Industrial Internet Of Things Environment Research Articles

An end-to-end learning approach for enhancing intrusion detection in Industrial-Internet of Things

The Industrial-Internet of Things (I-IoT) stands out as one of the most dynamically evolving subfields within the expansive realm of the Internet of Things (IoT). Its exponential growth is reshaping industrial landscapes, bringing forth transformative innovations and advancements at an unprecedented pace, as the core of Industry 4.0. Among the formidable challenges faced by the Industrial-Internet of Things, cybersecurity stands out as a critical concern. Deep learning-based Intrusion Detection System (IDS) solutions showcase their steadfast ability to secure resource-limited, investigation-demanding, and complex I-IoT environments. However, their effectiveness hinges not only on the model but also on the dataset on which they are trained. While numerous literature studies delve into this field, existing proposed models often grapple with challenges. They are frequently trained on outdated, non-diverse datasets or lack specific features crucial for I-IoT networks. Recent efforts, thankfully, introduce more adequate datasets like Edge-IIoTset. Researchers leverage this extensive dataset to train models, focusing on detecting the 14 sophisticated attacks. These attacks predominantly target real I-IoT networks. Despite these efforts, none of the existing models proves entirely efficient. A review of literature solutions reveals that many models cannot detect all 15 classes in the dataset. Some are multi-staged or overly complex. In response to these challenges, this paper presents an End-to-End learning , non-complex CNN1D model tailored to the specific problem of detecting 14 sophisticated threats targeting I-IoT environments. Our proposed model demonstrated remarkable efficiency with an accuracy of 99.96%, successfully detecting all 15 classes in the Edge-IIoTset dataset with a minimal loss of 0.0011. Not only that, but our model was validated with k-fold cross-validation, demonstrating its efficiency in preserving the same performance on unseen data and its ability to be generalized for real-world I-IoT environments.

Deep learning enabled intrusion detection system for Industrial IOT environment

The prevalence of security vulnerabilities in Internet of Things (IoT) applications poses a serious threat to enterprise systems, necessitating sophisticated and reliable defense solutions to counter emerging and evolving threats. For the Industrial Internet of Things (IIoT), stakeholders require trustworthy and sustainable systems that can prevent the loss of human life during critical operations. The impact of multi-variant persistent and sophisticated bot attacks on connected IIoTs is potentially catastrophic, and their detection presents a highly complex and critical challenge. Therefore, there is a pressing need for efficient and timely detection of IIoT botnet attacks. This research paper proposes a robust deep learning model named AttackNet for the detection and classification of different botnet attacks in IIoT based on adaptive based CNN-GRU model. The model is extensively evaluated using the latest dataset and standard performance evaluation metrics, demonstrating its capacity to protect IIoT networks against sophisticated cyber-attacks with a testing accuracy of 99.75%, a loss of 0.0063, precision and recall score of 99.75% and 99.74% respectively. Our proposed model demonstrates superior accuracy, particularly within the N_BaIoT dataset. It achieves an outstanding accuracy of 99.75% across ten classes, surpassing state-of-the-art techniques by a substantial margin ranging from 3.2% to 16.07%. Moreover, the proposed model outperforms state-of-the-art anomaly detection systems in IIoT based on a real-time IoT device dataset in terms of detecting and classifying botnet attacks accurately.

Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning Model for IIoT Environment

Intrusion Detection in the Industrial Internet of Things (IIoT) concentrations on the security and safety of critical structures and industrial developments. IIoT extends IoT principles to industrial environments, but linked sensors and devices can be deployed for monitoring, automation, and control of manufacturing, energy, and other critical systems. Intrusion detection systems (IDS) in IoT drive to monitor network traffic, device behavior, and system anomalies for detecting and responding to security breaches. These IDS solutions exploit a range of systems comprising signature-based detection, anomaly detection, machine learning (ML), and behavioral analysis, for identifying suspicious actions like device tampering, unauthorized access, data exfiltration, and denial-of-service (DoS) attacks. This study presents an Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning (IID-SBODL) model for IIoT Environment. The IID-SBODL technique initially preprocesses the input data for compatibility. Next, the IID-SBODL technique applies Echo State Network (ESN) model for effectual recognition and classification of the intrusions. Finally, the SBO algorithm optimizes the configuration of the ESN, boosting its capability for precise identification of anomalies and significant security breaches within IIoT networks. By widespread simulation evaluation, the experimental results pointed out that the IID-SBODL technique reaches maximum detection rate and improves the security of the IIoT environment. Through comprehensive experimentation on both UNSW-NB15 and UCI SECOM datasets, the model exhibited exceptional performance, achieving an average accuracy of 99.55% and 98.87%, precision of 98.90% and 98.93%, recall of 98.87% and 98.80%, and F-score of 98.88% and 98.87% for the respective datasets. The IID-SBODL model contributes to the development of robust intrusion detection mechanisms for safeguarding critical industrial processes in the era of interconnected and smart IIoT environments.

Proxy re-encryption with plaintext checkable encryption for integrating digital twins into IIoT

Digital Twin (DT) technology has emerged as a robust mechanism for overseeing and improving the lifecycle processes of the Industrial Internet of Things (IIoT) by creating their virtual counterparts. The widespread employment of cloud computing is apparent in delivering computational and storage services for the DT within the IIoT environment. Ensuring the confidentiality of DT data is crucial since data owners, cloud service providers, and users operate across separate trust domains. Accordingly, public key encryption is a viable solution for managing access. However, traditional public key encryption encounters challenges in achieving effective search and secure sharing of encrypted DT data in the cloud. This paper introduces a proxy re-encryption scheme with plaintext checkable encryption for data access control (PRE-IBSC-PCE), which addresses the challenges associated with searching for and sharing encrypted digital twin data in the cloud. The PRE-IBSC-PCE achieves efficient data search and sharing while ensuring privacy, integrity, non-repudiation, and authenticity. Meanwhile, we prove that PRE-IBSC-PCE is secure under the decisional bilinear Diffie–Hellman assumption in the random oracle model. Lastly, when compared to similar schemes, the performance analysis of the PRE-IBSC-PCE is more efficient and suitable for DT applications in IIoT.

Enhanced pelican optimization algorithm with ensemble-based anomaly detection in industrial internet of things environment

Enhanced pelican optimization algorithm with ensemble-based anomaly detection in industrial internet of things environment

Proof-of-authority-based secure and efficient aggregation with differential privacy for federated learning in industrial IoT

The industrial internet of things (IIoT) uses connected devices and sensors to improve efficiency in industry, but increased reliance on these systems makes them prone to faults. To ensure the reliability of IIoT systems, robust fault detection is necessary, which can be provided by artificial intelligence (AI). The current state-of-the-art approach for AI in IIoT is mainly focused on centralized learning, which is inefficient due to its high communication cost. Federated learning (FL) addresses this limitation by enabling distributed training without exposing individual information. In order to provide secure and efficient FL for the IIoT environment, several research studies have investigated the use of blockchain networks. However, most of these studies ignore the processing time metric, which is crucial for IIoT networks. In this paper, we propose a secure and efficient parameter aggregation technique that enhances trust, security, and privacy for IIoT. We use a lightweight smart contract deployed with a proof-of-authority (PoA)-based blockchain in combination with a Gaussian differential privacy mechanism. We evaluate our proposed system using a real bearing fault dataset and demonstrate that it is able to provide a secure aggregation process with an accuracy of 94.00% and a processing time of 1.54 s, which is suitable for the IIoT environment with a private blockchain network.

Improving the Reliability of Long-Range Communication against Interference for Non-Line-of-Sight Conditions in Industrial Internet of Things Applications

LoRa technology, renowned for its low-power, long-range capabilities in IoT applications, faces challenges in real-world scenarios, including fading channels, interference, and environmental obstacles. This paper aims to study the reliability of LoRa in Non-Line-of-Sight (NLoS) conditions and in noisy and mobile environments for Industrial IoT (IIoT) applications. Experimental measurements consider factors like vegetation and infrastructure, introducing mobility to replicate NLoS conditions. Utilizing an open-source LoRa Physical Layer (PHY) Software-Defined Radio (SDR) prototype developed with GNU Radio, we assess communication reliability through metrics such as Block Error Rate (BLER), Signal-to-Noise-Interference-plus-Noise Ratio (SINR), and data rate. The study reveals the estimated overall reliability of the LoRa signal at 90.23%, emphasizing specific configuration details. This work contributes to the broader field of LoRa communication, encompassing hardware, software, protocols, and management, enhancing our understanding of LoRa’s dependability in challenging IIoT environments.

Ransomware Threats in Industrial Internet of Things Networks: A Detection Approach

The Industrial Internet of Things (IIoT) is a challenging environment for ransomware threats, and it requires robust detection mechanisms to protect critical infrastructures. This study explores the complex landscape of ransomware attacks in IIoT and suggests proactive detection strategies. To develop an advanced detection model, this research uses the CATBoost algorithm that can handle categorical features by leveraging a comprehensive dataset that captures various attributes of ransomware incidents. The study also enhances the interpretability of the model by incorporating SHAP (SHapley Additive exPlanations) which explains how individual features affect ransomware identification in IIoT environments. Empirical evaluation demonstrates that the model can accurately classify ransomware instances with high precision and recall rates. Moreover, SHAP explanation reveals important features that influence the decisions made by the model thereby improving its interpretability and trustworthiness. The experimental results indicate that customized detection approaches are important and highlight the effectiveness of CATBoost algorithm in strengthening IIoT systems against ransomware attacks.

GpDB: A Graph-partition Based Storage Strategy for DAG-Blockchain in Edge-cloud IIoT

The industrial Internet-of-things (IIoT) has attracted extensive attention due to its real-time and automation characteristics. Edge computing and blockchain technologies facilitate the IIoT in terms of low latency services and data security respectively. However, with the continuous expansion of industrial data and the growth of industrial nodes, traditional blockchain technology has some critical limitations on low transaction throughput and high data storage costs. Directed Acyclic Graph (DAG)-blockchain adopts a graph structure of a single transaction as the basic unit, and it has the characteristics of asynchronous consensus. Some existing studies use DAGblockchain to replace the traditional blockchain to alleviate its low throughput problems like IOTA. However, with the rapid data generation in the IIoT environment, the topology scale of DAG-blockchain will increase sharply, which will aggravate the data storage cost of blockchain nodes. In this article, to reduce the data storage cost of edge servers, we design a Graphpartition based storage strategy for DAG-Blockchain (GpDB), equipped with a graph partition algorithm based on transaction freshness, which can partition DAG-blockchain topology in edge servers into two parts, which will be retained and removed respectively. Simulation shows that, in terms of storage cost, GpDB outperforms LDV and Layerchain by 62% and 74% respectively, and with the increasing number of transactions, GpDB has good scalability in reducing the storage cost, and better transaction throughput than IOTA.

Real-Time Adaptive Anomaly Detection in Industrial IoT Environments

Real-Time Adaptive Anomaly Detection in Industrial IoT Environments

Provably secure lightweight certificateless lattice‐based authenticated key exchange scheme for IIoT

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.

A multimodal data sensing and feature learning-based self-adaptive hybrid approach for machining quality prediction

With the rapid development of advanced manufacturing and information technologies, smart manufacturing has been proposed and gained wide attention. As the crucial section of achieving smart manufacturing, machining quality prediction aims to use data and statistical methods to predict the quality level of products or services during manufacturing, service provision, or product delivery. However, most data-driven methods suffer from using unimodal data, ignoring the complementary of other modal data, and using a single model in one prediction task, ignoring model unstable fluctuations corresponding to different features. To bridge these gaps, a novel self-adaptive hybrid approach is proposed for obtaining high accuracy of machining quality prediction in the industrial IoT environment. Firstly, a prototype system is designed to collect and perceive multimodal data in the manufacturing workshop/system, which enables the collection and perception of more comprehensive multimodal data of physical space. Secondly, the integration of sensors, data acquisition instruments, and ontology models is used to perform the process environment, which enables the construction of information space to store and transmit multimodal data. Meanwhile, the principal component analysis and model training sensed data are used to perform the machine learning model, which enables the construction of different information models. Thirdly, a probability model matrix-based self-adaptive hybrid prediction strategy is used to perform the machining quality prediction, which enables a better fitting degree for the quality prediction task. Finally, a real-life case study and extensive experiments are conducted to verify the effectiveness and superiority of the proposed self-adaptive hybrid approach.

A Dynamic IIoT Framework Based on the Publish-Subscribe Paradigm.

The use of the Internet of Things (IoT) technologies and principles in industrial environments is known as the Industrial Internet of Things (IIoT). The IIoT concept aims to integrate various industrial devices, sensors, and actuators for collection, storage, monitoring, and process automation. Due to the complexity of IIoT environments, there is no one-size-fits-all solution. The main challenges in developing an IIoT solution are represented by the diversity of sensors and devices, connectivity, edge/fog computing, and security. This paper proposes a distributed and customized IioT (Industrial Internet of Things) framework for the interaction of things from the industrial environment. This framework is distributed on the fog nodes of the IIoT architecture proposed, and it will have the possibility to interconnect local things (with low latency) or global things (with a latency generated by the Internet network). To demonstrate the functionality of the proposed framework, it is included in the fog nodes presented in other paper. These fog nodes allow the integration of CANOpen networks into an IioT architecture. The most important advantages of the proposed architecture are its customizability and the fact that it allows decision operations to be carried out at the edge of the network to eliminate latency due to the Internet.

Retracted: Mitigation Impact of Energy and Time Delay for Computation Offloading in an Industrial IoT Environment Using Levenshtein Distance Algorithm

Retracted: Mitigation Impact of Energy and Time Delay for Computation Offloading in an Industrial IoT Environment Using Levenshtein Distance Algorithm

An Adaptive Parallel Feature Learning and Hybrid Feature Fusion-Based Deep Learning Approach for Machining Condition Monitoring.

The rapid development of information and communication technologies has facilitated machining condition monitoring toward a data-driven paradigm, of which the Industrial Internet of Things (IIoT) serves as the fundamental basis to acquire data from physical equipment with sensing technologies as well as to learn the relationship between the system condition and the collected condition monitoring data. However, most data-driven methods suffer from using a single-domain space, ignoring the importance of the learned features, and failing to incorporate the handcrafted features assisted by domain knowledge. To solve these limitations, a novel deep learning approach is proposed for machining condition monitoring in the IIoT environment, which consists of three phases, including: 1) the unsupervised parallel feature extraction; 2) adaptive feature importance weighting; and 3) hybrid feature fusion. First, separate sparse autoencoders are utilized to conduct the unsupervised parallel feature extraction, which enables to learn abstract feature representation from multiple domain spaces simultaneously. Then, an attention module is designed for the adaptive feature importance weighting, which can assign higher weights to those critical features accordingly. Moreover, a hybrid feature fusion is deployed to complement the automatic feature learning and further yield better model performance by fusing the handcrafted features assisted by domain knowledge. Finally, a real-life case study and extensive experiments have been conducted to show the effectiveness and superiority of the proposed approach.

Energy-Efficient Deep Neural Networks for EEG Signal Noise Reduction in Next-Generation Green Wireless Networks and Industrial IoT Applications

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.

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

We propose and evaluate a data-driven intrusion detection system (IDS) for the Internet of Things (IoT) and Industrial IoT (IIoT) environments using the Edge-IIoT-2022 dataset. We model the IDS problem as a classification problem and learn the classifier via supervised learning algorithms. Our main contribution is an empirical analysis and evaluation of the Edge-IIoT-2022 dataset, which is a recent dataset compiled for developing IDSs in IoT and IIoT environments. We develop several IDSs from standard data analytics algorithms and evaluate their performance on Edge-IIoT-2022. We compare our IDSs with prior arts and demonstrate that highly accurate binary-class IDSs can be built via Edge-IIoT-2022, whereas multi-class IDSs would require careful treatment.

Fuzzy Logic and Its Application in the Assessment of Information Security Risk of Industrial Internet of Things

This article addresses the issue of information security in the Industrial Internet of Things (IIoT) environment. Information security risk assessment in the IIoT is complicated by several factors: the complexity and heterogeneity of the system, the dynamic nature of the system, the distributed network infrastructure, the lack of standards and guidelines, and the increased consequences of security breaches. Given these factors, information security risk assessment in the IIoT requires a comprehensive approach adapted to the peculiarities and requirements of a particular system and industry. It is necessary to use specialized risk assessment methods and to take into account the context and peculiarities of the system. The method of information security risk assessment in the IIoT, based on the mathematical apparatus of fuzzy set theory, is proposed. This paper analyzes information security threats for IIoT systems, from which the most significant criteria are selected. The rules, based on which decisions are made, are formulated in the form of logical formulas containing input parameters. Three fuzzy inference systems are used: one to estimate the probability of threat realization, another to estimate the probable damage, and a final one to estimate the information security risk for the IIoT system. Based on the proposed method, examples of calculating the information security risk assessment in the IIoT environment are provided. The proposed scientific approach can serve as a foundation for creating expert decision support systems for designing IIoT systems.

FL-CTIF: A federated learning based CTI framework based on information fusion for secure IIoT

Industrial Internet of Things (IIoT) are evolving and transitioning into virtual entities supported by digital twin technology to optimize existing processes further and improve their efficiency. The virtual representation of the several IIoT departments, such as manufacturing process, packaging, warehousing, and logistics enables predictive maintenance for optimizing local material resources for manufacturing and packaging, improved warehouse space allocation, and delivery schedule optimization. The high volume of data generated from IoT devices are exposed to cyber-attacks using modern attack vectors and thus threaten both physical IIoT environment and its digital twins. Existing intrusion detection systems focus on identifying anomalous traffic using obsolete knowledge of cyber-attack patterns for several attacks, such as ARP poisoning man-in-the-middle attacks, SSL-based attacks using encrypted traffic, and DNS flood-based DDoS traffic. A lack of focus on security using updated and comprehensive Cyber Threat Intelligence (CTI) threatens the security of the IIoT environment. In this paper, we present a Federated Learning based CTI framework based on Information Fusion (FL-CTIF) for securing IIoT environments. We design a comprehensive cyberattack dataset with updated feature selection using Information Fusion to improve the accuracy in identifying cyber-attacks. A federated learning-based ANN model is designed to reduce the model training rounds based on satisfaction level and measure the absence of improvement in each attack vector's average accuracy. The model is evaluated based on model aggregation performance and demonstrates an improved F1 score based on reduced training rounds and reduced CPU consumption. The federated learning model improves accuracy and reduces the false positive rate using the newly merged dataset compared to existing research.

Incentive-Based Federated Learning for Digital-Twin-Driven Industrial Mobile Crowdsensing

Mobile crowdsensing has empowered the Industrial Internet of Things (IIoT) in many ways, such as vehicle-aided traffic flow scheduling and drone-aided visual inspections, etc. However, dynamic perception and cooperative decision-making among these heterogeneous and resource-constrained mobile clients in IIoT remains a big challenge. In this paper, we propose an incentive-based federated learning scheme for digital twin driven industrial mobile crowdsensing. Specifically, we first design a digital twin driven industrial mobile crowdsensing architecture to achieve dynamic perception of the complex IIoT environment, among heterogeneous and resource-constrained mobile clients. Second, we develop a novel incentive-based federated learning framework incorporated with a contract-based reputation mechanism and a Stackelberg-based inter-client incentive mechanism, to optimize the model accuracy. Third, we devise a knowledge distillation algorithm for the federated learning framework, to address the heterogeneity of non-independent and identically distributed (Non-IID) data. Extensive experiments on both MNIST/FEMNIST and CIFAR10/100 datasets demonstrate the outperformance of our proposed scheme, in terms of model accuracy, incentive fairness, and data compatibility, compared to state-of-the-art studies.