Industrial Internet Of Things Devices Research Articles

A Comprehensive Survey of Security Challenges and Solutions of Industrial Internet of Things (IIoT) Networks

Despite the transformative potential of the Industrial Internet of Things (IIoT) in optimizing industrial operations, significant security and privacy challenges persist, underscoring the need for comprehensive solutions. This paper addresses this research gap by presenting a thorough survey of the security challenges and solutions in IIoT networks. Drawing from the extensive literature on IIoT, we identify and categorize key threats such as malware attacks, data breaches, and supply chain vulnerabilities. Additionally, we explore privacy concerns arising from the vast amounts of sensitive data collected and transmitted by IIoT devices. This analysis highlights the critical importance of encryption mechanisms, access controls, and authentication protocols as fundamental security measures. By synthesizing current research, this paper provides insights to guide the development of robust security strategies for IIoT deployments. Addressing these challenges is crucial to maintaining the reliability and integrity of industrial operations, and safeguarding against production disruptions, financial losses, and safety hazards. Through robust security measures and privacy-enhancing technologies, IIoT deployments can foster trust, compliance, and innovation in industrial settings, driving the evolution towards smart factories and connected enterprises. Keywords – Industrial Internet of Things, Cyber Security, Network Security, Threats.

CABC: A Cross-Domain Authentication Method Combining Blockchain with Certificateless Signature for IIoT

Device management in the Industrial Internet of Things (IIoT) is complex, especially in a cross-domain context. Trust identity authentication between cross-domain devices is required when devices work together. Existing identity authentication methods can result in heavy key management overhead or be computationally demanding on the device. In this paper, we propose a cross-domain authentication scheme based on blockchain and certificateless signature that can be suitable for IIoT devices. First, smart contracts are used to complete trusted identity verification of devices, which reduces the computing overhead of IIoT devices. Second, a trust value-based access control method is proposed, which can quickly and timely identify malicious authentication and ensure the normal operation of the authentication system. Finally, the validity and safety of our proposed methods are proven by experiments. The experiment results showed that our methods can meet the needs in terms of IIoT device security and blockchain system output performance, and can achieve cross-domain authentication for IIoT devices.

Maximizing throughput in NOMA-enable industrial IoT networks using digital twin and reinforcement learning

IntroductionIncreased deployment of heterogeneous and complex Industrial Internet of Things (IIoT) applications such as predictive maintenance and asset tracking places a substantial strain on the limited computational and communication resources. To cater to the rigorous demands of these applications, it is imperative to devise an adaptive online resource allocation method to enhance the efficiency of the current network operations. Multiaccess edge computing (MEC) and digital twins (DTs) are promising solutions that facilitate the realization of edge intelligence and find applications in various industrial applications. Yet, little is known about the advantage the two technologies offer to IIoT networks. ObjectiveThis study presents a joint optimization of offloading and resource allocation approach where MEC-server DT is created at the edge, and nonorthogonal multiple access (NOMA) communication is considered between IIoT devices and the industrial gateways (IGWs) for spectral efficiency. Our proposed framework is tailored to reduce mean task completion latency and enhance overall IIoT network throughput. MethodTo achieve our objective, we jointly optimize the computation resource allocation (RA), subchannel assignment (SA), and offloading decisions (OD). Given the inherent complexity of the problem, we further divide it into RA and SA/OD sub-problems. Employing Deep Reinforcement Learning (DRL), we have formulated a solution delineating the most efficient RA strategy and leveraged DT for optimal SA/OD strategies. ResultsSimulation results demonstrate the superior efficiency of our framework, realizing up to 92 % of the efficiency of the exhaustive search method while reducing computation and action decision time. ConclusionIn light of system dynamics considered for our work, the proposed framework perfomance showcase its robustness and potential application in real-world IIoT networks.

Leveraging Artificial Intelligence and Provenance Blockchain Framework to Mitigate Risks in Cloud Manufacturing in Industry 4.0

Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts.

Cryptanalysis and Improvement of a Blockchain-Based Certificateless Signature for IIoT Devices

With the rapid development of the Industrial Internet of Things (IIoT), security and privacy issues have become increasingly prominent when the data from IIoT devices is shared across public networks. Very recently, Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">al.</i> presented a blockchain-based certificateless signature (CLS) scheme for IIoT devices in order to achieve secure and lightweight communication (IEEE Transactions on Industrial Informatics, DOI: 10.1109/TII.2021.3084753). Wang et al. claimed that their CLS scheme is secure under the assumption of the elliptic curve discrete logarithm problem. Unfortunately, by providing two attack methods to analyze the security of Wang et al.'s CLS scheme, we find that it is insecure against universal forgery attacks. Without knowing any information about the target user's private key, the two categories of attackers can successfully forge the valid signature of any message. Hence, Wang et al.'s CLS scheme fails to achieve the claimed security goals. To fix the security flaws, we propose an enhanced blockchain-based and pairing-free CLS scheme, and prove its security in the random oracle model. Furthermore, the analysis results demonstrate that our revised scheme has higher security while keeping the performance of the original scheme.

Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT Communication

The industrial Internet of things (IIoT) is a major lever in Industry 4.0 development, where reducing the carbon footprint and energy consumption has become crucial for modern companies. Today’s IIoT device infrastructure wastes large amounts of energy on wireless communication, limiting device lifetime and increasing power consumption and battery requirements. Communication capabilities seriously affect the responsiveness and availability of autonomous IoT devices when collecting data and retrieving commands to/from higher-level applications. Thus, the objective of optimizing communication remains paramount; in addition to typical optimization methods, such as algorithms and protocols, a new concept is emerging, known as wake-up radio (WuR). WuR provides novel on-demand radio communication schemes that can increase device efficiency. By expanding the lifespan of IoT devices while maintaining high reactivity and communication performance, the WuR approach paves the way for a “place-and-forget” IoT device deployment methodology that combines a small carbon footprint with an extended lifetime and highly responsive functionality. WuR technology, when applied to IoT devices, facilitates green IIoT, thereby enabling the emergence of a novel on-demand IoT (OD-IoT) concept. This article presents an analysis of the state-of-the-art WuR technology within the green IoT paradigm and details the OD-IoT concept. Furthermore, this review provides an overview of WuR applications and their impact on the IIoT, including relevant industry use cases. Finally, we describe our experimental performance evaluation of a WuR-enabled device that is commercially available off the shelf. Specifically, we focused on the communication range and energy consumption, successfully demonstrating the applicability of WuR and the strong potential that it has and the benefits that it offers for sustainable IIoT systems.

Securing IIoT applications in 6G and beyond using adaptive ensemble learning and zero-touch multi-resource provisioning

The advent of Industry 5.0, driven by cutting-edge 6G technologies such as immersive cloud eXtended Reality (XR), Autonomous vehicles, holographic communication, and Digital Twin (DT), is set to trigger a substantial upswing in the deployment of the Industrial Internet of Things (IIoT) devices interconnected within networks. This expansion of IIoT devices will create a wider attack surface and increase the risk of data breaches, privacy violations, and system disruptions. Therefore, it is essential to design innovative mechanisms to ensure the reliability and security of these advanced 6G applications, as well as the IIoT devices that support them. In this context, we propose a novel Software-defined Networking (SDN)-based Ensemble Learning (EL) Framework for Secure IIoT applications in 6G and beyond, called AdaptSDN. The proposed framework leverages SDN technology to dynamically allocate network resources and deploy security measures on demand. It also leverages EL techniques to improve the intrusion detection system’s accuracy. By isolating IIoT devices into network slices, the framework limits the impact of attacks and reduces the potential for cascading failures. Digital twins are used for creating a virtual replica of the IIoT network, allowing for a real-time security threat detection. In particular, AdaptSDN includes three main modules: (1) A novel digital Twin (DT)-enabled data gathering and selection of informative features module to achieve two main objectives: reducing computational complexity and improving detection performance; (2) An SDN-based lightweight adaptive boosting module that uses an advanced boosting EL techniques to dynamically adjust weights and to effectively identify and respond to IIoT attacks in real-time; and (3) A zero-touch resources provisioning module that employs a non-cooperative game theory approach. This approach allows for automatically provisioning various resources in a network to efficiently mitigate network attacks; it enables SDN nodes to obtain the required virtual resources, i.e., storage, computing, and bandwidth, from the main orchestrator with respect to IIoT attack type. We carried out comprehensive experiments to assess the effectiveness of our proposed framework in detecting real-world IIoT attacks. The numerical results confirm that AdaptSDN has the potential to enable secure and reliable IIoT applications in 6G and beyond, meeting the stringent service requirements of the new emerging applications.

Secure STBC-Aided NOMA in Cognitive IIoT Networks

The Industrial Internet of Things (IIoT) has been recognised as having the potential to offer substantial benefit to a wide range of industrial sectors. However, the widespread development and deployment of IIoT pose a set of challenges, including the shortage of spectrum resources and network security. Given the heterogeneity of IIoT devices, conventional cryptographic security techniques are not sufficient, since they suffer from challenges including computation, storages, latency and interoperability. In this paper, we present a physical layer security analysis for cognitive IIoT networks. In our system, IIoT devices opportunistically utilize the primary spectrum; thereby improving spectrum efficiency and allowing access to a large number of devices. Specifically, the considered network uses space-time block coding (STBC) in conjunction with non-orthogonal multiple access (NOMA) in underlay cognitive mode to realize data transmission for IIOT devices with high spectrum efficiency. The secure STBC-NOMA transmission model is established by taking into account the interference from a primary user. New approximate and asymptotic expressions of secrecy outage probability (SOP) for the secondary users (SUs) are derived to characterize the system’s secrecy performance. In addition, the SU’s closed-form secrecy ergodic rate (ER) is provided. The proposed STBC-NOMA approach, when compared to the classic single antenna (SA) based NOMA, achieves better SOP performance for all SUs, as confirmed by both analytical and simulation findings. Furthermore, we show that the proposed STBC-NOMA framework improves the SOP performance of weaker users. Additionally, the STBC-NOMA framework outperforms the SA-NOMA framework in terms of secrecy ER performance for all SUs.

TinyAD: Memory-Efficient Anomaly Detection for Time-Series Data in Industrial IoT

Monitoring and detecting abnormal events in cyber-physical systems is crucial to industrial production. With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices. However, it is most challenging to deploy complex deep learning models such as Convolutional Neural Networks (CNNs) on these memory-constrained IIoT devices embedded with microcontrollers (MCUs). To alleviate the memory constraints of MCUs, we propose a novel framework named Tiny Anomaly Detection (TinyAD) to efficiently facilitate onboard inference of CNNs for real-time anomaly detection. First, we conduct a comprehensive analysis of depthwise separable CNNs and regular CNNs for anomaly detection and find that the depthwise separable convolution operation can reduce the model size by 50-90% compared with the traditional CNNs. Then, to reduce the peak memory consumption of CNNs, we explore two complementary strategies, in-place, and patch-by-patch memory rescheduling, and integrate them into a unified framework. The in-place method decreases the peak memory of the depthwise convolution by sparing a temporary buffer to transfer the activation results, while the patch-by-patch method further reduces the peak memory of layer-wise execution by slicing the input data into corresponding receptive fields and executing in order. Furthermore, by adjusting the dimension of convolution filters, these strategies apply to both univariate time series and multidomain time series features. Extensive experiments on real-world industrial datasets show that our framework can reduce peak memory consumption by 2-5x with negligible computation overhead.

Indoor Localization of Industrial IoT Devices and Applications Based on Recurrent Neural Networks

Indoor Localization of Industrial IoT Devices and Applications Based on Recurrent Neural Networks

Retracted: Detection of DDoS Attack within Industrial IoT Devices Based on Clustering and Graph Structure Features

Retracted: Detection of DDoS Attack within Industrial IoT Devices Based on Clustering and Graph Structure Features

Complex-valued hyperchaos-assisted vector-valued artificial neural key coordination for improving security in the Industrial Internet of Things

This paper presents an innovative solution to the key exchange problem in the Industrial Internet of Things (IIoT) implementation. Communication between connected devices must be safe and effective if the IIoT is to continue its fast growth. A crucial part of safeguarding the security and integrity of data shared across the IIoT ecosystem is played by cryptographic key exchange approaches. To provide reliable cryptographic key exchange in the IIoT context, this paper offers a unique approach that makes use of hyperchaotic with complex values, variable parameters, and vector-valued neural synchronization. The method addresses the continued demand for effective cryptographic key exchange across IIoT devices by using drive–response techniques to speed up important applications. Long assessment times in conventional algorithms make it difficult to hide neuronal coordination. As a result, this paper offers a condensed evaluation of ANNs’ (Artificial Neural Networks) synchronization by coordinating ANNs for session key exchange using a hyperchaotic setting. The recommended approach has several benefits, including the following: (1) introducing a hyperchaotic system to generate synchronized input vectors for ANN synchronization; (2) The adaptive rules of parameters-based control procedures are constructed mathematically; (3) reciprocal alignment of vector-valued ANNs to form a neural network for establishing session keys throughout the IIoT network; and (4) relevant numerical simulations are carried out to assess the scheme’s consistency. The recommended approach performs better than other methods that have been published in the literature, widening up opportunities for more effective and reliable industrial applications.

Provenance blockchain for ensuring IT security in cloud manufacturing

Provenance blockchain is an evolving concept for protection of production, logistics, and supply chain networks from rogue Industrial Internet of Things (IIoT) devices. Such rogue IIoT devices are a recognized threat in the cloud manufacturing networks. In extreme cases, they can be used to cause industrial accidents. A brief version of provenance is about end-to-end tracking and tracing of data and the nodes involved in creating, modifying, transmitting, storing, and deleting it at specific times and locations. It provides an end-to-end verifiable and controlled computation for ensuring trustworthiness, quality, reliability, and validity of data. Provenance has existed in computing using logging software systems. This research is focused on threats to food supply chains between two countries. A scenario for protecting food supply chain from India to UAE has been modeled. This research recognized the threat of harmful food items getting mixed with flow of genuine products in a supply chain. The IIoT devices used to control the flow can be authenticated using the evolving provenance blockchain technology. With the help of recent design recommendations in the literature, a model design has been created and simulated in this research. Observations from the simulation revealed that TCP congestions and unpredictable turnaround time for assigning cryptographic keys to IIoT device sessions may have to be explored in future. A collaborative design between the two nations has been proposed. All IIoT devices not supporting cryptography will be eliminated from the cloud manufacturing and supply chain networks. Currently, this design may be used for one time registration only. Future studies may provide improved versions in which, repeated authentication and keys replacements may be implemented.

An Energy-Efficient Multi-Level Sleep Strategy for Periodic Uplink Transmission in Industrial Private 5G Networks.

This paper proposes an energy-efficient multi-level sleep mode control for periodic transmission (MSC-PUT) in private fifth-generation (5G) networks. In general, private 5G networks meet IIoT requirements but face rising energy consumption due to dense base station (BS) deployment, particularly impacting operating expenses (OPEX). An approach of BS sleep mode has been studied to reduce energy consumption, but there has been insufficient consideration for the periodic uplink transmission of industrial Internet of Things (IIoT) devices. Additionally, 5G New Reno's synchronization signal interval limits the effectiveness of the deepest sleep mode in reducing BS energy consumption. By addressing this issue, the aim of this paper is to propose an energy-efficient multi-level sleep mode control for periodic uplink transmission to improve the energy efficiency of BSs. In advance, we develop an energy-efficient model that considers the trade-off between throughput impairment caused by increased latency and energy saving by sleep mode operation for IIoT's periodic uplink transmission. Then, we propose an approach based on proximal policy optimization (PPO) to determine the deep sleep mode of BSs, considering throughput impairment and energy efficiency. Our simulation results verify the proposed MSC-PUT algorithm's effectiveness in terms of throughput, energy saving, and energy efficiency. Specifically, we verify that our proposed MSC-PUT enhances energy efficiency by nearly 27.5% when compared to conventional multi-level sleep operation and consumes less energy at 75.21% of the energy consumed by the conventional method while incurring a throughput impairment of nearly 4.2%. Numerical results show that the proposed algorithm can significantly reduce the energy consumption of BSs accounting for periodic uplink transmission of IIoT devices.

Vulnerability Exploitation Risk Assessment Based on Offensive Security Approach

Security incidents targeting control systems and the industrial internet of things (IIoT) are on the rise as attackers gain a better understanding of the nature of these systems and their increasing connectivity to information technology (IT). Every year, the number of vulnerabilities associated with these incidents increases, making it impractical to apply timely patches for all of them. The current vulnerability assessments, which are the basis for vulnerability patching, have limitations in that they do not adequately reflect the risk of exploitation in the real world after discovery and do not consider operational technology (OT) and industrial control system (ICS) environments other than IT environments. This study proposes to evaluate exploit risk in real-world environments by considering OT/ICS environments and calculating three metrics, including exploit chain risk, exploit code availability, and exploit use probability based on cyber threat information, including IIoT vulnerability data, used in OT/ICS environments. In addition, we construct exploitation scenarios in a control system environment to prioritize vulnerabilities with a high risk of exploitation based on the three metrics. We show that by assessing the risk of attackers’ intentions and exploited technologies for attacks against IIoT devices in a control system environment, we can provide defenders with comprehensive attack risk information for proactive defense.

Privacy-preserving trust management method based on blockchain for cross-domain industrial IoT

The Industrial Internet of Things (IIOT) contains many devices from different autonomous domains (e.g., factories), which need to cooperate to complete complex manufacturing process. Devices from different domains collaborate with each other, which greatly raises trust concerns about device-to-device interactions. Existing trust management approaches may result in the risk of privacy leakage and low accuracy of trust evaluation. Thus, trust issues during interaction remain unsolved but imperative. In this paper, we propose a blockchain-based privacy-preserving trust management architecture PPTMA. Specifically, PPTMA adopts federated learning to train a task-specific trust model for different collaborative task. Our work is the first attempt to research the relationship between the weight calculation of trust metric and the change of context in the field of trust management. To preserve the privacy of devices, differential privacy (DP) is exploited during the trust evaluation process. In addition, a game theory-based incentive mechanism is proposed to encourage the IIOT device for actively and honestly submitting the trust data into the blockchain as so to promote the accuracy of trust computing. Finally, we also design a parallel consensus protocol (OPBFT) which realizes an assembly line to speed up the efficiency of the consensus process. The idea of consensus assembly line firstly proposed by us brings new opportunities for improving the consensus efficiency. Extensive experiments have been conducted to show the effectiveness and efficiency of the proposed method.

A Survey on the Role of Industrial IoT in Manufacturing for Implementation of Smart Industry.

The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented.

Fingerprinting Industrial IoT devices based on multi-branch neural network

Industrial Internet-of-Things systems suffer from a vast and vulnerable attack surface, raising widespread concerns about shielding IIoT devices from malicious attacks and reducing cyber risks. Device identification is the prerequisite to safeguard IIoT systems. We leverage the observation that IIoT network protocol implementations vary due to different hardware architectures/configurations and design tasks of IIoT devices, which cause the difference in their network traffic payloads. Specifically, we develop a novel neural network to learn the semantic/syntax features among multiple IIoT packets. The neural network has multiple branches, each of which consists of convolution layers, attention modules, and highway units for learning the classification model of IIoT devices. To validate the precision and recall of our neural network in IIoT devices fingerprinting, we have implemented a prototype of the proposed IIoT device identification system. Our results show that our approach achieves 95.8% precision and 95.4% recall, significantly outperforming other classification models.

HPCchain: A Consortium Blockchain System Based on CPU-FPGA Hybrid-PUF for Industrial Internet of Things

Industrial Internet of Things (IIoT) is experiencing rapid developments in the era of Industry 4.0. However, the ever-increasing applications put forwards higher requirements for authentication. Facing such a problem, researchers combine two cutting-edge techniques, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> Physical Unclonable Function (PUF) and blockchain. In detail, PUF can generate multiple Challenge-Response Pairs (CRPs) for IIoT devices by leveraging their unique physical features. Moreover, blockchain platforms are employed for storing/synchronizing CRPs, thereby resisting the single-point-failure. Although realizing the unclonable authentications, the existing works ignore the device heterogeneity of IIoT and fail to develop the specified blockchain platform for supporting PUF. In this article, we present a Hybrid PUF-based consortium blockchain for IIoT authentication, named HPCchain. Specifically, we first present the notion of Hybrid-PUF, which assigns different devices to generate different types of PUFs, and then employs them to play different roles in HPCchain. In this way, we can overcome the IIoT heterogeneity. Moreover, we propose the PUF-empowered credit scheme for HPCchain and realize the dynamic endorsement, with which we develop a PUF-based consensus mechanism for HPCchain. Finally, we design the registration and authentication schemes for IIoT nodes, atop HPCchain. Extensive experiments demonstrate the validity of our proposals.

FOG-Empowered Augmented-Intelligence-Based Proactive Defensive Mechanism for IoT-Enabled Smart Industries

The recent innovations in the network communication domain have entirely revolutionized the conventional industrial sector by introducing a new era of automatic communication. The Industrial Internet of Things (IIoT) is acknowledged as an exclusive space where IoT seems to divulge expressive manifestations. However, the expanded connectivity, more openness, and the widespread use of low-power communication devices of IIoT makes them vulnerable to malicious attacks, and criminal activities. Moreover, the heterogeneous and prevalent nature of the IIoT devices makes it very difficult to come up with a centralized threat detection mechanism, thus its security remains a major concern. Motivated by this, we propose a fog-empowered Augmented Intelligence (IA)-based defensive mechanism to ensure secure communication in IoT-enabled smart industries. The designed framework incorporates the aggregated potentials of two highly acclaimed Deep Learning (DL) classifiers: Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM). Where the DL-based scheme detects anomalies in such industrial networks and the FOG-based scenario promotes the routing flexibility and interoperability of the heterogeneous devices of the IoT-enabled smart industrial network. The validity of the proposed mechanism is analytically investigated by conducting a comparison with the benchmark threat detection techniques. The proposed solution is also generically examined parallel to some state-of-the-art approaches. The Cu-GRU-BiLSTM framework achieved up to 99.91% accuracy with a low false alarm rate and outperforms the baseline and recent threat detection approaches. The simulation and comparison results validate the effectiveness of the proposed mechanism and advocate it as a phenomenal choice to ensure efficacious and secure communication in IoT-based smart industries.