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Internet of things intrusion detection model and algorithm based on cloud computing and multi-feature extraction extreme learning machine

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Digital Communications and Networks 2023年第1期9卷 111-124页

作者： Haifeng Lin Qilin Xue Jiayin Feng Di Bai Nanjing Forestry University China Nanjing Agricultural University China Hebei Normal University of Science and Technology China

With the rapid development of the Internet of things(IoT),there are several challenges pertaining to security in IoT applications.Compared with the characteristics of the traditional internet,the IoT has many problems,such as large assets,complex and diverse structures,and lack of computing resources.Traditional network intrusion detection systems cannot meet the security needs of IoT applications.In view of this situation,this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection performance.Usually,traditional intrusion detection algorithms require considerable time for training,and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes;therefore,it is necessary to study intrusion detection algorithms with low weights,short training time,and high detection accuracy for deployment and application on cloud nodes.An appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion threats.This paper discusses the problems related to IoT intrusion prevention in cloud computing environments.Based on the analysis of cloud computing security threats,this study extensively explores IoT intrusion detection,cloud node monitoring,and intrusion response in cloud computing environments by using cloud computing,an improved extreme learning machine,and other methods.We use the Multi-Feature Extraction Extreme Learning Machine(MFE-ELM)algorithm for cloud computing,which adds a multi-feature extraction process to cloud servers,and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud nodes.In our simulation experiments,a classical dataset for intrusion detection is selected as a test,and test steps such as data preprocessing,feature engineering,model training,and result analysis are performed.The experimental

关键词： Internet of things Cloud Computing Intrusion Prevention Intrusion Detection Extreme Learning Machine

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

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Digital Communications and Networks 2023年第1期9卷 101-110页

作者： Abbas Yazdinejad Mostafa Kazemi Reza M.Parizi Ali Dehghantanha Hadis Karimipour Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical Engineering Faculty of EngineeringShahed UniversityTehranIran College of Computing and Software Engineering Kennesaw State UniversityGAUSA School of Engineering University of GuelphOntarioCanada

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.

关键词： Internet of things IIoT Anomaly detection Ensemble deep learning Neural networks LSTM

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

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Digital Communications and Networks 2023年第3期9卷 677-687页

作者： Leonardo B.Furstenau Yan Pablo Reckziegel Rodrigues Michele Kremer Sott Pedro Leivas Michael S.Dohan José Ricardo López-Robles Manuel J.Cobo Nicola Luigi Bragazzi Kim-Kwang Raymond Choo Department of Industrial Engineering Federal University of Rio Grande do SulPorto Alegre 90035-190Brazil University of Santa Cruz do Sul Av.Independencia2293Santa Cruz do SulRSBrazil Business School Unisinos UniversityAv.Dr.Nilo Peçanha 160091330-002Porto AlegreBrazil Lakehead University Thunder BayONP7B 5E1Canada Academic Unit of Accounting and Management Autonomous University of ZacatecasCalle Comercio y Administracion S/NFracc.ProgresoC.P.98066ZacatecasZacatecasMexico Department of Computer Science and Artificial Intelligence University of Granada18071 GranadaSpain York University ONM3J 1P3Canada Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

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.

关键词： Internet of things Strategic intelligence Industry 4.0 SciMAT Bibliometric analysis Science mapping

Time to forge ahead:The Internet of things for healthcare

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Digital Communications and Networks 2023年第1期9卷 223-235页

作者： Denzil Furtado Andre F.Gygax Chien Aun Chan Ashley I.Bush Melbourne Dementia Research Centre&Department of Chemical Engineering University of MelbourneVIC3010Australia Faculty of Business and Economics University of MelbourneVIC3010Australia Department of Electrical and Electronic Engineering University of MelbourneVIC3010Australia Florey Institute of Neuroscience and Mental Health University of MelbourneVIC3010Australia

Situated at the intersection of technology and medicine,the Internet of things(IoT)holds the promise of addressing some of healthcare's most pressing challenges,from medical error,to chronic drug shortages,to overburdened hospital systems,to dealing with the COVID-19 pandemic.However,despite considerable recent technological advances,the pace of successful implementation of promising IoT healthcare initiatives has been slow.To inspire more productive collaboration,we present here a simple—but surprisingly underrated—problemoriented approach to developing healthcare technologies.To further assist in this effort,we reviewed the various commercial,regulatory,social/cultural,and technological factors in the development of the IoT.We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem.To this end,we explore the key enabling technologies that underpin the fog architecture,from the sensing layer all the way up to the cloud.It is our hope that ongoing advances in sensing,communications,cryptography,storage,machine learning,and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.

关键词： Internet of things Healthcare Information Fog computing Artificial intelligence Machine learning Big data COVID-19 pandemic

Satellite Internet of things:challenges,solutions,and development trends

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Frontiers of Information Technology & Electronic Engineering 2023年第7期24卷 935-944页

作者： Xiaoming CHEN Zhaobin XU Lin SHANG College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China Institute of Advanced Technology Zhejiang UniversityHangzhou 310027China Innovation Academy for Microsatellites of Chinese Academy of Sciences Shanghai 201210China Shanghai Spacecom Satellite Technology Ltd. Shanghai 201612China

Satellite Internet of things(IoT)is a promising way to provide seamless coverage to a massive number of devices all over the world,especially in remote areas not covered by cellular networks,e.g.,forests,oceans,mountains,and deserts.In general,satellite IoT networks take low Earth orbit(LEO)satellites as access points,which solves the problem of wide coverage,but leads to many challenging issues.We first give an overview of satellite IoT,with an emphasis on revealing the characteristics of IoT services.Then,the challenging issues of satellite IoT,i.e.,massive connectivity,wide coverage,high mobility,low power,and stringent delay,are analyzed in detail.Furthermore,the possible solutions to these challenges are provided.In particular,new massive access protocols and techniques are designed according to the characteristics and requirements of satellite IoT.Finally,we discuss several development trends of satellite IoT to stimulate and encourage further research in such a broad area.

关键词： Internet of things Satellite communications Low Earth orbit(LEO) Massive connectivity Random access

Collaborative image compression and classification with multi-task learning for visual Internet of things

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Chinese Journal of Aeronautics 2022年第5期35卷 390-399页

作者： Bing DU Yiping DUAN Hang ZHANG Xiaoming TAO Yue WU Congchong RU School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China School of Computer Science and Technology Xidian UniversityXian 710071China Smart Network Computing Lab Cross-strait Tsinghua Research InstitutionBeijing 100084China

Widespread deployment of the Internet of things(Io T)has changed the way that network services are developed,deployed,and operated.Most onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io T.Typically,the sender would compress images,and then through the communication network,the receiver would decode images,and then analyze the images for applications.However,image compression and semantic inference are generally conducted separately,and thus,current compression algorithms cannot be transplanted for the use of semantic inference directly.A collaborative image compression and classification framework for visual Io T applications is proposed,which combines image compression with semantic inference by using multi-task learning.In particular,the multi-task Generative Adversarial Networks(GANs)are described,which include encoder,quantizer,generator,discriminator,and classifier to conduct simultaneously image compression and classification.The key to the proposed framework is the quantized latent representation used for compression and classification.GANs with perceptual quality can achieve low bitrate compression and reduce the amount of data transmitted.In addition,the design in which two tasks share the same feature can greatly reduce computing resources,which is especially applicable for environments with extremely limited resources.Using extensive experiments,the collaborative compression and classification framework is effective and useful for visual IoT applications.

关键词： Deep learning Generative Adversarial Network(GAN) Image classification Image compression Internet of things

Research on key technologies of UAV cluster cooperative system for Internet of things applications

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Journal of Control and Decision 2024年第1期11卷 26-35页

作者： Xu Jing Xi’an International University Xi’anPeople’s Republic of China

The combat tasks faced by UAVs are becoming more and more complex.Traditional single UAVs are limited by the constraints of platform load capacity,lightweight load,and insufficient lowpower equipment,so it is difficult to complete complex tasks independently.Aiming at typical UAV collaborative confrontation application scenarios,this paper constructs a multi-agentoriented cluster collaborative intelligent system architecture and establishes a swarm-oriented intelligent UAV cluster collaborative control algorithm.Moreover,this paper forms a simulation environment for UAV cluster collaborative confrontation and completes the design and implementation of the UAV cluster collaborative confrontation system based on swarm intelligence.In addition,this paper analyses the key technologies of the UAV cluster collaborative system with the support of the Internet of things technology and verifies the performance of the system after constructing the corresponding system.The experimental results show that the system constructed in this paper is effective.

关键词： Internet of things UAV cluster collaborative control key technology

ResNeSt-biGRU: An Intrusion DetectionModel Based on Internet of things

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Computers, Materials & Continua 2024年第4期79卷 1005-1023页

作者： Yan Xiang Daofeng Li XinyiMeng Chengfeng Dong Guanglin Qin School of Computer and Electronics Information Guangxi UniversityNanning530004China Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing Guangxi UniversityNanning530004China

The rapid expansion of Internet of things (IoT) devices across various sectors is driven by steadily increasingdemands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device hascaught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. Thishas resulted in a myriad of security challenges, including information leakage, malware propagation, and financialloss, among others. Consequently, developing an intrusion detection system to identify both active and potentialintrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practicalintrusion detection model that combines the strengths of ResNeSt, a variant of Residual Neural Network, andbidirectionalGated RecurrentUnitNetwork (biGRU).Our ResNeSt-biGRUframework diverges fromconventionalintrusion detection systems (IDS) by employing this dual-layeredmechanism that exploits the temporal continuityand spatial feature within network data streams, a methodological innovation that enhances detection accuracy.In conjunction with this, we introduce the PreIoT dataset, a compilation of prevalent IoT network behaviors, totrain and evaluate IDSmodels with a focus on identifying potential intrusion traffics. The effectiveness of proposedscheme is demonstrated through testing, wherein it achieved an average accuracy of 99.90% on theN-BaIoT datasetas well as on the PreIoT dataset and 94.45% on UNSW-NB15 dataset. The outcomes of this research reveal thepotential of ResNeSt-biGRU to bolster security measures, diminish intrusion-related vulnerabilities, and preservethe overall security of IoT ecosystems.

关键词： Internet of things cyberattack intrusion detection internet security

A distributed pavement monitoring system based on Internet of things

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Journal of Traffic and Transportation Engineering(English Edition) 2022年第2期9卷 305-317页

作者： Zhoujing Ye Ya Wei Jianfeng Li Guannan Yan Linbing Wang National Center for Materials Service Safety University of Science and Technology BeijingBeijing 100083China Department of Civil Engineering Tsinghua UniversityBeijing 100084China Center for Smart and Green Civil Systems Virginia TechBlacksburgVA 24061USA Joint USTB-Virginia Tech Lab on Multifunctional Materials University of Science and Technology BeijingBeijing 100083China Virginia Tech BlacksburgVA 24061USA

Pavement is an important part of transportation infrastructure.In order to maintain pavement before the damage and improve the service quality,it is necessary to develop an intelligent and durable pavement information monitoring system.However,the pavement dynamic response monitoring is highly costly,easily obsolete and statistically redundant.The emergence of the Internet of things(IoT)technology promises to change that.In this paper,an architecture of a distributed road IoT monitoring system is proposed,which has an acquisition layer,a preprocessing layer,a processing layer,an interaction layer,an energy layer and a network layer.Then,a prototype wireless pavement vibration monitoring system based on the IoT is developed,which consists of a number of wireless sensing nodes,a gateway,a remote server and a browser.Finally,data preprocessing,wireless communication,time synchronization,data processing and visualization,which represent the key to an effective system,are tested and discussed.The prototype wireless pavement vibration monitoring system provides a viable scheme for upgrading the IoT system and its application in the road infrastructures.In the future,any smart road will have an IoT wireless monitoring system to monitor the traffic,environment,and pavement information,which help enable traffic guidance,signal control,danger warning,scientific maintenance decision-making.

关键词： Monitoring system Internet of things Pavement vibration Wireless communication Traffic information