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Improving Method of Anomaly Detection Performance for industrial iot Environment

Computers, Materials & Continua 2022年第9期72卷 5377-5394页

作者： Junwon Kim Jiho Shin Ki-Woong Park Jung Taek Seo Department of Information Security Engineering Gachon UniversitySeongnam13120Korea Police Science Institute Korea National Police UniversityAsan31539Korea SysCore Lab. Sejong UniversitySeoul05006Korea Department of Computer Engineering Gachon UniversitySeongnam13120Korea

industrial Control System(ICS),which is based on industrial iot(Iiot),has an intelligent mobile environment that supports various mobility,but there is a limit to relying only on the physical security of the ICS environment.Due to various threat factors that can disrupt the workflow of the Iiot,machine learning-based anomaly detection technologies are being presented;it is also essential to study for increasing detection performance to minimize model errors for promoting stable ICS operation.In this paper,we established the requirements for improving the anomaly detection performance in the Iiot-based ICS environment by analyzing the related cases.After that,we presented an improving method of the performance of a machine learning model specialized for Iiot-based ICS,which increases the detection rate by applying correlation coefficients and clustering;it provides a mechanism to predict thresholds on a per-sequence.Likewise,we adopted the HAI dataset environment that actively reflected the characteristics of Iiot-based ICS and demonstrated that performance could be improved through comparative experiments with the traditional method and our proposed method.The presented method can further improve the performance of commonly applied error-based detection techniques and includes a primary method that can be enhanced over existing detection techniques by analyzing correlation coefficients between features to consider feedback between ICS components.Those can contribute to improving the performance of several detection models applied in ICS and other areas.

关键词： industrial iot industrial control system anomaly detection clustering algorithm correlation coefficient

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Efficient Bandwidth Allocation and Computation Configuration in industrial iot

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ZTE Communications 2023年第1期21卷 55-63页

作者： HUANG Rui LI Huilin ZHANG Yongmin Central South University Changsha 410012China

With the advancement of the industrial Internet of Things(iot),the rapidly growing demand for data collection and processing poses a huge challenge to the design of data transmission and computation resources in the industrial scenario.Taking advantage of improved model accuracy by machine learning algorithms,we investigate the inner relationship of system performance and data transmission and computation resources,and then analyze the impacts of bandwidth allocation and computation resources on the accuracy of the system model in this paper.A joint bandwidth allocation and computation resource configuration scheme is proposed and the Karush-Kuhn-Tucker(KKT)conditions are used to get an optimal bandwidth allocation and computation configuration decision,which can minimize the total computation resource requirement and ensure the system accuracy meets the industrial requirements.Simulation results show that the proposed bandwidth allocation and computation resource configuration scheme can reduce the computing resource usage by 10%when compared to the average allocation strategy.

关键词： bandwidth allocation computation resource management industrial iot system accuracy

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Smart contract token-based privacy-preserving access control system for industrial Internet of Things

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Digital Communications and Networks 2023年第2期9卷 337-346页

作者： Weizheng Wang Huakun Huang Zhimeng Yin Thippa Reddy Gadekallu Mamoun Alazab Chunhua Su Department of Computer Science City University of Hong KongHong Kong SAR999077China Division of Computer Science University of AizuFukushima965-8580Japan School of Information Technology and Engineering Vellore Institute of TechnologyTamil NaduIndia College of Engineering IT and EnvironmentCharles Darwin UniversityAustralia School of Computer Science and Cyber Engineering Guangzhou UniversityChina

Due to mobile Internet technology's rapid popularization,the industrial Internet of Things(Iiot)can be seen everywhere in our daily lives.While Iiot brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device communications.Hence,at present,a reliable and dynamic access control management system for Iiot is in urgent need.Up till now,numerous access control architectures have been proposed for Iiot.However,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be met.In this paper,we offer a smart contract token-based solution for decentralized access control in Iiot systems.Specifically,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).These three contracts collaboratively supervise and manage various events in Iiot environments.We also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration process.Subsequently,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local blockchain.Finally,experiment results show that our scheme has achieved secure and dynamic access control for the Iiot system compared with related research.

关键词： Blockchain Privacy preservation Smart contract industrial iot

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Intelligent Deep Learning Model for Privacy Preserving Iiot on 6G Environment

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Computers, Materials & Continua 2022年第7期72卷 333-348页

作者： Anwer Mustafa Hilal Jaber SAlzahrani Ibrahim Abunadi Nadhem Nemri Fahd NAl-Wesabi Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer Science King King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen

In recent times,industrial Internet of Things(Iiot)experiences a high risk of cyber attacks which needs to be resolved.Blockchain technology can be incorporated into Iiot system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks.Although blockchain-based Iiot network renders a significant support and meet the service requirements of next generation network,the performance arrived at,in existing studies still needs improvement.In this scenario,the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for industrial iot(PPBDL-Iiot)on 6G environment.The proposed PPBDLIiot technique aims at identifying the existence of intrusions in network.Further,PPBDL-Iiot technique also involves the design of Chaos Game Optimization(CGO)with Bidirectional Gated Recurrent Neural Network(BiGRNN)technique for both detection and classification of intrusions in the network.Besides,CGO technique is applied to fine tune the hyperparameters in BiGRNN model.CGO algorithm is applied to optimally adjust the learning rate,epoch count,and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model.Moreover,Blockchain enabled Integrity Check(BEIC)scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based Iiot system.The performance of the proposed PPBDL-Iiot methodology was validated using industrial Control System Cyber-attack(ICSCA)dataset and the outcomes were analysed under various measures.The experimental results highlight the supremacy of the presented PPBDL-Iiot technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.

关键词： 6G networks industrial iot blockchain security intrusion detection artificial intelligence

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Optimal Deep Learning Based Intruder Identification in industrial Internet of Things Environment

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Computer Systems Science & Engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial transformation.The industrial Internet of Things(Iiot)or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether.In industry 4.0,powerful IntrusionDetection Systems(IDS)play a significant role in ensuring network security.Though various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of networks.This is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic Iiot networks.Further,the existing Deep Learning(DL)can be employed to identify anonymous intrusions.Therefore,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the Iiot environment.The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format.The HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of dimensionality.Moreover,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the network.Finally,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model.The proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： industrial iot deep learning network security intrusion detection system attribute selection smart factory

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An Efficient Intrusion Detection Framework for industrial Internet of Things Security

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Computer Systems Science & Engineering 2023年第7期46卷 819-834页

作者： Samah Alshathri Ayman El-Sayed Walid El-Shafai Ezz El-Din Hemdan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Recently,the Internet of Things(iot)has been used in various applications such as manufacturing,transportation,agriculture,and healthcare that can enhance efficiency and productivity via an intelligent management console remotely.With the increased use of industrial iot(Iiot)applications,the risk of brutal cyber-attacks also increased.This leads researchers worldwide to work on developing effective Intrusion Detection Systems(IDS)for iot infrastructure against any malicious activities.Therefore,this paper provides effective IDS to detect and classify unpredicted and unpredictable severe attacks in contradiction to the iot infrastructure.A comprehensive evaluation examined on a new available benchmark TON_iot dataset is introduced.The data-driven iot/Iiot dataset incorporates a label feature indicating classes of normal and attack-targeting iot/Iiot applications.Correspondingly,this data involves iot/Iiot services-based telemetry data that involves operating systems logs and iot-based traffic networks collected from a realistic medium-scale iot network.This is to classify and recognize the intrusion activity and provide the intrusion detection objectives in iot environments in an efficient fashion.Therefore,several machine learning algorithms such as Logistic Regression(LR),Linear Discriminant Analysis(LDA),K-Nearest Neighbors(KNN),Gaussian Naive Bayes(NB),Classification and Regression Tree(CART),Random Forest(RF),and AdaBoost(AB)are used for the detection intent on thirteen different intrusion datasets.Several performance metrics like accuracy,precision,recall,and F1-score are used to estimate the proposed framework.The experimental results show that the CART surpasses the other algorithms with the highest accuracy values like 0.97,1.00,0.99,0.99,1.00,1.00,and 1.00 for effectively detecting the intrusion activities on the iot/Iiot infrastructure on most of the employed datasets.In addition,the proposed work accomplishes high performance compared to other recent related works in terms of different security and det

关键词： Attacks intrusion detection machine learning deep learning industrial iot TON_iot dataset

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Behavior of Delivery Robot in Human-Robot Collaborative Spaces During Navigation

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Intelligent Automation & Soft Computing 2023年第1期35卷 795-810页

作者： Kiran Jot Singh Divneet Singh Kapoor Mohamed Abouhawwash Jehad F.Al-Amri Shubham Mahajan Amit Kant Pandit Embedded Systems&Robotics Research Group Chandigarh UniversityMohali140413PunjabIndia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Electronics and Communication Shri Mata Vaishno Devi UniversityKatra182320India

Navigation is an essential skill for robots.It becomes a cumbersome task for the robot in a human-populated environment,and Industry 5.0 is an emerging trend that focuses on the interaction between humans and robots.Robot behavior in a social setting is the key to human acceptance while ensuring human comfort and safety.With the advancement in robotics technology,the true use cases of robots in the tourism and hospitality industry are expanding in number.There are very few experimental studies focusing on how people perceive the navigation behavior of a delivery robot.A robotic platform named“PI”has been designed,which incorporates proximity and vision sensors.The robot utilizes a real-time object recognition algorithm based on the You Only Look Once(YOLO)algorithm to detect objects and humans during navigation.This study is aimed towards evaluating human experience,for which we conducted a study among 36 participants to explore the perceived social presence,role,and perception of a delivery robot exhibiting different behavior conditions while navigating in a hotel corridor.The participants’responses were collected and compared for different behavior conditions demonstrated by the robot and results show that humans prefer an assistant role of a robot enabled with audio and visual aids exhibiting social behavior.Further,this study can be useful for developers to gain insight into the expected behavior of a delivery robot.

关键词： Human-robot interaction robot navigation robot behavior collaborative spaces industrial iot industry 5.0

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Enabling industrial Internet of Things(Iiot) towards an emerging smart energy system

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Global Energy Interconnection 2018年第1期1卷 39-47页

作者： Ding Zhang Ching Chuen Chan George You Zhou National Institute of Clean and Low Carbon Energy Department of Electrical and Electronic Engineering The University of Hong Kong

The increasing penetration of renewable energy on the transmission and distribution power network is driving the adoption of two-way power flow control, data and communications needed to meet the dependency of balancing generation and load. Thus, creating an environment where power and information flow seamlessly in real time to enable reliable and economically viable energy delivery, the advent of Internet of Energy(IoE) as well as the rising of Internet of Things(iot) based smart systems.The evolution of IT to Io T has shown that an information network can be connected in an autonomous way via routers from operating system(OS) based computers and devices to build a highly intelligent eco-system. Conceptually, we are applying the same methodology to the Io E concept so that Energy Operating System(EOS) based assets and devices can be developed into a distributed energy network via energy gateway and self-organized into a smart energy eco-system.This paper introduces a laboratory based IIo T driven software and controls platform developed on the NICE Nano-grid as part of a NICE smart system Initiative for Shenhua group. The goal of this effort is to develop an open architecture based industrial Smart Energy Consortium(ISEC) to attract industrial partners, academic universities, module supplies, equipment vendors and related stakeholder to explore and contribute into a test-bed centric open laboratory template and platform for next generation energy-oriented smart industry applications.In the meanwhile, ISEC will play an important role to drive interoperability standards for the mining industry so that the era of un-manned underground mining operation can become the reality as well as increasing safety regulation enforcement.

关键词： Internet of energy industrial iot Fractal

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