An Efficient and Provably Secure SM2 Key-Insulated Signature Scheme for Industrial Internet of Things

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.

A Double-Timescale Reinforcement Learning Based Cloud-Edge Collaborative Framework for Decomposable Intelligent Services in Industrial Internet of Things

With the proportion of intelligent services in the industrial internet of things(IIoT)rising rapidly,its data dependency and decomposability increase the difficulty of scheduling computing resources.In this paper,we propose an intelligent service computing framework.In the framework,we take the long-term rewards of its important participants,edge service providers,as the optimization goal,which is related to service delay and computing cost.Considering the different update frequencies of data deployment and service offloading,double-timescale reinforcement learning is utilized in the framework.In the small-scale strategy,the frequent concurrency of services and the difference in service time lead to the fuzzy relationship between reward and action.To solve the fuzzy reward problem,a reward mapping-based reinforcement learning(RMRL)algorithm is proposed,which enables the agent to learn the relationship between reward and action more clearly.The large time scale strategy adopts the improved Monte Carlo tree search(MCTS)algorithm to improve the learning speed.The simulation results show that the strategy is superior to popular reinforcement learning algorithms such as double Q-learning(DDQN)and dueling Q-learning(dueling-DQN)in learning speed,and the reward is also increased by 14%.

Age-Driven Joint Sampling and Non-Slot Based Scheduling for Industrial Internet of Things

Effective control of time-sensitive industrial applications depends on the real-time transmission of data from underlying sensors.Quantifying the data freshness through age of information(AoI),in this paper,we jointly design sampling and non-slot based scheduling policies to minimize the maximum time-average age of information(MAoI)among sensors with the constraints of average energy cost and finite queue stability.To overcome the intractability involving high couplings of such a complex stochastic process,we first focus on the single-sensor time-average AoI optimization problem and convert the constrained Markov decision process(CMDP)into an unconstrained Markov decision process(MDP)by the Lagrangian method.With the infinite-time average energy and AoI expression expended as the Bellman equation,the singlesensor time-average AoI optimization problem can be approached through the steady-state distribution probability.Further,we propose a low-complexity sub-optimal sampling and semi-distributed scheduling scheme for the multi-sensor scenario.The simulation results show that the proposed scheme reduces the MAoI significantly while achieving a balance between the sampling rate and service rate for multiple sensors.

Energy Minimization for Heterogenous Traffic Coexistence with Puncturing in Mobile Edge Computing-Based Industrial Internet of Things

Puncturing has been recognized as a promising technology to cope with the coexistence problem of enhanced mobile broadband(eMBB) and ultra-reliable low latency communications(URLLC)traffic. However, the steady performance of eMBB traffic while meeting the requirements of URLLC traffic with puncturing is a major challenge in some realistic scenarios. In this paper, we pay attention to the timely and energy-efficient processing for eMBB traffic in the industrial Internet of Things(IIoT), where mobile edge computing(MEC) is employed for data processing. Specifically, the performance of eMBB traffic and URLLC traffic in a MEC-based IIoT system is ensured by setting the threshold of tolerable delay and outage probability, respectively. Furthermore,considering the limited energy supply, an energy minimization problem of eMBB device is formulated under the above constraints, by jointly optimizing the resource blocks(RBs) punctured by URLLC traffic, data offloading and transmit power of eMBB device. With Markov's inequality, the problem is reformulated by transforming the probabilistic outage constraint into a deterministic constraint. Meanwhile, an iterative energy minimization algorithm(IEMA) is proposed.Simulation results demonstrate that our algorithm has a significant reduction in the energy consumption for eMBB device and achieves a better overall effect compared to several benchmarks.

Edge Cloud Selection in Mobile Edge Computing(MEC)-Aided Applications for Industrial Internet of Things(IIoT)Services

In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency.There are two types of costs for this kind of IoT network:a communication cost and a computing cost.For service efficiency,the communication cost of data transmission should be minimized,and the computing cost in the edge cloud should be also minimized.Therefore,in this paper,the communication cost for data transmission is defined as the delay factor,and the computing cost in the edge cloud is defined as the waiting time of the computing intensity.The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs.That is,a device chooses a routing path to the selected edge cloud based on the costs.The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load.The performance of the proposed method is validated through extensive computer simulation.When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states,the proposed method reduced both the average delay and the service pause counts to about 25%of the existing method.

Enhancing Internet of Things Intrusion Detection Using Artificial Intelligence

Escalating cyber security threats and the increased use of Internet of Things(IoT)devices require utilisation of the latest technologies available to supply adequate protection.The aim of Intrusion Detection Systems(IDS)is to prevent malicious attacks that corrupt operations and interrupt data flow,which might have significant impact on critical industries and infrastructure.This research examines existing IDS,based on Artificial Intelligence(AI)for IoT devices,methods,and techniques.The contribution of this study consists of identification of the most effective IDS systems in terms of accuracy,precision,recall and F1-score;this research also considers training time.Results demonstrate that Graph Neural Networks(GNN)have several benefits over other traditional AI frameworks through their ability to achieve in excess of 99%accuracy in a relatively short training time,while also capable of learning from network traffic the inherent characteristics of different cyber-attacks.These findings identify the GNN(a Deep Learning AI method)as the most efficient IDS system.The novelty of this research lies also in the linking between high yielding AI-based IDS algorithms and the AI-based learning approach for data privacy protection.This research recommends Federated Learning(FL)as the AI training model,which increases data privacy protection and reduces network data flow,resulting in a more secure and efficient IDS solution.

An ensemble deep learning model for cyber threat hunting in industrial internet of things

By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)platforms.Such vast heterogeneous data increase the challenges of security risks and data analysis procedures.As IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to operate.In this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is proposed.In this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data dimension.In addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and performance.To solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection model.In this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on data.The results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,respectively.Moreover,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.

Intelligent Intrusion Detection System for Industrial Internet of Things Environment

Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival rates.The classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity.To resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform.The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complex-ity and maximum detection accuracy.The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with CSOA.Besides,the OWKELM technique is applied for the intrusion detection and classification process.In addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization(SFO)algorithm.The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance.In order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.

Intelligent Intrusion Detection for Industrial Internet of Things Using Clustering Techniques

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.

Anomaly Detection for Industrial Internet of Things Cyberattacks

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%.

AID4I:An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

By identifying and responding to any malicious behavior that could endanger the system,the Intrusion Detection System(IDS)is crucial for preserving the security of the Industrial Internet of Things(IIoT)network.The benefit of anomaly-based IDS is that they are able to recognize zeroday attacks due to the fact that they do not rely on a signature database to identify abnormal activity.In order to improve control over datasets and the process,this study proposes using an automated machine learning(AutoML)technique to automate the machine learning processes for IDS.Our groundbreaking architecture,known as AID4I,makes use of automatic machine learning methods for intrusion detection.Through automation of preprocessing,feature selection,model selection,and hyperparameter tuning,the objective is to identify an appropriate machine learning model for intrusion detection.Experimental studies demonstrate that the AID4I framework successfully proposes a suitablemodel.The integrity,security,and confidentiality of data transmitted across the IIoT network can be ensured by automating machine learning processes in the IDS to enhance its capacity to identify and stop threatening activities.With a comprehensive solution that takes advantage of the latest advances in automated machine learning methods to improve network security,AID4I is a powerful and effective instrument for intrusion detection.In preprocessing module,three distinct imputation methods are utilized to handle missing data,ensuring the robustness of the intrusion detection system in the presence of incomplete information.Feature selection module adopts a hybrid approach that combines Shapley values and genetic algorithm.The Parameter Optimization module encompasses a diverse set of 14 classification methods,allowing for thorough exploration and optimization of the parameters associated with each algorithm.By carefully tuning these parameters,the framework enhances its adaptability and accuracy in identifying potential intrusions.Experimental results demonstrate that the AID4I framework can achieve high levels of accuracy in detecting network intrusions up to 14.39%on public datasets,outperforming traditional intrusion detection methods while concurrently reducing the elapsed time for training and testing.

Robust graph‐based localization for industrial Internet of things in the presence of flipping ambiguities

Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various applications.Therefore,a novel localisation algorithm is proposed for noisy range measurements in IIoT networks.The position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position estimation.Therefore,the AMs selection algorithm for the localisation of BMs in the IIoT network is introduced.Only those AMs will participate in the localisation process,which increases the accuracy of the final location estimate.Then,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement noise.The results are compared with the state‐of‐the‐art and verified through numerous simulations.

A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT

The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.

An Efficient SDFRM Security System for Blockchain Based Internet of Things

Blockchain has recently sparked interest in both the technological and businessfirms.The Internet of Things's(IoT)core principle emerged due to the connectivity of several new technologies,including wireless technology,the Inter-net,embedded automation systems,and micro-electromechanical devices.Manu-facturing environments and operations have been successfully converted by implementing recent advanced technology like Cloud Computing(CC),Cyber-Physical System(CSP),Information and Communication Technologies(ICT)and Enterprise Model,and other technological innovations into the fourth indus-trial revolution referred to as Industry 4.0.Data management is defined as the pro-cess of accumulation in order to make better business decisions,and process,secure and store information about a company.In the incipient model,there are interconnected contrivances and Machine-to-Machine(M2M)interactions,and transaction data are stored on the Blockchain.Security is a challenging aspect that must be punctiliously considered during the design and development phases of a CSP.In this research article,we proposed a Secure and Distributed Framework for Resource Management(SDFRM)in Industry 4.0 environments within a distribu-ted and collaborative Industry 4.0 system,the dynamic and trust-based Distributed Management Framework(DMF)of shared resource access.Such issues are focused by taking into account of the traditional characteristics of IoT/Industrial Internet of Things’(IIoT)-predicated environments,an SDFRM in Industry 4.0 environments within a distributed and collaborative Industry 4.0 system.Also,to ensure strong privacy over the procedures associated with Access Control(AC),a privacy-preserving method is proposed and integrated into the DMF.The proposed DMF,based on blockchain technology and peer-to-peer networks,allows dynamic access management and system governance without using third parties who could be attacked.We worked hard to design and implement the pro-posal to demonstrate its viability and evaluate its performance.Our proposal out-performs the Multichain Blockchain in terms of successful storage transactions with an achieved average throughput of 98.15%.

Internet of things:Conceptual network structure,main challenges and future directions

Internet of Things(IoT)is a key technology trend that supports our digitalized society in applications such as smart countries and smart cities.In this study,we investigate the existing strategic themes,thematic evolution structure,key challenges,and potential research opportunities associated with the IoT.For this study,we conduct a Bibliometric Performance and Network Analysis(BPNA),supplemented by an exhaustive Systematic Literature Review(SLR).Specifically,in BPNA,the software SciMAT is used to analyze 14,385 documents and 30,381 keywords in the Web of Science(WoS)database,which was released between 2002 and 2019.The results reveal that 31 clusters are classified according to their importance and development,and the conceptual structures of key clusters are presented,along with their performance analysis and the relationship with other subthemes.The thematic evolution structure describes the important cluster(s)over time.For the SLR,23 documents are analyzed.The SLR reveals key challenges and limitations associated with the IoT.We expect the results will form the basis of future research and guide decision-making in the IoT and other supporting industries.

SBFT:A BFT Consensus Mechanism Based on DQN Algorithm for Industrial Internet of Thing

With the development and widespread use of blockchain in recent years,many projects have introduced blockchain technology to solve the growing security issues of the Industrial Internet of Things(IIoT).However,due to the conflict between the operational performance and security of the blockchain system and the compatibility issues with a large number of IIoT devices running together,the mainstream blockchain system cannot be applied to IIoT scenarios.In order to solve these problems,this paper proposes SBFT(Speculative Byzantine Consensus Protocol),a flexible and scalable blockchain consensus mechanism for the Industrial Internet of Things.SBFT has a consensus process based on speculation,improving the throughput and consensus speed of blockchain systems and reducing communication overhead.In order to improve the compatibility and scalability of the blockchain system,we select some nodes to participate in the consensus,and these nodes have better performance in the network.Since multiple properties determine node performance,we abstract the node selection problem as a joint optimization problem and use Dueling Deep Q Learning(DQL)to solve it.Finally,we evaluate the performance of the scheme through simulation,and the simulation results prove the superiority of our scheme.

A Novel Secure Data Transmission Scheme in Industrial Internet of Things

The industrial Internet of Things(IoT)is a trend of factory development and a basic condition of intelligent factory.It is very important to ensure the security of data transmission in industrial IoT.Applying a new chaotic secure communication scheme to address the security problem of data transmission is the main contribution of this paper.The scheme is proposed and studied based on the synchronization of different-structure fractional-order chaotic systems with different order.The Lyapunov stability theory is used to prove the synchronization between the fractional-order drive system and the response system.The encryption and decryption process of the main data signals is implemented by using the n-shift encryption principle.We calculate and analyze the key space of the scheme.Numerical simulations are introduced to show the effectiveness of theoretical approach we proposed.

An Efficient Security Solution for Industrial Internet of Things Applications

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%.

Cyber Security and Privacy Issues in Industrial Internet of Things

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.

Analysis of Industrial Internet of Things and Digital Twins

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.