Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things(I...Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things(IIOT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management.Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart.We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.展开更多
Time sensitive networking(TSN)is a set of standards developed on the basis of audio video bridging(AVB).It has a promising future in the Industrial Internet of Things and vehicle-mounted multimedia,with such advantage...Time sensitive networking(TSN)is a set of standards developed on the basis of audio video bridging(AVB).It has a promising future in the Industrial Internet of Things and vehicle-mounted multimedia,with such advantages as high bandwidth,interoperability and low cost.In this paper,the TSN protocol stack is described and key technologies of network operation are summarized,including time synchronization,scheduling and flow shaping,flow management and fault tolerant mechanism.The TSN network model is then established.Its performance is illustrated to show how the frame priority works and also show the influence of IEEE802.1Qbv time-aware shaper and IEEE802.1Qbu frame preemption on network and time-sensitive data.Finally,we briefly discuss the challenges faced by TSN and the focus of future research.展开更多
With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce overheads.However,the channel between cloud servers and smar...With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce overheads.However,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be addressed.The SM2 digital signature algorithm can provide an authentication mechanism for data to solve such problems.Unfortunately,it still suffers from the problem of key exposure.In order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 algorithm.This scheme boasts strong key insulation and secure keyupdates.Our scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud environments.Finally,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.展开更多
Nowadays,a large number of intelligent devices involved in the Industrial Internet of Things(IIoT)environment are posing unprecedented cybersecurity challenges.Due to the limited budget for security protection,the IIo...Nowadays,a large number of intelligent devices involved in the Industrial Internet of Things(IIoT)environment are posing unprecedented cybersecurity challenges.Due to the limited budget for security protection,the IIoT devices are vulnerable and easily compromised to launch Distributed Denial-of-Service(DDoS)attacks,resulting in disastrous results.Unfortunately,considering the particularity of the IIoT environment,most of the defense solutions in traditional networks cannot be directly applied to IIoT with acceptable security performance.Therefore,in this work,we propose a multi-point collaborative defense mechanism against DDoS attacks for IIoT.Specifically,for the single point DDoS defense,we design an edge-centric mechanism termed EdgeDefense for the detection,identification,classification,and mitigation of DDoS attacks and the generation of defense information.For the practical multi-point scenario,we propose a collaborative defense model against DDoS attacks to securely share the defense information across the network through the blockchain.Besides,a fast defense information sharing mechanism is designed to reduce the delay of defense information sharing and provide a responsive cybersecurity guarantee.The simulation results indicate that the identification and classification performance of the two machine learning models designed for EdgeDefense are better than those of the state-of-the-art baseline models,and therefore EdgeDefense can defend against DDoS attacks effectively.The results also verify that the proposed fast sharing mechanism can reduce the propagation delay of the defense information blocks effectively,thereby improving the responsiveness of the multi-point collaborative DDoS defense.展开更多
Aiming for ultra-reliable low-latency wireless communications required in industrial internet of things(IIoT)applications,this paper studies a simple cognitive radio non-orthogonal multiple access(CR-NOMA)downlink sys...Aiming for ultra-reliable low-latency wireless communications required in industrial internet of things(IIoT)applications,this paper studies a simple cognitive radio non-orthogonal multiple access(CR-NOMA)downlink system.This system consists of two secondary users(SUs)dynamically interfered by the primary user(PU),and its performance is characterized by the outage probability of the SU communications.This outage probability is calculated under two conditions where,a)the transmission of PU starts after the channel state information(CSI)is acquired,so the base station(BS)is oblivious of the interference,and b)when the BS is aware of the PU interference,and the NOMA transmission is adapted to the more comprehensive knowledge of the signal to interference plus noise ratio(SINR).These results are verified by simulations,and their good agreement suggests our calculations can be used to reduce the complexity of future analysis.We find the outage probability is reduced when the SUs move further away from the primary transmitter or when the signal from PU is less powerful,and the BS always has better performance when it is aware of the interference.The findings thus emphasize the importance of monitoring the channel quality and realtime feedback to optimize the performance of CR-NOMA system.展开更多
The rapid growth of the Internet of Things(IoT)in the industrial sector has given rise to a new term:the Industrial Internet of Things(IIoT).The IIoT is a collection of devices,apps,and services that connect physical ...The rapid growth of the Internet of Things(IoT)in the industrial sector has given rise to a new term:the Industrial Internet of Things(IIoT).The IIoT is a collection of devices,apps,and services that connect physical and virtual worlds to create smart,cost-effective,and scalable systems.Although the IIoT has been implemented and incorporated into a wide range of industrial control systems,maintaining its security and privacy remains a significant concern.In the IIoT contexts,an intrusion detection system(IDS)can be an effective security solution for ensuring data confidentiality,integrity,and availability.In this paper,we propose an intelligent intrusion detection technique that uses principal components analysis(PCA)as a feature engineering method to choose the most significant features,minimize data dimensionality,and enhance detection performance.In the classification phase,we use clustering algorithms such as K-medoids and K-means to determine whether a given flow of IIoT traffic is normal or attack for binary classification and identify the group of cyberattacks according to its specific type for multi-class classification.To validate the effectiveness and robustness of our proposed model,we validate the detection method on a new driven IIoT dataset called X-IIoTID.The performance results showed our proposed detection model obtained a higher accuracy rate of 99.79%and reduced error rate of 0.21%when compared to existing techniques.展开更多
The integration of the Internet and the traditional manufacturing industry makes the industrial Internet of things(IIoT) as a popular research topic. However, traditional industrial networks continue to face challenge...The integration of the Internet and the traditional manufacturing industry makes the industrial Internet of things(IIoT) as a popular research topic. However, traditional industrial networks continue to face challenges of resource management and limited raw data storage and computation capacity. A novel software defined industrial network(SDIN) architecture was proposed to address the existing drawbacks in IIoT such as resource utilization, data processing and storage, and system compatibility. The architecture is developed based on the software defined network(SDN) architecture, combining hierarchical cloud and fog computing and content-aware caching technologies. Based on the SDIN architecture, two types of edge computing strategies in industrial applications are discussed. Different scenarios and service requirements are considered. The simulation results confirm that the SDIN architecture is feasible and effective in the application of edge computing offloading.展开更多
With the development of the Industrial Internet of Things(IIoT),end devices(EDs)are equipped with more functions to capture information.Therefore,a large amount of data is generated at the edge of the network and need...With the development of the Industrial Internet of Things(IIoT),end devices(EDs)are equipped with more functions to capture information.Therefore,a large amount of data is generated at the edge of the network and needs to be processed.However,no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing(MEC)devices,the data is short of security and may be changed during transmission.In view of this challenge,this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security.Blockchain technology is adopted to ensure data consistency.Meanwhile,to reduce the impact of low throughput of blockchain on task offloading performance,we design the processes of consensus and offloading as a Markov decision process(MDP)by defining states,actions,and rewards.Deep reinforcement learning(DRL)algorithm is introduced to dynamically select offloading actions.To accelerate the optimization,we design a novel reward function for the DRL algorithm according to the scale and computational complexity of the task.Experiments demonstrate that compared with methods without optimization,our mechanism performs better when it comes to the number of task offloading and throughput of blockchain.展开更多
The evolution of the Internet of Things(IoT)has empowered modern industries with the capability to implement large-scale IoT ecosystems,such as the Industrial Internet of Things(IIoT).The IIoT is vulnerable to a diver...The evolution of the Internet of Things(IoT)has empowered modern industries with the capability to implement large-scale IoT ecosystems,such as the Industrial Internet of Things(IIoT).The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational andfinancial harm to organizations.To preserve the confidentiality,integrity,and availability of IIoT networks,an anomaly-based intrusion detection system(IDS)can be used to provide secure,reliable,and efficient IIoT ecosystems.In this paper,we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks.The proposed anomaly-based IDS is divided into three phases:pre-processing,feature selection,and classification.In the pre-processing phase,data cleaning and nor-malization are performed.In the feature selection phase,the candidates’feature vectors are computed using two feature reduction techniques,minimum redun-dancy maximum relevance and neighborhood components analysis.For thefinal step,the modeling phase,the following classifiers are used to perform the classi-fication:support vector machine,decision tree,k-nearest neighbors,and linear discriminant analysis.The proposed work uses a new data-driven IIoT data set called X-IIoTID.The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%,a sensitivity rate of 99.59%,a specificity rate of 99.58%,and a low false positive rate of 0.4%.展开更多
Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.How...Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However,IIoT involves some security vulnerabilities that are more damaging than those of IoT.Accordingly,Intrusion Detection Systems(IDSs)have been developed to forestall inevitable harmful intrusions.IDSs survey the environment to identify intrusions in real time.This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security.We combine Isolation Forest(IF)with Pearson’s Correlation Coefficient(PCC)to reduce computational cost and prediction time.IF is exploited to detect and remove outliers from datasets.We apply PCC to choose the most appropriate features.PCC and IF are applied exchangeably(PCCIF and IFPCC).The Random Forest(RF)classifier is implemented to enhance IDS performances.For evaluation,we use the Bot-IoT and NF-UNSW-NB15-v2 datasets.RF-PCCIF and RF-IFPCC show noteworthy results with 99.98%and 99.99%Accuracy(ACC)and 6.18 s and 6.25 s prediction time on Bot-IoT,respectively.The two models also score 99.30%and 99.18%ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2,respectively.Results prove that our designed model has several advantages and higher performance than related models.展开更多
Industry 4.0 has become a reality by fusing the Industrial Internet of Things(IIoT)and Artificial Intelligence(AI),providing huge opportunities in the way manufacturing companies operate.However,the adoption of this p...Industry 4.0 has become a reality by fusing the Industrial Internet of Things(IIoT)and Artificial Intelligence(AI),providing huge opportunities in the way manufacturing companies operate.However,the adoption of this paradigm shift,particularly in the field of smart factories and production,is still in its infancy,suffering from various issues,such as the lack of high-quality data,data with high-class imbalance,or poor diversity leading to inaccurate AI models.However,data is severely fragmented across different silos owned by several parties for a range of reasons,such as compliance and legal concerns,preventing discovery and insight-driven IIoT innovation.Notably,valuable and even vital information often remains unutilized as the rise and adoption of AI and IoT in parallel with the concerns and challenges associated with privacy and security.This adversely influences interand intra-organization collaborative use of IIoT data.To tackle these challenges,this article leverages emerging multi-party technologies,privacy-enhancing techniques(e.g.,Federated Learning),and AI approaches to present a holistic,decentralized architecture to form a foundation and cradle for a cross-company collaboration platform and a federated data space to tackle the creeping fragmented data landscape.Moreover,to evaluate the efficiency of the proposed reference model,a collaborative predictive diagnostics and maintenance case study is mapped to an edge-enabled IIoT architecture.Experimental results show the potential advantages of using the proposed approach for multi-party applications accelerating sovereign data sharing through Findable,Accessible,Interoperable,and Reusable(FAIR)principles.展开更多
The ongoing expansion of the Industrial Internet of Things(IIoT)is enabling the possibility of effective Industry 4.0,where massive sensing devices in heterogeneous environments are connected through dedicated communi...The ongoing expansion of the Industrial Internet of Things(IIoT)is enabling the possibility of effective Industry 4.0,where massive sensing devices in heterogeneous environments are connected through dedicated communication protocols.This brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face,providing ad-hoc functions for scheduling and guaranteeing the network operations.Recently,the large development of SoftwareDefined Networking(SDN)and Artificial Intelligence(AI)technologies have made feasible the design and control of scalable and secure IIoT networks.This paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT networks.After surveying the state-of-the-art research efforts in the subject,the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network(AI-SDIN)that divides the traditional industrial networks into three functional layers.And with this aim in mind,key technologies(Blockchain-based Data Sharing,Intelligent Wireless Data Sensing,Edge Intelligence,Time-Sensitive Networks,Integrating SDN&TSN,Distributed AI)and improve applications based on AISDIN are also discussed.Further,the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.展开更多
Industrial Internet of Things(IIoT)offers efficient communication among business partners and customers.With an enlargement of IoT tools connected through the internet,the ability of web traffic gets increased.Due to ...Industrial Internet of Things(IIoT)offers efficient communication among business partners and customers.With an enlargement of IoT tools connected through the internet,the ability of web traffic gets increased.Due to the raise in the size of network traffic,discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues.A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification(MPDQDJREBC)is introduced for accurate attack detection wi th minimum time consumption in IIoT.The proposed MPDQDJREBC technique includes feature selection and categorization.First,the network traffic features are collected from the dataset.Then applying the Maximum Posterior Dichotomous Quadratic Discriminant analysis to find the significant features for accurate classification and minimize the time consumption.After the significant features selection,classification is performed using the Jaccardized Rocchio Emphasis Boost technique.Jaccardized Rocchio Emphasis Boost Classification technique combines the weak learner result into strong output.Jaccardized Rocchio classification technique is considered as the weak learners to identify the normal and attack.Thus,proposed MPDQDJREBC technique gives strong classification results through lessening the quadratic error.This assists for proposed MPDQDJREBC technique to get better the accuracy for attack detection with reduced time usage.Experimental assessment is carried out with UNSW_NB15 Dataset using different factors such as accuracy,precision,recall,F-measure and attack detection time.The observed results exhibit the MPDQDJREBC technique provides higher accuracy and lesser time consumption than the conventional techniques.展开更多
With the rapid development of data applications in the scene of Industrial Internet of Things(IIoT),how to schedule resources in IIoT environment has become an urgent problem to be solved.Due to benefit of its strong ...With the rapid development of data applications in the scene of Industrial Internet of Things(IIoT),how to schedule resources in IIoT environment has become an urgent problem to be solved.Due to benefit of its strong scalability and compatibility,Kubernetes has been applied to resource scheduling in IIoT scenarios.However,the limited types of resources,the default scheduling scoring strategy,and the lack of delay control module limit its resource scheduling performance.To address these problems,this paper proposes a multi-resource scheduling(MRS)scheme of Kubernetes for IIoT.The MRS scheme dynamically balances resource utilization by taking both requirements of tasks and the current system state into consideration.Furthermore,the experiments demonstrate the effectiveness of the MRS scheme in terms of delay control and resource utilization.展开更多
The Industrial Internet of Things(IIoT)has been growing for presentations in industry in recent years.Security for the IIoT has unavoidably become a problem in terms of creating safe applications.Due to continual need...The Industrial Internet of Things(IIoT)has been growing for presentations in industry in recent years.Security for the IIoT has unavoidably become a problem in terms of creating safe applications.Due to continual needs for new functionality,such as foresight,the number of linked devices in the industrial environment increases.Certification of fewer signatories gives strong authentication solutions and prevents trustworthy third parties from being publicly certified among available encryption instruments.Hence this blockchain-based endpoint protection platform(BCEPP)has been proposed to validate the network policies and reduce overall latency in isolation or hold endpoints.A resolver supports the encoded model as an input;network functions can be optimized as an output in an infrastructure network.The configuration of the virtual network functions(VNFs)involved fulfills network characteristics.The output ensures that the final service is supplied at the least cost,including processing time and network latency.According to the findings of this comparison,our design is better suited to simplified trust management in IIoT devices.Thus,the experimental results show the adaptability and resilience of our suggested confidence model against behavioral changes in hostile settings in IIoT networks.The experimental results show that our proposed method,BCEPP,has the following,when compared to other methods:high computational cost of 95.3%,low latency ratio of 28.5%,increased data transmitting rate up to 94.1%,enhanced security rate of 98.6%,packet reception ratio of 96.1%,user satisfaction index of 94.5%,and probability ratio of 33.8%.展开更多
The rapid development of the Internet of Things(IoT)in the industrial domain has led to the new term the Industrial Internet of Things(IIoT).The IIoT includes several devices,applications,and services that connect the...The rapid development of the Internet of Things(IoT)in the industrial domain has led to the new term the Industrial Internet of Things(IIoT).The IIoT includes several devices,applications,and services that connect the physical and virtual space in order to provide smart,cost-effective,and scalable systems.Although the IIoT has been deployed and integrated into a wide range of industrial control systems,preserving security and privacy of such a technology remains a big challenge.An anomaly-based Intrusion Detection System(IDS)can be an effective security solution for maintaining the confidentiality,integrity,and availability of data transmitted in IIoT environments.In this paper,we propose an intelligent anomalybased IDS framework in the context of fog-to-things communications to decentralize the cloud-based security solution into a distributed architecture(fog nodes)near the edge of the data source.The anomaly detection system utilizes minimum redundancy maximum relevance and principal component analysis as the featured engineering methods to select the most important features,reduce the data dimensionality,and improve detection performance.In the classification stage,anomaly-based ensemble learning techniques such as bagging,LPBoost,RUSBoost,and Adaboost models are implemented to determine whether a given flow of traffic is normal or malicious.To validate the effectiveness and robustness of our proposed model,we evaluate our anomaly detection approach on a new driven IIoT dataset called XIIoTID,which includes new IIoT protocols,various cyberattack scenarios,and different attack protocols.The experimental results demonstrated that our proposed anomaly detection method achieved a higher accuracy rate of 99.91%and a reduced false alarm rate of 0.1%compared to other recently proposed techniques.展开更多
The industrial Internet of Things (IIoT) is an important engine for manufacturingenterprises to provide intelligent products and services. With the development of IIoT, moreand more attention has been paid to the appl...The industrial Internet of Things (IIoT) is an important engine for manufacturingenterprises to provide intelligent products and services. With the development of IIoT, moreand more attention has been paid to the application of ultra-reliable and low latency communications(URLLC) in the 5G system. The data analysis model represented by digital twins isthe core of IIoT development in the manufacturing industry. In this paper, the efforts of3GPP are introduced for the development of URLLC in reducing delay and enhancing reliability,as well as the research on little jitter and high transmission efficiency. The enhancedkey technologies required in the IIoT are also analyzed. Finally, digital twins are analyzedaccording to the actual IIoT situation.展开更多
As a typical industrial Internet of things(IIOT)service,demand response(DR)is becoming a promising enabler for intelligent energy management in 6 G-enabled smart grid systems,to achieve quick response for supply-deman...As a typical industrial Internet of things(IIOT)service,demand response(DR)is becoming a promising enabler for intelligent energy management in 6 G-enabled smart grid systems,to achieve quick response for supply-demand mismatches.How-ever,existing literatures try to adjust customers’load profiles optimally,instead of electricity overhead,energy consumption patterns of residential appliances,customer satisfaction levels,and energy consumption habits.In this paper,a novel DR method is investigated by mixing the aforementioned factors,where the residential customer cluster is proposed to enhance the performance.Clustering approaches are leveraged to study the electricity consumption habits of various customers by extracting their features and characteristics from historical data.Based on the extracted information,the residential appliances can be scheduled effectively and flexibly.Moreover,we propose and study an efficient optimization framework to obtain the optimal scheduling solution by using clustering and deep learning methods.Extensive simulation experiments are conducted with real-world traces.Numerical results show that the proposed DR method and optimization framework outperform other baseline schemes in terms of the system overhead and peak-to-average ratio(PAR).The impact of various factors on the system utility is further analyzed,which provides useful insights on improving the efficiency of the DR strategy.With the achievement of efficient and intelligent energy management,the proposed method also promotes the realization of China’s carbon peaking and carbon neutrality goals.展开更多
The emergence of industry 4.0 stems from research that has received a great deal of attention in the last few decades.Consequently,there has been a huge paradigm shift in the manufacturing and production sectors.Howev...The emergence of industry 4.0 stems from research that has received a great deal of attention in the last few decades.Consequently,there has been a huge paradigm shift in the manufacturing and production sectors.However,this poses a challenge for cybersecurity and highlights the need to address the possible threats targeting(various pillars of)industry 4.0.However,before providing a concrete solution certain aspect need to be researched,for instance,cybersecurity threats and privacy issues in the industry.To fill this gap,this paper discusses potential solutions to cybersecurity targeting this industry and highlights the consequences of possible attacks and countermeasures(in detail).In particular,the focus of the paper is on investigating the possible cyber-attacks targeting 4 layers of IIoT that is one of the key pillars of Industry 4.0.Based on a detailed review of existing literature,in this study,we have identified possible cyber threats,their consequences,and countermeasures.Further,we have provided a comprehensive framework based on an analysis of cybersecurity and privacy challenges.The suggested framework provides for a deeper understanding of the current state of cybersecurity and sets out directions for future research and applications.展开更多
Generally,the risks associated with malicious threats are increasing for the Internet of Things(IoT)and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices.T...Generally,the risks associated with malicious threats are increasing for the Internet of Things(IoT)and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices.Thus,anomaly-based intrusion detection models for IoT networks are vital.Distinct detection methodologies need to be developed for the Industrial Internet of Things(IIoT)network as threat detection is a significant expectation of stakeholders.Machine learning approaches are considered to be evolving techniques that learn with experience,and such approaches have resulted in superior performance in various applications,such as pattern recognition,outlier analysis,and speech recognition.Traditional techniques and tools are not adequate to secure IIoT networks due to the use of various protocols in industrial systems and restricted possibilities of upgradation.In this paper,the objective is to develop a two-phase anomaly detection model to enhance the reliability of an IIoT network.In the first phase,SVM and Naïve Bayes,are integrated using an ensemble blending technique.K-fold cross-validation is performed while training the data with different training and testing ratios to obtain optimized training and test sets.Ensemble blending uses a random forest technique to predict class labels.An Artificial Neural Network(ANN)classifier that uses the Adam optimizer to achieve better accuracy is also used for prediction.In the second phase,both the ANN and random forest results are fed to the model’s classification unit,and the highest accuracy value is considered the final result.The proposed model is tested on standard IoT attack datasets,such as WUSTL_IIOT-2018,N_BaIoT,and Bot_IoT.The highest accuracy obtained is 99%.A comparative analysis of the proposed model using state-of-the-art ensemble techniques is performed to demonstrate the superiority of the results.The results also demonstrate that the proposed model outperforms traditional techniques and thus improves the reliability of an IIoT network.展开更多
基金supported in part by the Science and Technology development fund(FDCT)of Macao(011/2017/A)the National Natural Science Foundation of China(61803397)。
文摘Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things(IIOT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management.Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart.We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.
基金supported by ZTE Industry-Academia-Research Cooperation Funds
文摘Time sensitive networking(TSN)is a set of standards developed on the basis of audio video bridging(AVB).It has a promising future in the Industrial Internet of Things and vehicle-mounted multimedia,with such advantages as high bandwidth,interoperability and low cost.In this paper,the TSN protocol stack is described and key technologies of network operation are summarized,including time synchronization,scheduling and flow shaping,flow management and fault tolerant mechanism.The TSN network model is then established.Its performance is illustrated to show how the frame priority works and also show the influence of IEEE802.1Qbv time-aware shaper and IEEE802.1Qbu frame preemption on network and time-sensitive data.Finally,we briefly discuss the challenges faced by TSN and the focus of future research.
基金This work was supported in part by the National Natural Science Foundation of China(Nos.62072074,62076054,62027827,62002047)the Sichuan Science and Technology Innovation Platform and Talent Plan(Nos.2020JDJQ0020,2022JDJQ0039)+2 种基金the Sichuan Science and Technology Support Plan(Nos.2020YFSY0010,2022YFQ0045,2022YFS0220,2023YFG0148,2021YFG0131)the YIBIN Science and Technology Support Plan(No.2021CG003)the Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China(Nos.ZYGX2021YGLH212,ZYGX2022YGRH012).
文摘With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce overheads.However,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be addressed.The SM2 digital signature algorithm can provide an authentication mechanism for data to solve such problems.Unfortunately,it still suffers from the problem of key exposure.In order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 algorithm.This scheme boasts strong key insulation and secure keyupdates.Our scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud environments.Finally,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.
基金supported by the National Key Research and Development Program of China under Grant 2019YFB2102001.
文摘Nowadays,a large number of intelligent devices involved in the Industrial Internet of Things(IIoT)environment are posing unprecedented cybersecurity challenges.Due to the limited budget for security protection,the IIoT devices are vulnerable and easily compromised to launch Distributed Denial-of-Service(DDoS)attacks,resulting in disastrous results.Unfortunately,considering the particularity of the IIoT environment,most of the defense solutions in traditional networks cannot be directly applied to IIoT with acceptable security performance.Therefore,in this work,we propose a multi-point collaborative defense mechanism against DDoS attacks for IIoT.Specifically,for the single point DDoS defense,we design an edge-centric mechanism termed EdgeDefense for the detection,identification,classification,and mitigation of DDoS attacks and the generation of defense information.For the practical multi-point scenario,we propose a collaborative defense model against DDoS attacks to securely share the defense information across the network through the blockchain.Besides,a fast defense information sharing mechanism is designed to reduce the delay of defense information sharing and provide a responsive cybersecurity guarantee.The simulation results indicate that the identification and classification performance of the two machine learning models designed for EdgeDefense are better than those of the state-of-the-art baseline models,and therefore EdgeDefense can defend against DDoS attacks effectively.The results also verify that the proposed fast sharing mechanism can reduce the propagation delay of the defense information blocks effectively,thereby improving the responsiveness of the multi-point collaborative DDoS defense.
基金This work is funded by National Major Project(No.2017ZX03001021-005)National Key R&D Program of China(No.2017YFB1001600)+1 种基金Standard Development and Test bed Construction for Smart Factory Virtual Mapping Model and Digitized Delivery(No.MIIT 2019-00899-3-1)2018 Sugon Intelligent Factory on Advanced Computing Devices(No.MIIT 2018-265-137).
文摘Aiming for ultra-reliable low-latency wireless communications required in industrial internet of things(IIoT)applications,this paper studies a simple cognitive radio non-orthogonal multiple access(CR-NOMA)downlink system.This system consists of two secondary users(SUs)dynamically interfered by the primary user(PU),and its performance is characterized by the outage probability of the SU communications.This outage probability is calculated under two conditions where,a)the transmission of PU starts after the channel state information(CSI)is acquired,so the base station(BS)is oblivious of the interference,and b)when the BS is aware of the PU interference,and the NOMA transmission is adapted to the more comprehensive knowledge of the signal to interference plus noise ratio(SINR).These results are verified by simulations,and their good agreement suggests our calculations can be used to reduce the complexity of future analysis.We find the outage probability is reduced when the SUs move further away from the primary transmitter or when the signal from PU is less powerful,and the BS always has better performance when it is aware of the interference.The findings thus emphasize the importance of monitoring the channel quality and realtime feedback to optimize the performance of CR-NOMA system.
文摘The rapid growth of the Internet of Things(IoT)in the industrial sector has given rise to a new term:the Industrial Internet of Things(IIoT).The IIoT is a collection of devices,apps,and services that connect physical and virtual worlds to create smart,cost-effective,and scalable systems.Although the IIoT has been implemented and incorporated into a wide range of industrial control systems,maintaining its security and privacy remains a significant concern.In the IIoT contexts,an intrusion detection system(IDS)can be an effective security solution for ensuring data confidentiality,integrity,and availability.In this paper,we propose an intelligent intrusion detection technique that uses principal components analysis(PCA)as a feature engineering method to choose the most significant features,minimize data dimensionality,and enhance detection performance.In the classification phase,we use clustering algorithms such as K-medoids and K-means to determine whether a given flow of IIoT traffic is normal or attack for binary classification and identify the group of cyberattacks according to its specific type for multi-class classification.To validate the effectiveness and robustness of our proposed model,we validate the detection method on a new driven IIoT dataset called X-IIoTID.The performance results showed our proposed detection model obtained a higher accuracy rate of 99.79%and reduced error rate of 0.21%when compared to existing techniques.
基金supported by the Key Program of the National Natural Science Foundation of China(61431008)the Project of Intelligent Manufacturing Integrated Standardization and New Model Application
文摘The integration of the Internet and the traditional manufacturing industry makes the industrial Internet of things(IIoT) as a popular research topic. However, traditional industrial networks continue to face challenges of resource management and limited raw data storage and computation capacity. A novel software defined industrial network(SDIN) architecture was proposed to address the existing drawbacks in IIoT such as resource utilization, data processing and storage, and system compatibility. The architecture is developed based on the software defined network(SDN) architecture, combining hierarchical cloud and fog computing and content-aware caching technologies. Based on the SDIN architecture, two types of edge computing strategies in industrial applications are discussed. Different scenarios and service requirements are considered. The simulation results confirm that the SDIN architecture is feasible and effective in the application of edge computing offloading.
基金supported by the Projects of Software of Big Data Processing Tool(TC210804V-1)Big Data Risk Screening Model Procurement(No.S20200).
文摘With the development of the Industrial Internet of Things(IIoT),end devices(EDs)are equipped with more functions to capture information.Therefore,a large amount of data is generated at the edge of the network and needs to be processed.However,no matter whether these computing tasks are offloaded to traditional central clusters or mobile edge computing(MEC)devices,the data is short of security and may be changed during transmission.In view of this challenge,this paper proposes a trusted task offloading optimization scheme that can offer low latency and high bandwidth services for IIoT with data security.Blockchain technology is adopted to ensure data consistency.Meanwhile,to reduce the impact of low throughput of blockchain on task offloading performance,we design the processes of consensus and offloading as a Markov decision process(MDP)by defining states,actions,and rewards.Deep reinforcement learning(DRL)algorithm is introduced to dynamically select offloading actions.To accelerate the optimization,we design a novel reward function for the DRL algorithm according to the scale and computational complexity of the task.Experiments demonstrate that compared with methods without optimization,our mechanism performs better when it comes to the number of task offloading and throughput of blockchain.
文摘The evolution of the Internet of Things(IoT)has empowered modern industries with the capability to implement large-scale IoT ecosystems,such as the Industrial Internet of Things(IIoT).The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational andfinancial harm to organizations.To preserve the confidentiality,integrity,and availability of IIoT networks,an anomaly-based intrusion detection system(IDS)can be used to provide secure,reliable,and efficient IIoT ecosystems.In this paper,we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks.The proposed anomaly-based IDS is divided into three phases:pre-processing,feature selection,and classification.In the pre-processing phase,data cleaning and nor-malization are performed.In the feature selection phase,the candidates’feature vectors are computed using two feature reduction techniques,minimum redun-dancy maximum relevance and neighborhood components analysis.For thefinal step,the modeling phase,the following classifiers are used to perform the classi-fication:support vector machine,decision tree,k-nearest neighbors,and linear discriminant analysis.The proposed work uses a new data-driven IIoT data set called X-IIoTID.The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%,a sensitivity rate of 99.59%,a specificity rate of 99.58%,and a low false positive rate of 0.4%.
文摘Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However,IIoT involves some security vulnerabilities that are more damaging than those of IoT.Accordingly,Intrusion Detection Systems(IDSs)have been developed to forestall inevitable harmful intrusions.IDSs survey the environment to identify intrusions in real time.This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security.We combine Isolation Forest(IF)with Pearson’s Correlation Coefficient(PCC)to reduce computational cost and prediction time.IF is exploited to detect and remove outliers from datasets.We apply PCC to choose the most appropriate features.PCC and IF are applied exchangeably(PCCIF and IFPCC).The Random Forest(RF)classifier is implemented to enhance IDS performances.For evaluation,we use the Bot-IoT and NF-UNSW-NB15-v2 datasets.RF-PCCIF and RF-IFPCC show noteworthy results with 99.98%and 99.99%Accuracy(ACC)and 6.18 s and 6.25 s prediction time on Bot-IoT,respectively.The two models also score 99.30%and 99.18%ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2,respectively.Results prove that our designed model has several advantages and higher performance than related models.
文摘Industry 4.0 has become a reality by fusing the Industrial Internet of Things(IIoT)and Artificial Intelligence(AI),providing huge opportunities in the way manufacturing companies operate.However,the adoption of this paradigm shift,particularly in the field of smart factories and production,is still in its infancy,suffering from various issues,such as the lack of high-quality data,data with high-class imbalance,or poor diversity leading to inaccurate AI models.However,data is severely fragmented across different silos owned by several parties for a range of reasons,such as compliance and legal concerns,preventing discovery and insight-driven IIoT innovation.Notably,valuable and even vital information often remains unutilized as the rise and adoption of AI and IoT in parallel with the concerns and challenges associated with privacy and security.This adversely influences interand intra-organization collaborative use of IIoT data.To tackle these challenges,this article leverages emerging multi-party technologies,privacy-enhancing techniques(e.g.,Federated Learning),and AI approaches to present a holistic,decentralized architecture to form a foundation and cradle for a cross-company collaboration platform and a federated data space to tackle the creeping fragmented data landscape.Moreover,to evaluate the efficiency of the proposed reference model,a collaborative predictive diagnostics and maintenance case study is mapped to an edge-enabled IIoT architecture.Experimental results show the potential advantages of using the proposed approach for multi-party applications accelerating sovereign data sharing through Findable,Accessible,Interoperable,and Reusable(FAIR)principles.
基金This work was supported by the six talent peaks project in Jiangsu Province(No.XYDXX-012)Natural Science Foundation of China(No.62002045),China Postdoctoral Science Foundation(No.2021M690565)Fundamental Research Funds for the Cornell University(No.N2117002).
文摘The ongoing expansion of the Industrial Internet of Things(IIoT)is enabling the possibility of effective Industry 4.0,where massive sensing devices in heterogeneous environments are connected through dedicated communication protocols.This brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face,providing ad-hoc functions for scheduling and guaranteeing the network operations.Recently,the large development of SoftwareDefined Networking(SDN)and Artificial Intelligence(AI)technologies have made feasible the design and control of scalable and secure IIoT networks.This paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT networks.After surveying the state-of-the-art research efforts in the subject,the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network(AI-SDIN)that divides the traditional industrial networks into three functional layers.And with this aim in mind,key technologies(Blockchain-based Data Sharing,Intelligent Wireless Data Sensing,Edge Intelligence,Time-Sensitive Networks,Integrating SDN&TSN,Distributed AI)and improve applications based on AISDIN are also discussed.Further,the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.
文摘Industrial Internet of Things(IIoT)offers efficient communication among business partners and customers.With an enlargement of IoT tools connected through the internet,the ability of web traffic gets increased.Due to the raise in the size of network traffic,discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues.A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification(MPDQDJREBC)is introduced for accurate attack detection wi th minimum time consumption in IIoT.The proposed MPDQDJREBC technique includes feature selection and categorization.First,the network traffic features are collected from the dataset.Then applying the Maximum Posterior Dichotomous Quadratic Discriminant analysis to find the significant features for accurate classification and minimize the time consumption.After the significant features selection,classification is performed using the Jaccardized Rocchio Emphasis Boost technique.Jaccardized Rocchio Emphasis Boost Classification technique combines the weak learner result into strong output.Jaccardized Rocchio classification technique is considered as the weak learners to identify the normal and attack.Thus,proposed MPDQDJREBC technique gives strong classification results through lessening the quadratic error.This assists for proposed MPDQDJREBC technique to get better the accuracy for attack detection with reduced time usage.Experimental assessment is carried out with UNSW_NB15 Dataset using different factors such as accuracy,precision,recall,F-measure and attack detection time.The observed results exhibit the MPDQDJREBC technique provides higher accuracy and lesser time consumption than the conventional techniques.
基金This work was supported by the National Natural Science Foundation of China(61872423)the Industry Prospective Primary Research&Development Plan of Jiangsu Province(BE2017111)the Scientific Research Foundation of the Higher Education Institutions of Jiangsu Province(19KJA180006).
文摘With the rapid development of data applications in the scene of Industrial Internet of Things(IIoT),how to schedule resources in IIoT environment has become an urgent problem to be solved.Due to benefit of its strong scalability and compatibility,Kubernetes has been applied to resource scheduling in IIoT scenarios.However,the limited types of resources,the default scheduling scoring strategy,and the lack of delay control module limit its resource scheduling performance.To address these problems,this paper proposes a multi-resource scheduling(MRS)scheme of Kubernetes for IIoT.The MRS scheme dynamically balances resource utilization by taking both requirements of tasks and the current system state into consideration.Furthermore,the experiments demonstrate the effectiveness of the MRS scheme in terms of delay control and resource utilization.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number IFPHI-218-611-2020.”。
文摘The Industrial Internet of Things(IIoT)has been growing for presentations in industry in recent years.Security for the IIoT has unavoidably become a problem in terms of creating safe applications.Due to continual needs for new functionality,such as foresight,the number of linked devices in the industrial environment increases.Certification of fewer signatories gives strong authentication solutions and prevents trustworthy third parties from being publicly certified among available encryption instruments.Hence this blockchain-based endpoint protection platform(BCEPP)has been proposed to validate the network policies and reduce overall latency in isolation or hold endpoints.A resolver supports the encoded model as an input;network functions can be optimized as an output in an infrastructure network.The configuration of the virtual network functions(VNFs)involved fulfills network characteristics.The output ensures that the final service is supplied at the least cost,including processing time and network latency.According to the findings of this comparison,our design is better suited to simplified trust management in IIoT devices.Thus,the experimental results show the adaptability and resilience of our suggested confidence model against behavioral changes in hostile settings in IIoT networks.The experimental results show that our proposed method,BCEPP,has the following,when compared to other methods:high computational cost of 95.3%,low latency ratio of 28.5%,increased data transmitting rate up to 94.1%,enhanced security rate of 98.6%,packet reception ratio of 96.1%,user satisfaction index of 94.5%,and probability ratio of 33.8%.
文摘The rapid development of the Internet of Things(IoT)in the industrial domain has led to the new term the Industrial Internet of Things(IIoT).The IIoT includes several devices,applications,and services that connect the physical and virtual space in order to provide smart,cost-effective,and scalable systems.Although the IIoT has been deployed and integrated into a wide range of industrial control systems,preserving security and privacy of such a technology remains a big challenge.An anomaly-based Intrusion Detection System(IDS)can be an effective security solution for maintaining the confidentiality,integrity,and availability of data transmitted in IIoT environments.In this paper,we propose an intelligent anomalybased IDS framework in the context of fog-to-things communications to decentralize the cloud-based security solution into a distributed architecture(fog nodes)near the edge of the data source.The anomaly detection system utilizes minimum redundancy maximum relevance and principal component analysis as the featured engineering methods to select the most important features,reduce the data dimensionality,and improve detection performance.In the classification stage,anomaly-based ensemble learning techniques such as bagging,LPBoost,RUSBoost,and Adaboost models are implemented to determine whether a given flow of traffic is normal or malicious.To validate the effectiveness and robustness of our proposed model,we evaluate our anomaly detection approach on a new driven IIoT dataset called XIIoTID,which includes new IIoT protocols,various cyberattack scenarios,and different attack protocols.The experimental results demonstrated that our proposed anomaly detection method achieved a higher accuracy rate of 99.91%and a reduced false alarm rate of 0.1%compared to other recently proposed techniques.
文摘The industrial Internet of Things (IIoT) is an important engine for manufacturingenterprises to provide intelligent products and services. With the development of IIoT, moreand more attention has been paid to the application of ultra-reliable and low latency communications(URLLC) in the 5G system. The data analysis model represented by digital twins isthe core of IIoT development in the manufacturing industry. In this paper, the efforts of3GPP are introduced for the development of URLLC in reducing delay and enhancing reliability,as well as the research on little jitter and high transmission efficiency. The enhancedkey technologies required in the IIoT are also analyzed. Finally, digital twins are analyzedaccording to the actual IIoT situation.
基金supported by the National Natural Science Foundation of China(62171218)。
文摘As a typical industrial Internet of things(IIOT)service,demand response(DR)is becoming a promising enabler for intelligent energy management in 6 G-enabled smart grid systems,to achieve quick response for supply-demand mismatches.How-ever,existing literatures try to adjust customers’load profiles optimally,instead of electricity overhead,energy consumption patterns of residential appliances,customer satisfaction levels,and energy consumption habits.In this paper,a novel DR method is investigated by mixing the aforementioned factors,where the residential customer cluster is proposed to enhance the performance.Clustering approaches are leveraged to study the electricity consumption habits of various customers by extracting their features and characteristics from historical data.Based on the extracted information,the residential appliances can be scheduled effectively and flexibly.Moreover,we propose and study an efficient optimization framework to obtain the optimal scheduling solution by using clustering and deep learning methods.Extensive simulation experiments are conducted with real-world traces.Numerical results show that the proposed DR method and optimization framework outperform other baseline schemes in terms of the system overhead and peak-to-average ratio(PAR).The impact of various factors on the system utility is further analyzed,which provides useful insights on improving the efficiency of the DR strategy.With the achievement of efficient and intelligent energy management,the proposed method also promotes the realization of China’s carbon peaking and carbon neutrality goals.
基金The author(s)acknowledge Jouf University,Saudi Arabia for his funding support.
文摘The emergence of industry 4.0 stems from research that has received a great deal of attention in the last few decades.Consequently,there has been a huge paradigm shift in the manufacturing and production sectors.However,this poses a challenge for cybersecurity and highlights the need to address the possible threats targeting(various pillars of)industry 4.0.However,before providing a concrete solution certain aspect need to be researched,for instance,cybersecurity threats and privacy issues in the industry.To fill this gap,this paper discusses potential solutions to cybersecurity targeting this industry and highlights the consequences of possible attacks and countermeasures(in detail).In particular,the focus of the paper is on investigating the possible cyber-attacks targeting 4 layers of IIoT that is one of the key pillars of Industry 4.0.Based on a detailed review of existing literature,in this study,we have identified possible cyber threats,their consequences,and countermeasures.Further,we have provided a comprehensive framework based on an analysis of cybersecurity and privacy challenges.The suggested framework provides for a deeper understanding of the current state of cybersecurity and sets out directions for future research and applications.
基金work through Researchers Supporting Project number(RSP-2020/164),King Saud University,Riyadh,Saudi Arabia.
文摘Generally,the risks associated with malicious threats are increasing for the Internet of Things(IoT)and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices.Thus,anomaly-based intrusion detection models for IoT networks are vital.Distinct detection methodologies need to be developed for the Industrial Internet of Things(IIoT)network as threat detection is a significant expectation of stakeholders.Machine learning approaches are considered to be evolving techniques that learn with experience,and such approaches have resulted in superior performance in various applications,such as pattern recognition,outlier analysis,and speech recognition.Traditional techniques and tools are not adequate to secure IIoT networks due to the use of various protocols in industrial systems and restricted possibilities of upgradation.In this paper,the objective is to develop a two-phase anomaly detection model to enhance the reliability of an IIoT network.In the first phase,SVM and Naïve Bayes,are integrated using an ensemble blending technique.K-fold cross-validation is performed while training the data with different training and testing ratios to obtain optimized training and test sets.Ensemble blending uses a random forest technique to predict class labels.An Artificial Neural Network(ANN)classifier that uses the Adam optimizer to achieve better accuracy is also used for prediction.In the second phase,both the ANN and random forest results are fed to the model’s classification unit,and the highest accuracy value is considered the final result.The proposed model is tested on standard IoT attack datasets,such as WUSTL_IIOT-2018,N_BaIoT,and Bot_IoT.The highest accuracy obtained is 99%.A comparative analysis of the proposed model using state-of-the-art ensemble techniques is performed to demonstrate the superiority of the results.The results also demonstrate that the proposed model outperforms traditional techniques and thus improves the reliability of an IIoT network.
