The rapid growth and pervasive presence of the Internet of Things(IoT)have led to an unparalleled increase in IoT devices,thereby intensifying worries over IoT security.Deep learning(DL)-based intrusion detection(ID)h...The rapid growth and pervasive presence of the Internet of Things(IoT)have led to an unparalleled increase in IoT devices,thereby intensifying worries over IoT security.Deep learning(DL)-based intrusion detection(ID)has emerged as a vital method for protecting IoT environments.To rectify the deficiencies of current detection methodologies,we proposed and developed an IoT cyberattacks detection system(IoT-CDS)based on DL models for detecting bot attacks in IoT networks.The DL models—long short-term memory(LSTM),gated recurrent units(GRUs),and convolutional neural network-LSTM(CNN-LSTM)were suggested to detect and classify IoT attacks.The BoT-IoT dataset was used to examine the proposed IoT-CDS system,and the dataset includes six attacks with normal packets.The experiments conducted on the BoT-IoT network dataset reveal that the LSTM model attained an impressive accuracy rate of 99.99%.Compared with other internal and external methods using the same dataset,it is observed that the LSTM model achieved higher accuracy rates.LSTMs are more efficient than GRUs and CNN-LSTMs in real-time performance and resource efficiency for cyberattack detection.This method,without feature selection,demonstrates advantages in training time and detection accuracy.Consequently,the proposed approach can be extended to improve the security of various IoT applications,representing a significant contribution to IoT security.展开更多
Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities f...Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented knowledge.To ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and availability.Among these approaches,Intrusion Detection Systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of threats.This study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification algorithm.By combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of attack.Our analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT environments.Experimental results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack detection.Despite the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of environments.Future work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.展开更多
With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices ge...With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.展开更多
文章提出了一种面向5G频段和窄带物联网(narrow band internet of things,NB-IoT)的电力物联网频率可重构天线。该天线主要由两个“L”型的辐射枝节、带缺陷地结构的接地面以及用于馈电的微带线组成,辐射枝节和微带线之间分别用PIN二极...文章提出了一种面向5G频段和窄带物联网(narrow band internet of things,NB-IoT)的电力物联网频率可重构天线。该天线主要由两个“L”型的辐射枝节、带缺陷地结构的接地面以及用于馈电的微带线组成,辐射枝节和微带线之间分别用PIN二极管连接。通过调节PIN二极管来改变该天线的表面电流分布,从而实现了良好的可重构性,对改变缺陷地结构来增大电流的路径,使天线获得更宽的带宽。结果表明,当天线的尺寸大小为0.43λ0×0.49λ0,工作在NB-IoT频段(1.85GHz~1.9.0GHz)和5G通信频段(4.4 GHz~5.0 GHz),最大增益分别可为4.5 dBi和5.9 dBi。在实际应用中,该天线可用于认知无线电技术,即在5G通信频段工作状态下探测空闲频谱,并及时切换到NB-IoT工作状态下,从而调整到空闲频率工作,充分满足电网深度监测与智能发电、输电等业务需求。展开更多
The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by...The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.展开更多
Rapid urbanization has been happening around the world,leading to many challenges and difficulties in infrastructure,communication network,transportation,environmental and organizational problems.Proper and responsibl...Rapid urbanization has been happening around the world,leading to many challenges and difficulties in infrastructure,communication network,transportation,environmental and organizational problems.Proper and responsible management of urban resources plays a significant role in sustainable development.Smart sustainable cities use ICTs(Information and Communication Technologies)to improve quality of life,efficiency of urban operation and services.The latest advancement in communication,technology,data management,and IoT(Internet of Things)provide a tremendous role for practical implementations and adoption of devices and entities.Smart sustainable cities can be intellectualized as an innovative approach of controlling urban resources and valuable components based on the latest advancement in ICT.Our study focuses on reviewing and discussing the literature that states the vital components of IoT associated with smart sustainable cities in general and specifically with green energy.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
火灾事故对人民生命财产安全构成了极大的威胁。消防水源作为灭火时的首选,在救援中发挥着重要作用。针对消防供水设施人工管理效率低、工作状态难以实时知晓和人为损坏频繁等问题,设计了一种基于窄带物联网(Narrow Band Internet of Th...火灾事故对人民生命财产安全构成了极大的威胁。消防水源作为灭火时的首选,在救援中发挥着重要作用。针对消防供水设施人工管理效率低、工作状态难以实时知晓和人为损坏频繁等问题,设计了一种基于窄带物联网(Narrow Band Internet of Things, NB-IoT)的消防水源数字化感知系统。系统终端采用自主设计,实现消防水源关键信息的实时采集,通过NB-IoT无线通信技术将数据上传至云平台。云平台实现数据预处理、流转与存储,并通过配套的Web端和APP将关键信息进行可视化展示。消防管理人员可远程查看消防水源状态以及巡检记录的信息,出现异常时自动报警推送。利用组合模型对监测终端采集的非用水时水压进行学习,通过设置阈值与时间窗口实现消防用水行为的识别,对消防水系统的维护进行指导。经测试,系统运行稳定、使用便捷,具有良好的实用价值。展开更多
The agricultural Internet of Things(IoT)system is a critical component of modern smart agriculture,and its security risk assessment methods have garnered increasing attention from the industry.Current agricultural IoT...The agricultural Internet of Things(IoT)system is a critical component of modern smart agriculture,and its security risk assessment methods have garnered increasing attention from the industry.Current agricultural IoT security risk assessment methods primarily rely on expert judgment,introducing subjective factors that reduce the credibility of the assessment results.To address this issue,this study constructed a dataset for agricultural IoT security risk assessment based on real-world security reports.A PCARF algorithm,built on random forest principles,was proposed,incorporating ensemble learning strategies to enhance prediction accuracy.Compared to the second-best model,the proposed model demonstrated a 2.7%increase in accuracy,a 3.4%improvement in recall rate,a 3.1%rise in Area Under the Curve(AUC),and a 7.9%boost in Matthews Correlation Coefficient(MCC).Extensive comparative experiments showed that the proposed model outperforms others in prediction accuracy and robustness.展开更多
文摘The rapid growth and pervasive presence of the Internet of Things(IoT)have led to an unparalleled increase in IoT devices,thereby intensifying worries over IoT security.Deep learning(DL)-based intrusion detection(ID)has emerged as a vital method for protecting IoT environments.To rectify the deficiencies of current detection methodologies,we proposed and developed an IoT cyberattacks detection system(IoT-CDS)based on DL models for detecting bot attacks in IoT networks.The DL models—long short-term memory(LSTM),gated recurrent units(GRUs),and convolutional neural network-LSTM(CNN-LSTM)were suggested to detect and classify IoT attacks.The BoT-IoT dataset was used to examine the proposed IoT-CDS system,and the dataset includes six attacks with normal packets.The experiments conducted on the BoT-IoT network dataset reveal that the LSTM model attained an impressive accuracy rate of 99.99%.Compared with other internal and external methods using the same dataset,it is observed that the LSTM model achieved higher accuracy rates.LSTMs are more efficient than GRUs and CNN-LSTMs in real-time performance and resource efficiency for cyberattack detection.This method,without feature selection,demonstrates advantages in training time and detection accuracy.Consequently,the proposed approach can be extended to improve the security of various IoT applications,representing a significant contribution to IoT security.
文摘Internet of Things(IoT)refers to the infrastructures that connect smart devices to the Internet,operating autonomously.This connectivitymakes it possible to harvest vast quantities of data,creating new opportunities for the emergence of unprecedented knowledge.To ensure IoT securit,various approaches have been implemented,such as authentication,encoding,as well as devices to guarantee data integrity and availability.Among these approaches,Intrusion Detection Systems(IDS)is an actual security solution,whose performance can be enhanced by integrating various algorithms,including Machine Learning(ML)and Deep Learning(DL),enabling proactive and accurate detection of threats.This study proposes to optimize the performance of network IDS using an ensemble learning method based on a voting classification algorithm.By combining the strengths of three powerful algorithms,Random Forest(RF),K-Nearest Neighbors(KNN),and Support Vector Machine(SVM)to detect both normal behavior and different categories of attack.Our analysis focuses primarily on the NSL-KDD dataset,while also integrating the recent Edge-IIoT dataset,tailored to industrial IoT environments.Experimental results show significant enhancements on the Edge-IIoT and NSL-KDD datasets,reaching accuracy levels between 72%to 99%,with precision between 87%and 99%,while recall values and F1-scores are also between 72%and 99%,for both normal and attack detection.Despite the promising results of this study,it suffers from certain limitations,notably the use of specific datasets and the lack of evaluations in a variety of environments.Future work could include applying this model to various datasets and evaluating more advanced ensemble strategies,with the aim of further enhancing the effectiveness of IDS.
基金supported by the Shandong Province Science and Technology Project(2023TSGC0509,2022TSGC2234)Qingdao Science and Technology Plan Project(23-1-5-yqpy-2-qy)Open Topic Grants of Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving,Anhui Jianzhu University(IBES2024KF08).
文摘With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.
文摘文章提出了一种面向5G频段和窄带物联网(narrow band internet of things,NB-IoT)的电力物联网频率可重构天线。该天线主要由两个“L”型的辐射枝节、带缺陷地结构的接地面以及用于馈电的微带线组成,辐射枝节和微带线之间分别用PIN二极管连接。通过调节PIN二极管来改变该天线的表面电流分布,从而实现了良好的可重构性,对改变缺陷地结构来增大电流的路径,使天线获得更宽的带宽。结果表明,当天线的尺寸大小为0.43λ0×0.49λ0,工作在NB-IoT频段(1.85GHz~1.9.0GHz)和5G通信频段(4.4 GHz~5.0 GHz),最大增益分别可为4.5 dBi和5.9 dBi。在实际应用中,该天线可用于认知无线电技术,即在5G通信频段工作状态下探测空闲频谱,并及时切换到NB-IoT工作状态下,从而调整到空闲频率工作,充分满足电网深度监测与智能发电、输电等业务需求。
基金described in this paper has been developed with in the project PRESECREL(PID2021-124502OB-C43)。
文摘The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control systems.With the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are essential.Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns.This paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT systems.However,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss functions.To address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN hyper-parameters.The 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local minima.The proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model training.The generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded features.We evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and IoT-23.Experiments conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false positives.The 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence trend.The proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and IoT-23 datasets,with values of 0.24,1.10,and 0.09,respectively.Additionally,it attained the highest accuracy,ranging from 94%to 100%.These results suggest a promising direction for future IoT security frameworks,offering a scalable and efficient solution to safeguard against evolving cyber threats.
文摘Rapid urbanization has been happening around the world,leading to many challenges and difficulties in infrastructure,communication network,transportation,environmental and organizational problems.Proper and responsible management of urban resources plays a significant role in sustainable development.Smart sustainable cities use ICTs(Information and Communication Technologies)to improve quality of life,efficiency of urban operation and services.The latest advancement in communication,technology,data management,and IoT(Internet of Things)provide a tremendous role for practical implementations and adoption of devices and entities.Smart sustainable cities can be intellectualized as an innovative approach of controlling urban resources and valuable components based on the latest advancement in ICT.Our study focuses on reviewing and discussing the literature that states the vital components of IoT associated with smart sustainable cities in general and specifically with green energy.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
文摘火灾事故对人民生命财产安全构成了极大的威胁。消防水源作为灭火时的首选,在救援中发挥着重要作用。针对消防供水设施人工管理效率低、工作状态难以实时知晓和人为损坏频繁等问题,设计了一种基于窄带物联网(Narrow Band Internet of Things, NB-IoT)的消防水源数字化感知系统。系统终端采用自主设计,实现消防水源关键信息的实时采集,通过NB-IoT无线通信技术将数据上传至云平台。云平台实现数据预处理、流转与存储,并通过配套的Web端和APP将关键信息进行可视化展示。消防管理人员可远程查看消防水源状态以及巡检记录的信息,出现异常时自动报警推送。利用组合模型对监测终端采集的非用水时水压进行学习,通过设置阈值与时间窗口实现消防用水行为的识别,对消防水系统的维护进行指导。经测试,系统运行稳定、使用便捷,具有良好的实用价值。
文摘The agricultural Internet of Things(IoT)system is a critical component of modern smart agriculture,and its security risk assessment methods have garnered increasing attention from the industry.Current agricultural IoT security risk assessment methods primarily rely on expert judgment,introducing subjective factors that reduce the credibility of the assessment results.To address this issue,this study constructed a dataset for agricultural IoT security risk assessment based on real-world security reports.A PCARF algorithm,built on random forest principles,was proposed,incorporating ensemble learning strategies to enhance prediction accuracy.Compared to the second-best model,the proposed model demonstrated a 2.7%increase in accuracy,a 3.4%improvement in recall rate,a 3.1%rise in Area Under the Curve(AUC),and a 7.9%boost in Matthews Correlation Coefficient(MCC).Extensive comparative experiments showed that the proposed model outperforms others in prediction accuracy and robustness.
