant development of the Internet, Internet of Things (IoT) has become an emerging technology in many industries. To support security and privacy in the Industrial IoT environment, a user may interact with another user on the Internet to share confidential information, which requires an authenticated communication channel. To meet this demand, in this article the authors developed an identity-based two-party authenticated key agreement (ID-2PAKA) protocol that allows two users to communicate securely and share sensitive data across IoT-enabled regions. Similar protocols found in the literature either proven to be insecure or carry the burden of high communication and computational costs. The proposed ID-2PAKA protocol is analyzed in the random oracle model to achieve provable security based on the hardness assumptions of computational Diffie-Hellman and bilinear Diffie-Hellman problems. The performance analysis of the proposed ID-2PAKA protocol is performed using the pairing-based cryptography library. The comparative results from the perspective of the computation and communication costs against the competing protocols showed that the proposed ID-2PAKA protocol is secure and efficient.

A trusted mechanism for detecting malware in Industrial Internet of Things (IIoT) using blockchain technology is proposed. The proposed mechanism leverages the immutability and decentralization features of blockchain to ensure the integrity of the malware detection process, while minimizing the overhead associated with data integrity. The mechanism involves the use of a consensus algorithm, Proof of Authority (PoA) to validate malware detection results and a smart contract to enforce the consensus rules. Experimental results show that the proposed approach can efficiently detect malware in IIoT environments with minimal impact on system performance. The proposed architecture is thoroughly validated using MATLAB and a variety of security criteria, including attack strength, message alteration, and false validation probability. Based on the obtained results, the suggested method is effective in improving the security of IIoT networks by detecting malware attacks within the network. The proposed mechanism provides a promising solution for enhancing the security of IIoT systems, which are becoming increasingly vulnerable to cyber-attacks.

The urgent penetration of the Industrial Internet of Things (IIoT) in industrial sectors has fostered unmatched benefits in terms of efficiency, productivity, and overall performance. Although current security frameworks already offer a sound level of protection, they do not adequately scale to the growing diversity and number of devices integrated into the Industrial Internet of Things. They rely on static, centralized network organization and management, which cannot ensure data integrity across a huge number of highly distributed devices. The proposed research provides insight into an innovative end-to-end security solution model, which caters to the needs of IIoT via the introduction of the blockchain. The current security solutions seem to fail in terms of security, scalability, data efficiency, integrity, and the actual needs of IIoT. As a result, the top chance of a cyberattack or similar threat remains high. The proposed work demonstrates that IIoTs may benefit from blockchain frameworks because blockchains reuse existing security mechanisms, enable transaction validation without central trust, and implement appropriate authorization. Additionally, the IIoT environment can benefit from implementing security frameworks that reflect their characteristics. In this paper, an overall solution that employed the decentralized setup of blockchain was proposed for authentication, authorization, and data integrity for all devices. Our approach is free from the need for any central authority. That mechanism is based on smart contracts that enforce security policies. Our framework allows for the switching of any access rule at any time, as well as it can immediately react to some suspicious behavior. In our paper, we proposed some lightweight cryptographic schemes, and Data management overhead which could fit any device. Moreover, our algorithm for the blockchain implementation is hybrid because this approach incorporates all the best features of the private and public implementation due to the individual blocks containing feasible information rather than the entire history.

Wireless electroencephalography (EEG) has emerged as a critical interface between human cognitive processes and machine learning technologies in the burgeoning field of sensor communications. This paper presents a comprehensive review of advancements in wireless EEG communication and analysis, with an emphasis on their role in next-generation green wireless networks and industrial IoT. The review explores the efficacy of modulation techniques, such as amplitude-shift keying (ASK) and frequency-shift keying (FSK) in EEG data transmission, and emphasizes the transformative role of deep learning in the joint transmission and restoration of EEG signals. In addition, we propose a novel, energy-efficient approach to deep learning-based EEG analytics, designed to enhance wireless information transfer for industrial IoT applications. By applying an autoencoder to sample the EEG data and incorporating a hidden layer to simulate a noisy communication channel, we assessed the energy efficiency and reliability of the transmission. Our results demonstrate that the chosen network topology and parameters significantly affect not only data fidelity but also energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. A key aspect of our study is related to symmetry. Our results demonstrate that the chosen network topology and parameters significantly impact not only data fidelity but also energy fidelity and energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. Furthermore, we realized that the EEG data showed mildly marked symmetry. Neural networks must also exhibit asymmetric behavior for better performance.

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.