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In the IIoT, billions of devices continually provide information that is extremely diverse, variable, and large-scale and presents significant hurdles for interpretation and analysis. Additionally, issues about data transmission, scaling, computation, and storage can result in data anomalies that significantly affect IIoT applications. This work presents a novel anomaly detection framework for the IIoT in the context of the challenges posed by vast, heterogeneous, and complex data streams. This paper proposes a two-staged multi-variate approach employing a composition of long short-term memory (LSTM) and a random forest (RF) Classifier. Our approach leverages the LSTM's superior temporal pattern recognition capabilities in multi-variate time-series data and the exceptional classification accuracy of the RF model. By integrating the strengths of LSTM and RF models, our method provides not only precise predictions but also effectively discriminates between anomalies and normal occurrences, even in imbalanced datasets. We evaluated our model on two real-world datasets comprising periodic and non-periodic, short-term, and long-term temporal dependencies. Comparative studies indicate that our proposed method outperforms well-established alternatives in anomaly detection, highlighting its potential application in the IIoT environment. [ABSTRACT FROM AUTHOR]

In an era where Information and Communication Technologies (ICT) redefine the boundaries of competition and collaboration, the concept of coopetition — simultaneous competition and cooperation — emerges as a strategic imperative for small and medium-sized enterprises (SMEs). This study presents the design and development of an Industrial Internet of Things (IIoT) artefact designed to enable competition among SMEs. Bringing the Service-Dominant Logic (S-D logic) foundations to IIoT, a Design Science Research (DSR) approach was employed. This approach facilitated the integration of design theory with practical problem-solving, creating the Coopetition IIoT-based System. The primary goal of this solution is to augment the dynamics of coopetition networks, with a particular focus on SMEs. The practical evaluation of the Coopetition IIoT-based System is assessed through a prototype evaluation by experts representing twenty-four manufacturing stone SMEs, a crucial sector in the Portuguese economy. The feedback received was highly positive, indicating a positive evaluation rate of 78.9%. This favourable response highlights the Coopetition IIoT-based System's proficiency in fostering simultaneous competition and cooperation throughout the lifecycle of business opportunities in SME networks, thereby underscoring its potential as a facilitator of effective competition. Theoretically, this research enriches the application of S-D logic in coopetition networks and advances state-of-the-art IIoT systems. Practically, the Coopetition IIoT-based System demonstrates significant potential in boosting the competitiveness of SMEs in developed economies. Nonetheless, the ultimate efficacy of such IIoT systems will be best determined through real-world application and evaluation. Future research should concentrate on the real-case deployment and assessment of Coopetition IIoT-based Systems within coopetition networks. [ABSTRACT FROM AUTHOR]

In recent years, with the application of Internet of Things (IoT) and cloud technology in smart industrialization, Industrial Internet of Things (IIoT) has become an emerging hot topic. The increasing amount of data and device numbers in IIoT poses significant challenges to its security issues, making anomaly detection particularly important. Existing methods for anomaly detection in the IIoT often fall short when dealing with data imbalance, and the huge amount of IIoT data makes feature selection challenging and computationally intensive. In this paper, we propose an optimal deep learning model for anomaly detection in IIoT. Firstly, by setting different thresholds of eXtreme Gradient Boosting (XGBoost) for feature selection, features with importance above the given threshold are retained, while those below are ignored. Different thresholds yield different numbers of features. This approach not only secures effective features but also reduces the feature dimensionality, thereby decreasing the consumption of computational resources. Secondly, an optimized loss function is designed to study its impact on model performance in terms of handling imbalanced data, highly similar categories, and model training. We select the optimal threshold and loss function, which are part of our optimal model, by comparing metrics such as accuracy, precision, recall, False Alarm Rate (FAR), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision–Recall Curve (AUC-PR) values. Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. Experiments are conducted using the UNSW-NB15 and NSL-KDD datasets. The proposed MIX_LSTM model can achieve 0.084 FAR, 0.984 AUC-ROC, and 0.988 AUC-PR values in the binary anomaly detection experiment on the UNSW-NB15 dataset. In the NSL-KDD dataset, it can achieve 0.028 FAR, 0.967 AUC-ROC, and 0.962 AUC-PR values. By comparing the evaluation indicators, the model shows good performance in detecting abnormal attacks in the Industrial Internet of Things compared with traditional deep learning models, machine learning models and existing technologies. [ABSTRACT FROM AUTHOR]

This work introduces an intrusion detection system (IDS) tailored for industrial internet of things (IIoT) environments based on an optimized convolutional neural network (CNN) model. The model is trained on a dataset that was balanced using a novel multi-class implementation of synthetic minority over-sampling technique (SMOTE) that ensures equal representation of all classes. Additionally, systematic optimization will be used to fine tune the hyperparameters of the CNN model and mitigate the effects of the increased size of the training dataset. Evaluation results will demonstrate substantial improvement in performance when the optimized CNN model is trained on the balanced dataset. The proposed IDS will be evaluated using the IIoT-specific WUSTL-IIOT-2021 dataset, and then its generalization capability will be verified using the non-domain specific UNSW_NB15 dataset. The model's performance will be evaluated using accuracy, precision, recall, and F1-score metrics. The results will demonstrate that the proposed IDS is highly effective with performance exceeding 99.9% on all performance metrics. The IDS is also highly effective in detecting intrusion for generic IT networks achieving improvements in excess of 30% compared to the default baseline model. The results emphasize the versatility and effectiveness of the proposed IDS model, making it a reliable and adaptable solution for enhancing network security across diverse network environments. [ABSTRACT FROM AUTHOR]

Industrial internet of things (IIoT) is considered as large-scale IoT-based network comprising of sensors, communication channels, and security protocols used in Industry 4.0 for diverse real-time operations. Industrial IoT (IIoT) networks are vulnerable to diverse cyber threats and attacks. Attack detection is the biggest security issue in the IIoT. Various traditional attack detection methods are proposed by several researchers but all are insufficient to protect privacy and security. To address the issue, a novel Gradient Descent Scaling and Segmented Regression Fine-tuned Federated Learning (GDS-SRFFL) method is introduced for IIoT network attack detection. The aim of the GDS-SRFFL method is to enhance the security of an IIoT network. Initially, the novelty of Gradient Descent Scaling-based preprocessing is applied to the raw dataset for obtaining feature feature-scaled preprocessed network sample. Then, the unwanted intrusions are discovered by using a Segmented Regression Fine-tuned Mini-batch Federated Learning model to ensure the protection of IoT networks with the novelty of SoftMax Regression. In order to validate the proposed methodology, experimentations were conducted on different parameters, namely accuracy, precision, recall, specificity, and attack detection time, and the results concluded that proposed GDS-SRFFL has improved accuracy by 10%, precision by 13%, recall by 10%, specificity by 11% as well as minimum attack detection time by 28% as compared to existing techniques like CNN + LSTM (Altunay and Albayrak in Eng Sci Technol Int J 38:101322, 2023, https://doi.org/10.1016/j.jestch.2022.101322), Enhanced Deep and Ensemble learning in SCADA-based IIoT network (Khan et al. in IEEE Trans Ind Inf 19(1):1030–1038, https://doi.org/10.1109/TII.2022.3190352), RNN (Ullah and Mahmoud in IEEE Access 10:62722–62750, 2022, https://doi.org/10.1109/ACCESS.2022.3176317), and other CNN methods. The proposed method "GDS-SRFFL" has overall accuracy of 89.42% as compared to other existing methods. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) envisions enhanced surveillance and control for industrial applications through diverse IoT devices. However, the increasing heterogeneity of deployed end devices poses challenges to current practices, hampering overall performance as device numbers escalate. To tackle this issue, we introduce an innovative distributed power control algorithm leveraging the wireless channel's nature to approximate the centralized maximum-weight scheduling algorithm. Employing ubiquitous multi-protocol mobile devices as intermediaries, we propose a concurrent dual-hop/multi-hop backhauling strategy, improving interoperability and facilitating data relay, translation, and forwarding from end IoT devices. Our focus is directed towards addressing large-scale network stability and queue management challenges. We formulate a long-term time-averaged optimization problem, incorporating considerations of end-to-end rate control, routing, link scheduling, and resource allocation to guarantee essential network-wide throughput. Furthermore, we present a real-time decomposition-based approximation algorithm that ensures adaptive resource allocation, queue stability, and meeting Quality of Service (QoS) constraints with the highest energy efficiency. Comprehensive numerical results verify significant energy efficiency improvements across diverse traffic models, maintaining throughput requirements for both uniform and hotspot User Equipment (UE) distribution patterns. This work offers a comprehensive solution to enhance IIoT performance and address evolving challenges in industrial applications. [ABSTRACT FROM AUTHOR]

Time-slotted Channel Hopping (TSCH) Media Access Control (MAC) was specified to target the Industrial Internet of Things needs. This MAC balances energy, bandwidth, and latency for deterministic communications in unreliable wireless environments. Building a distributed or autonomous TSCH schedule is arduous because the node negotiates cells with its neighbours based on queue occupancy, latency, and consumption metrics. The Minimal TSCH Configuration defined by RFC 8180 was specified for bootstrapping a 6TiSCH network and detailed configurations necessary to be supported. In particular, it adopts Routing Protocol for Low Power and Lossy networks (RPL) Non-Storing mode, which reduces the node’s network awareness. Dealing with unpredicted traffic far from the forwarding node is difficult due to limited network information. Anticipating this unexpected flow from multiple network regions is essential because it can turn the forwarding node into a network bottleneck leading to high latency, packet discard or disconnection rates, forcing RPL to change the topology. To cope with that, this work proposes a new mechanism that implements an RPL control message option for passing forward the node’s cell demand, allowing the node to anticipate the proper cell allocation for supporting the traffic originating by nodes far from the forwarding point embedded in Destination-Oriented Directed Acyclic Graph (DODAG) Information Object (DIO) and Destination Advertisement Object (DAO) RPL control messages. Implementing this mechanism in a distributed TSCH Scheduling developed in Contiki-NG yielded promising results in supporting unforeseen traffic bursts and has the potential to significantly improve the performance and reliability of TSCH schedules in challenging network environments. [ABSTRACT FROM AUTHOR]

With the improvement of network transmission level and hardware storage and computing capacity, the Internet of Things (IoT) has been rapidly developed, and its application has attracted extensive attention. As an innovative application of Internet of Things technology in the field of industrial automation production, the Industrial Internet of Things includes technologies such as edge computing, big data analysis and intelligent sensing, which greatly improves production efficiency and reduces management costs. At present, Industrial Internet of Things (IIoT) has become the key competitiveness of the country. However, the heterogeneity of equipment, the limitation of resources and the low security of data have greatly hindered the development of IIoT. Therefore, in terms of ensuring efficient energy utilization, a trusted relay routing mechanism for optimizing chain performance based on multi-attribute comprehensive evaluation for the IIoT is proposed. It is an improvement of the low-energy adaptive clustering hierarchy (LEACH) algorithm (a cluster routing protocol that is mainly used in WSN) to improve the network's data security transmission, energy efficiency and load balancing characteristics. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) is designed to leverage the smart industries' performance with information and communication technologies. The functions of the industrial devices and the process control are managed using IoT components for automated production. However, external IoT support increases the risk of vulnerabilities that intervene in the industries' operational process. In this article, Blockchain-aided Secure Process Control is introduced. This proposal focuses on mitigating the intervening external vulnerabilities in the sequential task processing intervals. The resilient and vulnerable process controls are differentiated based on their allocation-to-outcome factor. This factor serves as the key to classify the nature of the process control. For this purpose, classification tree-based learning is employed. The outcome of the classification tree is useful in blockchain interactions with the process controller. The process controller is administered using the blockchain system based on the classification output. The harmonized process of blockchain and outcome factor-based classification improves the job delivery rate under controlled time. The metrics latency, adversary detection, false rate, and job failures are used for analyzing the proposed method's performance. The proposed method achieves an 11.9% high allocation rate, 14.14% less adversary impact, and 16.45% less processing time for different tasks. [ABSTRACT FROM AUTHOR]

Both technical and commercial businesses have become increasingly interested in blockchain technology. The Internet of Things (IoT) was established out of the interconnectedness of numerous new technologies, such as wireless technology, the Internet, embedded automation systems, and micro-electromechanical devices. Manufacturing settings and activities have been successfully altered into the fourth industrial revolution, known as Industry 4.0, by applying cutting-edge technologies such as cloud computing (CC), cloud service provider (CSP), Information communication technologies (ICT), and Enterprise Model, as well as other technological advancements. Data management is characterized as acquiring data to make better business decisions; data about a corporation is processed, secured, and stored. In the early notion, there were connected contrivances and Machine-to-Machine (M2M) interactions, and transaction data is recorded on the Blockchain. Security is a challenging subject that must be carefully considered throughout a CSP's design and development phases. By focusing on such issues while taking into account the traditional characteristics of IoT/ IIoT-predicated environments, we proposed a Secure and Distributed Framework for Resource Management (SDFRM) in Industry 4.0 environments within a distributed and collaborative Industry 4.0 system, the dynamic and trust-based Distributed Management Framework (DMF) of shared resource access, in this research article. Furthermore, to offer strong privacy assurances for Access Control (AC)-equivalent actions, a privacy-preserving approach is devised and implemented into the Distributed Management Framework (DMF). Based on blockchain technology and peer-to-peer networks, the recommended DMF provides for dynamic access control and system governance without relying on third-party vulnerabilities. A privacy-preserving technique is presented and implemented into the DMF to give adequate privacy safeguards for AC-related processes. With an average throughput of 98.15 percent, our proposal exceeds the Multichannel Blockchain regarding successful storage transactions. [ABSTRACT FROM AUTHOR]

Social media platforms have enabled users to share their thoughts, ideas, and opinions on different subject matters and meanwhile generate lots of information which can be adopted to understand people's emotion towards certain products. This information can be effectively applied for Aspect Category Detection (ACD). Similarly, people's emotions and recommendation-based Artificial Intelligence (AI)-powered systems are in trend to assist vendors and other customers to improve their standards. These systems have applications in all sorts of business available on multiple platforms. However, the current conventional approaches fail in providing promising results. Thus, in this paper, we propose novel convolutional attention-based bidirectional modified LSTM by combining the techniques of the next word, next sequence, and pattern prediction with ACD. The proposed approach extracts significant features from public reviews to detect entity and attribute pair, which are treated as a sequence or pattern from a given opinion. Next, we trained our word vectors with the proposed model to strengthen the ACD process. Empirically, we compare the approach with the state-of-the-art ACD models that use SemEval-2015, SemEval-2016, and SentiHood datasets. Results show that the proposed approach effectively achieves 78.96% F1-Score on SemEval-2015, 79.10% F1-Score on SemEval-2016, and 79.03% F1-Score on SentiHood which is higher than the existing approaches. [ABSTRACT FROM AUTHOR]

Smart and innovative education is an integral part of the development of sustainable entrepreneurship practices. Due to this, UNCED included it on its agenda. The primary goal of smart education is to teach young people to be responsible members of society in the years to come. The youth of this generation should have the opportunity to play an active role in shaping our future. For the sake of future generations, they should be taught to accept personal responsibility for their own well-being and the well-being of future generations. However, with traditional teaching techniques, this goal is not achieved; therefore, there is a requirement for the integration of cutting-edge technologies such as IoT, cloud computing, AI, and machine learning with the education process for the development of sustainable entrepreneurship practices in order to achieve a better future. In this context, we analyze the current smart education techniques proposed by different researchers that lead to the development of sustainable entrepreneurship practices. In this investigation, we also highlight the various limitations and challenges of the current smart education system. [ABSTRACT FROM AUTHOR]

While the combination of virtual reality (VR), industrial internet of things (IIoT) and digital twin (DT) can promote the integration of the physical world (real) and the digital world (virtual), one of the most important challenges that companies face is the choice of architecture. This study intends to utilize VR, IIoT, DT advantages and to reduce some adverse effects the constituent processes produce when they are individually applied. This bridges the existing gap between the physical and digital machines to enable near real-time monitoring control of the manufacturing process. In the realization of this paradigm shift, we use of IIoT infrastructures by adopting a bi-directional communication protocol for conveyance of data and information between the physical and virtual models in hardware in the loop or software in loop configuration. This study takes into account user case industrial application for the formation of silica scale and methods of reducing its production. In systems operating above pH 8.5, magnesium silicate is very likely to form due to the presence of magnesium hydroxide Mg(OH)2 and silicate (SiO4)4− ions. In this research work, the developed platform (VR, IIoT and DT) is used to remotely monitor and control the process that prevents silica scale formation by maintaining an acidic solution (pH < 6.7). A supervisory control is achieved using VR whereby instructions and commands sent to the physical station for execution. The data collected is stored in a data lake and is used to find the PID controller trends for pH 4 dosing and gain actionable insights. Analysis of the data is done by utilization of visualization schemes, diagrams and infographics. Results show the achievement of near real-time control (correlation coefficient at 99.92%) of a cyber-physical machine using VR by the adoption of bi-directional communication between the physical and virtual models in an immersive environment. [ABSTRACT FROM AUTHOR]

Over the past decade, in the industrial sector we have witnessed the rise of a revolutionary movement, known as Industry 4.0, that promotes the digital transformation as the key to increase the competitiveness of manufacturing factories. Among the many technologies recognized as “drivers” of such revolutionary transition, microservices stand out as a software development paradigm capable of bringing several benefits to the manufacturing process. Whilst the literature offers many examples of initiatives exploiting microservices in digitally-advanced sectors (e.g., finance, telecommunication, retailing), its potential in the industrial manufacturing is yet to be fully unleashed. We conducted an extensive literature survey in the twofold aim of bringing to the reader’s attention the many benefits that the microservices paradigm may offer in industrial manufacturing settings, and drawing a picture of how light virtualization techniques are actually being exploited to achieve Industry 4.0 digitization goals. In this paper, we propose a structured analysis of the collected literature proposals which combines the benefits sought by authors when approaching to the microservices techniques and the specific scope of application of proposals. We conclude the paper highlighting the research aspects that have not been sufficiently explored in the literature and that would deserve further attention in the near future. [ABSTRACT FROM AUTHOR]

The manufacturing industry is one of the most suitable sectors that can significantly benefit from implementing the Industrial Internet of Things (IIoT) concepts and technologies. The extensive automation in the manufacturing industries leads to many heterogeneous applications based on IIoT, requiring effective integration of many heterogeneous systems and seamless operations across the machines. The problem of integration and seamless operation introduces IIoT as a separate field of study in smart manufacturing that brings many challenges, including security, traceability, trust, and reliability. In IIoT, many devices will be connected using wireless and internet infrastructure to communicate with each other. In such a scenario, the IIoT devices' trustworthiness becomes an essential factor in avoiding injection by malicious machines. Therefore, an intelligent computational model is needed to cluster and classify the IIoT devices' trustworthiness accurately. In this paper, we present a trust model based on the neutrosophic weighted product method (WPM) used by IIoT applications to assess IIoT devices' trust score. The developed model assesses devices' trustworthiness based on the spatial knowledge, temporal experience, and behavioural pattern retrieved from the IIoT devices. Furthermore, the model proposes neutrosophic K-NN clustering and neutrosophic support vector machines (SVM) for classifying the extracted characteristics. The proposed neutrosophic SVM method is able to correctly identify the trust boundaries and produces the final trust score. The simulation results show that the proposed trust model detects the misbehaviour of IIoT devices more accurately than other aggregation methods. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) brings revolutionary technical supports to modern industries. However, today's IIoT still faces the challenges of modeling varying time-series in common data isolation while considering data security. To accurately characterize industrial dynamics, we propose a possible solution based on federated sequence learning (FSL) with cyber attack detection capabilities. Under a federated framework, FSL constructs a collaborative global model without violating local data integrity. Taking advantages of the locally sequential modeling, FSL captures the intrinsic industrial time-series responses. Furthermore, data heterogeneity among distributed clients is also considered, which is important to maintenance a robust but sensitive attack detection. Experiments on classic distributed datasets demonstrate that FSL is capable to accurately model data heterogeneity caused by data isolation and dynamics of time-series. Real IIoT attack detection experiments using a distributed testbed show that our FSL provides better detection performances for industrial time-series sensory data compared to existing methods. Therefore, the proposed attack detection approach FSL is promising in real IIoT scenarios in terms of feasibility, robustness and accuracy. [ABSTRACT FROM AUTHOR]

This study explores the application of machine learning algorithms for supporting complex product manufacturing quality through a focus on quality assurance and control. We aim to take advantage of ML technics to solve one of the complex manufacturing problems of the tempered glass manufacturing industry as a first attempt to automate product quality prediction and optimization in this industrial field as an alternative to destructive testing methodologies. The choice of this application field was motivated by the lack of a robust engineering technique to assess the production quality in real time; this arises the need of using advanced smart manufacturing solution as AI to save the extremely high cost of destructive tests. As methodology, this paper investigates the performance of machine learning techniques including Ridge Regression, Linear Regression, Light Gradient Boosting Machine, and Lasso Regression, for predicting the product thermal treatment quality within the selected type of industry. In the first part, we applied the selected machine learning models to a dataset collected manually and made up of the more relevant process parameters of the heating and the quenching process. Evaluating the results of the applied models, based on several performance indicators such as mean absolute error, mean squared error, and r-squared, declared that Ridge Regression was the most accurate model with a mean error of 14.33 which is significantly acceptable in a business point of view and not reachable by any human level experience of prediction. The second part consists of developing a digitalized device connected to the manufacturing process to provide predictions in real time. This device operates as an error-proofing system that sends a reverse signal to the machine in case the prediction shows a non-compliant quality of the current processed product. This study can be expanded to predict the optimal process parameters to use when the predicted values do not meet the desired quality and can advantageously replace the trial-and-error approach that is generally adopted for defining those parameters. The contribution of our work relies on the introduction of a clear methodology (from idea to industrialization) for the design and deployment of an industrial-grade predictive solution within a new field which is the glass transformation. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) is the network architecture used to simplify data management and facilitate communication network innovation. Security has been a more important task in the Industrial Internet of Things (IIoT) using SDN communication networks due to the network complexities. Moreover, communication still faces challenges in ensuring security and privacy to protect from security attacks and offers reliable financial services in IIoT. Davis Mayer Streebog Cryptographic Blockchain-based Secured Data Transaction (DMSCB-SDT) framework is introduced in SDN to improve secure data communication. The DMSCB-SDT framework aims to perform data transactions via the SDN using a blockchain-based network in the financial service industry. The DMSCB-SDT framework is designed with the integration of blockchain and the Streebog Cryptographic hash function. At first, the input data are separated into different blocks. Streebog cryptographic hash function uses the Davis Mayer compression function for producing the hash for each data block to perform the encryption process. After that, the original data is altered into ciphertext in the form of the hash value. Next, the data is transmitted to the receiver for performing decryption for obtaining the original data with a similar key. The unauthorized entity is avoided to protect the original data. As a result, the DMSCB-SDT framework ensures security and confidentiality to preserve the data and provide better communication. The statistical analysis and results show that the DMSCB-SDT framework achieves better security in terms of a higher confidentiality rate, dynamically minimizing the processing time of secure data transactions, and minimizing the computation overhead than the other state-of-the-art works. [ABSTRACT FROM AUTHOR]

The present spreading out in healthcare initiated with the appreciation of e-healthcare by interconnecting the medical sensors. IoT played a dynamic part in the healthcare industry, which founded the realization of the Industrial Internet of Things (IIoT). The IIoT is becoming a recognized measure of healthcare universally and is shifting the aptitude to treat patients. IIoT-enabled smart health can easily authenticate the patients and expedites efficient tracking. It supports critical information sharing for effective diagnosis and treatments. IIoT-enabled health systems are the combination of sensors, cameras, devices, and objects that produce a giant quantity of data called big data. The spreading out of Big Data founded the realization of Machine Learning (ML) to predict diseases for better treatment. The major goal of IIoT-enabled smart health systems is to investigate big data and provide valued insights for improved QoS. This research suggests an IIoT-enabled system for healthcare to process the Big Data generated by IIoT sensors using machine learning. The proposed scheme is the optimized parallel and distributed framework for real-time efficient processing. The proposed framework is comprised of three modules that are big data preparation, optimized model building, and big data computation. The data preparation is performed for accurate prediction and to get faster real-time big data processing. The current proposals lack effective prediction and resourceful parallel data processing. The proposed framework is simulated through an experiment with the parallel and distributed framework. The efficiency of the proposed scheme has been verified using authentic datasets with experimentations and simulations. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) has been universally applied in all aspects of industrial production, contributing to the development of communication technology. As a result, its security is now strongly and critically related to production process safety and even national security. However, since data communication in IIoT is dependent upon the public channel, messages are vulnerable to eavesdropping and tampering, bringing significant risks to industrial production systems. A secure and effective authentication scheme is vital to ensure the security of IIoT. In addition, the resources required for the sensors used in IIoT are often restricted due to deployment in complex production environment. Since energy consumption is related to the distance over which the communication occurs, the widely-used single gateway model is not effective for energy saving of sensors. Therefore, we propose a multi-gateway authentication scheme and adopt dynamic authentication credentials to provide better security strategies. In order to ensure that the update takes effect on all distributed gateways, the consensus algorithm of blockchain is used to synchronize information. The consensus algorithm reduces overhead through group policy optimization. Finally, AVISPA simulation, Burrows Abadi Needham logic(BAN-Logic), and informal security analyses are adopted to confirm the security. A performance analysis during an NS-3 simulation proves the effectiveness and practicality of the scheme. [ABSTRACT FROM AUTHOR]

Cyber-attack detection within Industrial Internet of Things (IIoT) environments presents unique challenges due to the complex, resource-constrained, and real-time nature of these networks. Traditional detection techniques often struggle to adapt to the dynamic environment of IIoT. For instance, many existing methods rely on signature-based detection, which fails to identify evolving threats. Other approaches, such as anomaly-based detection, can generate a high rate of False Positives, leading to inefficiencies in threat management. To address these challenges, we propose a novel detection and classification model specifically tailored for IIoT environments. The proposed model integrates Genetic Algorithms (GA) and Deep Learning (DL) to enhance cyber-attack detection within IIoT environments. The GA component optimises feature selection from raw network data, ensuring the extraction of meaningful and relevant features. Leveraging these selected features, the DL component constructs a robust model capable of accurately detecting and classifying various cyber-attack patterns across IIoT devices. Through experimentation on real-world IIoT network traffic (UNSW-NB 15 dataset), the proposed approach demonstrates its efficacy in improving attack detection accuracy and adaptability. The integration of GA and DL offers a synergistic solution that addresses the complexities of IIoT cybersecurity, contributing to a more secure and resilient IIoT ecosystem. The integrated GA–DL classification model developed in this work achieved 98% precision, 96% accuracy, 94% recall, and 12% losses with only less than 50% of the features of the UNSW-NB 15 dataset. The reduction in features required for the identification and classification of cyber-attacks reduces the processing time by 50%. [ABSTRACT FROM AUTHOR]

The research and development (R&D) of the scale-up process of third-generation photovoltaics (PVs) can benefit from the emerging trends and technologies related to the Industrial Internet of Things. However, to migrate the small-scale laboratory PVs products to a larger version of the industrial scale, a processing platform is needed to design, fabricate, and test the production line. In this paper, after a brief introduction of the production process of thin-film PVs, specifically dye-sensitized solar cells, the Industrial Internet Reference Architecture (IIRA) has been applied to the R&D scenario for the production of thin-film PVs, in order to synchronize and manage the large amount of data generated by the real, virtual or hybrid production devices and processes. The results of this study suggest that the future implementation of IIRA is a reliable option in a learning factory environment for multidisciplinary collaboration, research training in novel technologies and methods in the Tijuana Institute of Technology. This contribution is in order to optimize and scale-up the production process of a new generation of solar cells. [ABSTRACT FROM AUTHOR]

The IEEE 802.15.4 Time Slotted Channel Hopping (TSCH) communication mode is a key standard in the Industrial Internet of Things (IIoT). To schedule communications, TSCH uses deterministic transmissions to deal with latency requirements and channel hopping to cope with interference in IIoT environments. Nonetheless, this latter might not be sufficient to ensure reliable delivery of critical data since industrial networks are prone to severe external interference, which impacts the quality of wireless channels. In this paper, we propose an effective local Channel Selection approach for Reliable communication Scheduling in TSCH networks, dubbed CSRS. CSRS leans on effective assessment metrics to estimate the quality of available communication channels and stateless local exchange of bad-channels blacklists. CSRS is schedule-independent; hence it can be combined with any TSCH schedule, including the standardized Minimal Scheduling Function (MSF), to reduce the negative impact of bad channels. CSRS integration with MSF is implemented in Contiki and validated through extensive realistic trace-based simulations and public testbed experiments. Obtained results demonstrate the efficiency of our proposal in terms of reliability, latency, and energy consumption when compared with state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

Biosensors-based devices are transforming medical diagnosis of diseases and monitoring of patient signals. The development of smart and automated molecular diagnostic tools equipped with biomedical big data analysis, cloud computing and medical artificial intelligence can be an ideal approach for the detection and monitoring of diseases, precise therapy, and storage of data over the cloud for supportive decisions. This review focused on the use of machine learning approaches for the development of futuristic CRISPR-biosensors based on microchips and the use of Internet of Things for wireless transmission of signals over the cloud for support decision making. The present review also discussed the discovery of CRISPR, its usage as a gene editing tool, and the CRISPR-based biosensors with high sensitivity of Attomolar (10−18M), Femtomolar (10−15M) and Picomolar (10−12M) in comparison to conventional biosensors with sensitivity of nanomolar 10−9M and micromolar 10−3M. Additionally, the review also outlines limitations and open research issues in the current state of CRISPR-based biosensing applications. [ABSTRACT FROM AUTHOR]

The integration of smart devices in the manufacturing section has brought along disruptions in the production processes. The existing literature in the field of IIoT and Industry 4.0 have focused much on the technical aspects of the network design and development, however, there is a substantial gap in examining the challenges of implementation of IIoT techniques, especially in context to India. This paper presents an exhaustive analysis and categorization of the challenges faced by the manufacturing sector in the implementation of the Industrial Internet of Things (IIoT). Challenges were categorized into technical and organizational challenges with the help of experts. The paper aims to attain a hierarchical structure, which will further be helpful to policymakers to identify the most critical challenge allowing them to make a well-versed decision. The results of this study are expected to highlight the key challenge wherein the industry and researchers can focus their strategic efforts. This will facilitate the address of implicit issues while implementing IIoT Techniques in the manufacturing industry. [ABSTRACT FROM AUTHOR]

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With the anticipated increment in connected devices for the Internet of Things (IoT) applications, ensuring low power consumption with long-range data transmission is needed. An low power wide area network (LPWAN) technology is well suited to fulfill this requirement. LoRa is known to customize its physical layer settings to achieve desired transmission range, coverage, and energy consumption. For large-scale realistic deployments, LoRa based Industrial IoT (IIoT) solution needs to offer configurable granularity and compatibility with legacy systems and processes. However, the requirement of a seam-less handoff across end-to-end (E2E) systems is usually based on dense short-range wireless systems. As IIoT applications typically experience varying environmental conditions, we consider four scenarios, namely line-of-sight (LOS), non-LOS (NLOS), obstruction, and conventional environment, to present the variation in the network efficiency (NE). This paper aims to implement the fuzzy inference system algorithm as a controller in an IIoT-based based application. The Mamdani fuzzy inference system algorithm is implemented on the gateway node that receives sensor values and computes network parameters to predict NE. The result of the FIS is then analyzed for tuning hardware settings on LoRa nodes. These settings are then propagated to the actuator nodes to set optimal tuning of the spreading factor, bandwidth, and coding rate for maximizing lifetime and optimizing data flow. We also study and ascertain that the parameters under different network conditions need to be observed to interpret the results predicted from the FIS model. [ABSTRACT FROM AUTHOR]

Critical Industries such as Manufacturing, Power, and Intelligent Transportation are increasingly using IIoT systems, making them more susceptible to cyberattacks. To counter these cyberattacks, policymakers have made strong guidelines, and various security provisions like secure authentication and encryption mechanisms as effective countermeasures for these systems. The exponential rise in cyberattacks has proven that all these measures are not sufficient to protect IIoT systems and have certain limitations. Considering the progress in Artificial Intelligence, it is widely acknowledged that Machine Learning (ML) based Intrusion Detection Systems (IDS) hold significant potential for identifying these cyberattacks. Numerous ML-based IDS have been proposed, which are capable of detecting known attacks but do not perform well in recognizing the “Unknown-Attacks” or Zero-Day Attacks (ZDAs) and Advanced Persistent Threats (APTs); hence, one of the most prominent concerns in the cyber industry is how threat intelligence could be used to protect against these exploits. The proposed “Hybrid Multi-Stage Intrusion Detection System” (HMS-IDS) is driven by supervised and unsupervised approaches to identify both known and unknown cyber-attacks in IIoT environments. By carefully evaluating the esteemed CIC-ToN-IoT dataset, the proposed IDS model attains staggering levels of accuracy, reaching an impressive 99.49% in detecting known attacks and an exceptional 98.936% in identifying unknown attacks. These compelling findings unequivocally substantiate the system’s efficacy in real-time detection of malicious cyber incursions targeting IIoT devices, thereby underscoring its tremendous potential for wide-scale implementation and practical deployment. To validate the proposed model’s reliability, the performance evaluation is also performed on state-of-the-art datasets, namely KDD-99 Cup, NSL-KDD, CICIDS 2017. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things is crucial for enterprise and country to drive the strategic upgrade and raise the level of national intelligent manufacturing. When pondering the IIoT industry evaluation, the corresponding dominating issues involve numerous indeterminacies. Spherical fuzzy set, portrayed by memberships of positive, neutral and negative, is a more efficient methods of seizing indeterminacy. In this article, firstly, the fire-new spherical fuzzy score function is explored for solving some suspensive comparison issues. Moreover, the objective weight and combined weight are determined by Renyi entropy method and non-linear weighted comprehensive method, respectively. Later, the multi-criteria decision making method based on combined compromise solutionis developed under spherical fuzzy environment. Finally, the corresponding method is effectively validated by the issue of IIoT industry evaluation. The main characteristics of the presented algorithm are: (1) without counterintuitive phenomena; (2) no division or antilogarithm by zero problem; (3) no square root by negative number issue; (4) no violation of the original definition issue. [ABSTRACT FROM AUTHOR]

IEEE Std 802.15.4-2015 Time Slotted Channel Hopping (TSCH) is the de facto Medium Access Control (MAC) mechanism for industrial applications. It renders communications more resilient to interference by spreading them over the time (time-slotted) and the frequency (channel-hopping) domains. The 6TiSCH architecture bases itself on this new MAC layer to enable high reliability communication in Wireless Sensor Networks (WSNs). In particular, it manages the construction of a distributed communication schedule that continuously adapts to changes in the network. In this paper, we first provide a thorough description of the 6TiSCH architecture, the 6TiSCH Operation Sublayer (6top), and the Minimal Scheduling Function (MSF). We then study its behavior and reactivity from low to high traffic rates by employing the Python-based 6TiSCH simulator. Our performance evaluation results demonstrate that the convergence pattern of MSF is the root cause of the majority of packet losses observed in the network. We also show that MSF is prone to over-provisioning of the network resources, especially in the case of varying traffic load. We propose a mathematical model to predict the convergence pattern of MSF. Finally we investigate the impact of varying parameters on the behavior of the scheduling function. [ABSTRACT FROM AUTHOR]

Peer-to-Peer (P2P) networks are prominent in the Internet-of-things-assisted industrial environments for distributed computing and smart control systems. The problem arises with the independence and peer systems security due to anonymous access and security measures. In this paper, an innovative control-driven autonomous authentication scheme is proposed for improving the access security of P2P industrial systems. The proposed scheme provides authentication based on P2P system control requirements within its access time. The P2P control systems and their functionalities are provided with classified security measures for administering autonomous security. The advantage of offering autonomous protection is to prevent the sequence of security breaches and control sabotage. In this process, the control system requirements and authentications are paired by identifying the machines' operating time and access time. For identification and grouping-based classification, support vector machines are used. It learns the sabotage and control requirements based on access and control time for providing a rupture-less industrial process. It helps to leverage the detection of autonomous adversaries in P2P industrial control systems. Besides, a less complex and latent-free security measure is achievable using the proposed scheme. [ABSTRACT FROM AUTHOR]

Across so many industries, non-destructive evaluation has proven its worth time and again through quality and safety assurance of valuable assets. Yet, over time, it became underappreciated in business decisions. In most cases, the data gathered by NDT is used for quality assurance assessments resulting in binary decisions. And we seem to miss out on value of the information content of NDE which goes way deeper and can help other stakeholders: such as engineering, management, inspectors, service providers, and even regulators. Some of those groups might not even be aware of the benefits of NDE data and its digitalization. Unfortunately, the NDE industry typically makes the data access unnecessarily difficult by proprietary interfaces and data formats. Both those challenges need to be addressed now by the NDE industry. The confluence of NDE and Industry 4.0, dubbed as NDE 4.0, provides a unique opportunity for the NDE/NDT Industry to not only readjust the value perception but to gain new customer groups through a broad set of value creation activities across the ecosystem. The integration of NDE into the Cyber-Physical Loop (including IIoT and Digital Twin) is the chance for the NDE industry to now shift the perception from a cost center to a value center. This paper provides an overview of the NDE ecosystem, key value streams, cyber-physical loops that create value, and a number of use cases for various stakeholders in the ecosystem. [ABSTRACT FROM AUTHOR]

Like with the previous revolutions the goal of the fourth revolution is to make manufacturing, design, logistics, maintenance, and other related fields faster, more efficient, and more customer centric. This holds for classical industries, for civil engineering, and for NDE and goes along with new business opportunities and models. Core components to enable those fourth revolutions are semantic interoperability, converting data into information, the Industrial Internet of Things (IIoT) offering the possibility for every device, asset, or thing to communicate with each other using standard open interfaces, and the digital twin converting all the available information into knowledge and closing the cyber-physical loop. For NDE this concept can be used #1 to design, improve, and tailor the inspection system hard- and software and #2 to choose and adapt to best inspection solution for the customer or to enhance the inspection performance. Enabling better quality, speed, and cost at the same time. On a broader view, the integration of NDE into IIoT and Digital Twin is the chance for the NDE industry for the overdue change from a cost center to a value center. In most cases, data gathered by NDE is used for a quality assurance assessment resulting in a binary decision. But the information content of NDE goes way deeper and is of major interest for additional groups: engineering and management. Some of those groups might currently not be aware of the benefits of NDE data and the NDE industry makes the access unnecessarily difficult by proprietary interfaces and data formats. Both those challenges need to be taken on now by the NDE industry. The big IT players are not waiting and, if not available on the market, they will develop and offer additional data sources including ultrasonics, X-ray or eddy current. [ABSTRACT FROM AUTHOR]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Smart grid is one of the major prospective candidates in the Industrial Internet of Things family that ensures smooth and efficient power distribution, restoration in times of emergency, and usage control for the consumers. Electric power generators contribute at the core of smart grid along with the transmission lines and transformers. Extensive research works are conducted to optimize different parameters such as efficient energy usage, automated demand response, and emergency grid failure recovery. However, the component status analysis of the electric generators within a smart grid is still in the nascent stage. In this paper, we propose a novel routing protocol for supervised device-data transfer from smart grid generators to the command and control center using wireless ad hoc and sensor networks. Our protocol assumes various sensor devices (temperature sensors, oil level sensor, turbine status sensors, etc.) to be employed on each generator due to their mechanical sophistication. Additionally, we introduce the ambient energy harvesting for the sensors energy replenishment to accommodate tolerable node outage. Our simulation results demonstrate promising outcome with respect to different key parameters such as message flow, energy consumption, outage frequency, remaining energy, and harvested energy. [ABSTRACT FROM AUTHOR]

It is proposed to use modern artificial neural networks to identify cyber threats in networks of the Industrial Internet of Things. The modeling of an industrial system under the influence of cyberattacks was carried out. As a result of the experiments, the optimal configuration parameters of the recurrent LSTM network with a confirmed number of layers and states have been determined. [ABSTRACT FROM AUTHOR]

This article describes the transformation of an enterprise en route from the conventional construction according to the Industrial Control Systems (ICS) architecture to the architecture of the Industrial Internet of Things (IIoT). The main cybersecurity threats that must be taken into account in the digital transformation are considered. The distinct features of each of the phases are described. [ABSTRACT FROM AUTHOR]

The transition from the information economy to the digital one presents new challenges for society associated with the development of disruptive technologies, a network of cyber-physical systems, artificial intelligence and big data. When creating digital platforms, a number of difficulties arise: the large size of the digital infrastructure and its heterogeneity, poorly established information interaction between segments, the lack of a unified approach to ensuring cybersecurity and a high dependence on the qualifications of personnel and equipment reliability. The introduction of the digital economy leads to an increase in the risk of cyber threats associated with access control problems between information flow regulation and control systems. To solve the problems of detecting cyber threats, it is proposed to use generative adversarial neural networks. Algorithms for learning and testing a neural network were presented. The results of the experiments have demonstrated that the proposed solution is highly accurate in detecting cyberattacks. [ABSTRACT FROM AUTHOR]

Connections of cyber-physical system (CPS) components are gradually increasing owing to the introduction of the Industrial Internet of Things (IIoT). IIoT vulnerability analysis has become a major issue because complex skillful cyber-attacks on CPS systems exploit their zero-day vulnerabilities. However, current white box techniques for vulnerability analysis are difficult to use in real heterogeneous environments, where devices supplied by various manufacturers and diverse firmware versions are used. Therefore, we herein propose a novel protocol fuzzing test technique that can be applied in a heterogeneous environment. As seed configuration can significantly influence the test result in a black box test, we update the seed pool using test cases that travel different program paths compared to the seed. The input, output, and Delta times are used to determine if a new program area has been searched in the black box environment. We experimentally verified the effectiveness of the proposed. [ABSTRACT FROM AUTHOR]

The advent of Industrial Internet of Things (IIoT) has determined the proliferation of smart devices connected to the Internet and injected a vast amount of data into it, which may undergo many computational stages at several clusters. On the one hand, the benefits brought by these technologies are well known; however, in the envisaged scenario, the exposure of data, services and infrastructures to malicious attacks has definitely grown. Even a single breach on any of the links of the data–service–infrastructure chain may seriously compromise the security of the end-user application. Therefore, the logical and smart clustering while satisfying security and reliability is a key issue for IIoT networks. A novel clustering method proposed based on power demand assures security of data information in IIoT-based applications. First, security capacity of the system is calculated from mutual information of primary channel and eavesdropping channel. Then, under the maximum transmit power constraint, an optimal transmit power is found based on deep learning technique, which maximizes security capacity of the system. Finally, the network is clustered according to the calculated power demand. Experimental results accredit the proposed method has higher security and reliability, as well as lower network time overhead and power consumption. [ABSTRACT FROM AUTHOR]

Manufacturers and industrialists have a significant opportunity at hand in automation for the complex processes involved in manufacturing rather than labor-intensive based system analysis and control. Especially industrial IoT (IIoT) technology provides far more intricate details to the industrial automation for prompt decisions automatically through a web server. Hence in this present work, online data monitoring of important attributes associated with the mining industry during the extraction of zinc and lead are analyzed using IIoT. The real-time analysis of the extraction efficiency rate of zinc and lead concerning temperature, pH and a particle size parameter in mining sectors is carried out by storing data in the cloud. It is accomplished by using an integrated IoT module holding Revolution-Pi IIoT (IIoT) gateway with AC500 PLC to afford enhanced data communication from the mining field to the cloud server to increase the performance of the processes. Based on the retrieval of historical data from the cloud, a multivariate regression model for extraction efficiency of zinc and lead is formulated by using pH, temperature and particle size as influencing parameters to estimate the predictions. [ABSTRACT FROM AUTHOR]

Nowadays, we are already involved in the Fourth Industrial Revolution, known as Industry 4.0. Inside this paradigm, IoT is one of fundamental technologies that allows the integration of physical and virtual systems. The important role of the IoT is based on its potential to offer different solutions for the operational of existing industrial system, in order to transform them in digital environments. Following this idea, a proof of concept is presented, applying IIoT (Industrial Internet of Things) in a shipyard. This technology is used as a key enabler to achieved a connected and advanced manufacturing with the aim of improve efficiency in a specific workshop inside the shipyard. This first approach to the transformation of conventional shipyard in a smart factory comprises two parts: the communication between the information systems and the connection with physical devices. The goal is to solve the challenges that arise with the implementation of IIoT and to provide a useful decision tool to operators and managers of the workshop. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoTs) is the fast growing network of interconnected things that collect and exchange data using embedded sensors planted everywhere. It is an interconnection of several things through a diverse communication system capable of monitoring, collecting, exchanging, analysing, and delivering valuable and challenging amount of information. Given their ability to be operated autonomously, their high mobility, and their communication and processing power, Unmanned Aerial Vehicles (UAVs) are expected to be involved in numerous IIoT-related applications, where multimedia and video streaming plays a key role. Our main interest is the multimedia routing in IIoT and its facilities during and/or after operational hours. For recovering, constructing and selecting k-disjoint paths, capable of putting up with failure of the parameters but satisfying the quality of service, we introduce an industry-oriented Canonical Particle Swarm (CPS) optimization data delivery framework. During communication with the UAV, multi-swarm strategy is used to determine the optimal direction while performing a multipath routing. Authenticity of the proposed approach has been tested and results show that, compared to the ordinary Canonical Particle Multi-path Swarm (CPMS) optimization and Fully Multi-path Particle Swarm (FMPS) optimization, the proposed method is better. [ABSTRACT FROM AUTHOR]

The purpose of any business is to delight the customer as a primary stakeholder, thereby enhancing the growth and profitability. Understanding customer needs and building them on end to end value chain not only will result in serving customers on time, but also improve the effectiveness of the processes to retain competitiveness. Value stream mapping remains a popular visualization tool in the hands of the Lean Manager who seeks to produce more with less. However, value stream mapping (VSM) tends to be static and skill dependent. With the advent of Industrial Internet of Things (IIoT), there could be a paradigm shift on how VSM could be leveraged for maximizing results. IIoT makes it possible to convert the VSM as a dynamic one, enhancing with several additional parameters measured simultaneously in real time, making the relationship between cause and effect more visible. Literally, with the addition of IIoT, we could digitally re-live the moments from the past to identify the connections between the cause and effect more specifically with better accuracy. In this paper, we attempt to clarify how IIoT could enhance the VSM as a strategic differentiator for making better decisions. In a sensor-based efficiency monitoring system, the VSM becomes dynamic; thereby all the parameters including the bottleneck operations could be continuously monitored and acted upon to attain the future state eliminating the dependency on the expertise of the people. [ABSTRACT FROM AUTHOR]

The demand of customer-specific products leads to a fundamental change to manufacturing facilities. To adapt the facilities to new product types, frequently occurring functionality changes in industrial automation systems are expected. Functionality changes are primarily implemented by software changes. These software changes within the operation phase can be implemented, for instance, by over-the-air software updates or ad hoc integration of new components. The effects of these changes are often difficult to estimate, especially in distributed automation systems. This mainly poses a challenge on production line operators, who are required to validate their automation systems after functionality changes have been executed. The goal of this contribution is to assist production line operators in the validation process of their automation systems after software changes. Formal verification methods can support the operators, due to its fully automated execution. However, the creation process of the behavior models needed for the formal verification is complex and error-prone. This is why formal verification is usually not used. Hence, a model-based technique is presented to automate this creation process. By means of this, the subsystem affected by the software change is automatically identified and subsequently a suitable input to a model-based verification tool is generated. The concept is based on the generation of a system model by composing the Petri net models of components within the automation system. In order to identify affected components, an impact analysis is performed, starting from the component in which a modification occurred. Followingly, a tailored subsystem is composed using the component models necessary for verification. This subsystem is applied to verify the system requirements for the affected components. To evaluate the applicability of the concept in the field of industrial automation, a distributed automation system was implemented. A service-oriented, OPC-UA-based, control network is thereby used to implement a technical process. Furthermore, a configuration interface enables change of the components at runtime. This emulates over-the-air updates and ad hoc networking. The concept is implemented with the demonstrator "TestIAS." This test device detects software changes within the automation system and verifies them automatically according to the model-based approach presented. An empirical evaluation was performed with ten different reconfiguration scenarios showing functional changes. In addition, based on the time measurements of the time saving due to the impact analysis, the efficiency enhancement is substantiated. [ABSTRACT FROM AUTHOR]

This article presents an approach to building a system for identifying cyberthreats in modern industrial systems (IIoT, VANET, WSN) by means of artificial intelligence and deep learning. The results of the tests conducted to assess the suggested approach for efficiency based on deep-learning networks are provided. [ABSTRACT FROM AUTHOR]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With Industry 4.0 and Internet of Things (IoT) era coming, remote passwords and control-flow vulnerabilities play a key role to detect attackers in Industry IoT (IIoT), who can easily complete remote session and control-flow hijacking on leverage of these types of Sensitive Information (SI). However, how to measure security degree of Sensitive Information is an open issue. To our best knowledge, no effective method can detect secret trace of SI thieves in Advanced Persistent Threat (APT), especially for backdoors and vulnerabilities in software or firmware. To deal with these problems, we propose a new design, called, IIoT-SIDefender (IIoT-SID), we measure security degree of Sensitive Information via Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), based on selected taint tracking and real-time memory modification, attack-defense and fix-distribution approaches are proposed. Until now, it is the first defined SI guard method to detect SI-leakage scenarios and reject SI-leverage attack. To verify our proposal, experimental tests are verified in a large number of IIoT applications and devices, including IP cameras, smart meters, PLCs and smart routers. Test results have demonstrated that we can capture security level for Sensitive Information as expected, detect potential leakage points in data lifetime (including unknown backdoors and vulnerabilities), describe fine-grained semantics of accidental leakage and secret leverage points, and generate relative hot fix to prevent further attack. [ABSTRACT FROM AUTHOR]

One important application area for sensor data analytics is Industry 4.0. Industrial furnaces (IFs) are sophisticated devices utilised in industrial production applications that need for unique heat treatment cycles. They are built with specialised thermodynamic materials and methods. emission of black carbon (EoBC) during IF operation as a result of the incomplete combustion of fossil fuels is one of the most important problems. This research proposes novel technique in carbon footprint analysis in environmental data from green manufacturing Industry 4.0 using machine learning with intelligent Internet of things (IIoT). Here, the environmental data from green manufacturing industry is collected and processed for analysing the presence of carbon by air monitoring by hidden fuzzy Gaussian kernel–based principle analysis. The experimental analysis is carried out for various air-monitored data in terms of training accuracy, positive predictive value, precision, robustness, energy consumption. Finally, we suggest ways for reducing carbon emissions and energy usage based on case studies that make use of our methodology. By making accounting simpler, we intend to encourage further investigation into energy-efficient algorithms and advance the long-term development of machine learning studies. [ABSTRACT FROM AUTHOR]