5G provides a unified authentication architecture and access management for IoT(Internet of Things)devices.But existing authentication services cannot cover massive IoT devices with various computing capabilities.In a...5G provides a unified authentication architecture and access management for IoT(Internet of Things)devices.But existing authentication services cannot cover massive IoT devices with various computing capabilities.In addition,with the development of quantum computing,authentication schemes based on traditional digital signature technology may not be as secure as we expected.This paper studies the authentication mechanism from the user equipment to the external data network in 5G and proposed an authentication protocol prototype that conforms to the Third Generation Partnership Program(3GPP)standard.This prototype can accommodate various Hash-based signature technologies,applying their advantages in resource consumption to meet the authentication requirements of multiple types of IoT devices.The operation of the proposed authentication scheme is mainly based on the Hash function,which is more efficient than the traditional authentication scheme.It provides flexible and high-quality authentication services for IoT devices cluster in the 5G environment combining the advantages of Hash-based signature technology and 5G architecture.展开更多
The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability t...The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks.Further,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT systems.The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis dataset.Moreover,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and recall.The study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT environments.Furthermore,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested methodology.This research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT devices.However,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world datasets.Utilizing WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT ecosystems.To summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning methods.The research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance analysis.Future research could focus on enhancing the recommended approach by adding more datasets and leveraging real-time monitoring capabilities to strengthen IoT devices’defenses against new cybersecurity threats.展开更多
The Internet of Things(IoT)has become an integral part of various industries,from smart cities to healthcare,driving the need for energy-efficient and stable devices,especially in complex and unpredictable environment...The Internet of Things(IoT)has become an integral part of various industries,from smart cities to healthcare,driving the need for energy-efficient and stable devices,especially in complex and unpredictable environments.This research investigates the optimization of energy consumption and the enhancement of stability in IoT devices operating in such environments.The study addresses key challenges,including resource constraints,fluctuating environmental conditions,and the increasing complexity of IoT networks.It explores various energy optimization techniques,such as low-power communication protocols,edge and cloud computing,and machine learning models,that help reduce energy usage while maintaining performance.Furthermore,it examines stability enhancement strategies,including fault-tolerant mechanisms,resilient network architectures,and real-time monitoring and adaptive control,that ensure the continuous and reliable operation of IoT devices despite external disruptions.The findings of this research contribute to the development of next-generation IoT systems that are both energy-efficient and resilient,thereby promoting sustainable deployment in real-world applications.展开更多
Robust encryption techniques require heavy computational capability and consume large amount of memory which are unaffordable for resource constrained IoT devices and Cyber-Physical Systems with an inclusion of genera...Robust encryption techniques require heavy computational capability and consume large amount of memory which are unaffordable for resource constrained IoT devices and Cyber-Physical Systems with an inclusion of general-purpose data manipulation tasks.Many encryption techniques have been introduced to address the inability of such devices,lacking in robust security provision at low cost.This article presents an encryption technique,implemented on a resource constrained IoT device(AVR ATmega2560)through utilizing fast execution and less memory consumption properties of curve25519 in a novel and efficient lightweight hash function.The hash function utilizes GMP library for multi-precision arithmetic calculations and pre-calculated curve points to devise a good cipher block using ECDH based key exchange protocols and large random prime number generator function.展开更多
The Internet of Things(IoT)is emerging as an innovative phenomenon concerned with the development of numerous vital applications.With the development of IoT devices,huge amounts of information,including users’private...The Internet of Things(IoT)is emerging as an innovative phenomenon concerned with the development of numerous vital applications.With the development of IoT devices,huge amounts of information,including users’private data,are generated.IoT systems face major security and data privacy challenges owing to their integral features such as scalability,resource constraints,and heterogeneity.These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data,creating an attractive opportunity for cyberattacks.To address these challenges,artificial intelligence(AI)techniques,such as machine learning(ML)and deep learning(DL),are utilized to build an intrusion detection system(IDS)that helps to secure IoT systems.Federated learning(FL)is a decentralized technique that can help to improve information privacy and performance by training the IDS on discrete linked devices.FL delivers an effectual tool to defend user confidentiality,mainly in the field of IoT,where IoT devices often obtain privacy-sensitive personal data.This study develops a Privacy-Enhanced Federated Learning for Intrusion Detection using the Chameleon Swarm Algorithm and Artificial Intelligence(PEFLID-CSAAI)technique.The main aim of the PEFLID-CSAAI method is to recognize the existence of attack behavior in IoT networks.First,the PEFLIDCSAAI technique involves data preprocessing using Z-score normalization to transformthe input data into a beneficial format.Then,the PEFLID-CSAAI method uses the Osprey Optimization Algorithm(OOA)for the feature selection(FS)model.For the classification of intrusion detection attacks,the Self-Attentive Variational Autoencoder(SA-VAE)technique can be exploited.Finally,the Chameleon Swarm Algorithm(CSA)is applied for the hyperparameter finetuning process that is involved in the SA-VAE model.A wide range of experiments were conducted to validate the execution of the PEFLID-CSAAI model.The simulated outcomes demonstrated that the PEFLID-CSAAI technique outperformed other recent models,highlighting its potential as a valuable tool for future applications in healthcare devices and small engineering systems.展开更多
Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security,reliability,and efficiency.The ...Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security,reliability,and efficiency.The healthcare sector becomes advantageous and offers different ways to manage patient information in order to improve healthcare service quality.The futuristic sustainable computing solutions in e-healthcare applications depend upon Internet of Things(IoT)in cloud computing environment.The energy consumed during data communication from IoT devices to cloud server is significantly high and it needs to be reduced with the help of clustering techniques.The current research article presents a new Oppositional Glowworm Swarm Optimization(OGSO)algorithmbased clustering with Deep Neural Network(DNN)called OGSO-DNN model for distributed healthcare systems.The OGSO algorithm was applied in this study to select the Cluster Heads(CHs)from the available IoT devices.The selected CHs transmit the data to cloud server,which then executes DNN-based classification process for healthcare diagnosis.An extensive simulation analysis was carried out utilizing a student perspective healthcare data generated from UCI repository and IoT devices to forecast the severity level of the disease among students.The proposed OGSO-DNN model outperformed previous methods by attaining the maximum average sensitivity of 96.956%,specificity of 95.076%,the accuracy of 95.764%and F-score value of 96.888%.展开更多
Purpose This study examines the impact of 26-week exercise intervention facilitated by IoT devices on cognitive function,hippocampal volume,and health indicators in a real-world setting based on the Transtheoretical M...Purpose This study examines the impact of 26-week exercise intervention facilitated by IoT devices on cognitive function,hippocampal volume,and health indicators in a real-world setting based on the Transtheoretical Model.Methods Middle-aged participants(n=121,mean age=49.8±7.62 years)were randomly assigned to BrainUp(n=61)or Sham(n=60)application groups.Both groups engaged in voluntary aerobic exercise over a period of 26 weeks using an IoT device.The BrainUp application was designed to deliver personalized exercise routines aimed at enhancing participants’cognitive function and hippocampal volume based on their individual level.Results Linear mixed models revealed that both groups exhibited improvements in cognitive function and health indicators at post-intervention compared to pre-intervention,but no significant changes in hippocampal volume in either group over time.Path analysis and structural equation modeling indicated that motivation for exercise at 26 weeks played a key role in improving cognitive function and blood glucose,and alleviating depression.Conclusions Integrating physical activity with IoT devices in real-world settings shows promise for enhancing brain health and preventing hippocampal volume loss due to aging.Motivation might play a crucial role in maximizing the health benefits of physical activity,especially during the transition from the Action to Maintenance stages of behavioral changes to an active lifestyle.展开更多
In the modern world,women now have tremendous success in every field.They can play,learn,and earn as much as men.But what about safety?Do they have the same secure environment that men and boys do?The answer is“NO”....In the modern world,women now have tremendous success in every field.They can play,learn,and earn as much as men.But what about safety?Do they have the same secure environment that men and boys do?The answer is“NO”.Women and girls have been subjected to numerous incidents,including acid throwing,rape,kidnapping,and harassment.It is common to read a lot of news like this in newspapers every day.These incidents make women feel unsafe in this society.Our freedom came a long time ago,but women still lack complete security in this society.All women cannot fight or shout all the time when some danger is happening to them.What can the physically challenged person and Children do?To make women feel safe,we designed“Wrist Band”using IoT for women safety.As the sensors sense information from the body,it will always update the information such as pulse,temperature,and vibration to the well-wishers through the Blynk app.展开更多
Every day,more and more data is being produced by the Internet of Things(IoT)applications.IoT data differ in amount,diversity,veracity,and velocity.Because of latency,various types of data handling in cloud computing ...Every day,more and more data is being produced by the Internet of Things(IoT)applications.IoT data differ in amount,diversity,veracity,and velocity.Because of latency,various types of data handling in cloud computing are not suitable for many time-sensitive applications.When users move from one site to another,mobility also adds to the latency.By placing computing close to IoT devices with mobility support,fog computing addresses these problems.An efficient Load Balancing Algorithm(LBA)improves user experience and Quality of Service(QoS).Classification of Request(CoR)based Resource Adaptive LBA is suggested in this research.This technique clusters fog nodes using an efficient K-means clustering algorithm and then uses a Decision Tree approach to categorize the request.The decision-making process for time-sensitive and delay-tolerable requests is facilitated by the classification of requests.LBA does the operation based on these classifications.The MobFogSim simulation program is utilized to assess how well the algorithm with mobility features performs.The outcome demonstrates that the LBA algorithm’s performance enhances the total system performance,which was attained by(90.8%).Using LBA,several metrics may be examined,including Response Time(RT),delay(d),Energy Consumption(EC),and latency.Through the on-demand provisioning of necessary resources to IoT users,our suggested LBA assures effective resource usage.展开更多
At present,the network security situation is becoming more and more serious.Malicious network attacks such as computer viruses,Trojans and hacker attacks are becoming more and more rampant.National and group network a...At present,the network security situation is becoming more and more serious.Malicious network attacks such as computer viruses,Trojans and hacker attacks are becoming more and more rampant.National and group network attacks such as network information war and network terrorism have a serious damage to the production and life of the whole society.At the same time,with the rapid development of Internet of Things and the arrival of 5G era,IoT devices as an important part of industrial Internet system,have become an important target of infiltration attacks by hostile forces.This paper describes the challenges facing firmware vulnerability detection at this stage,and introduces four automatic detection and utilization technologies in detail:based on patch comparison,based on control flow,based on data flow and ROP attack against buffer vulnerabilities.On the basis of clarifying its core idea,main steps and experimental results,the limitations of its method are proposed.Finally,combined with four automatic detection methods,this paper summarizes the known vulnerability detection steps based on firmware analysis,and looks forward to the follow-up work.展开更多
The concept of Internet of Things（IoT）was first proposed by MIT Prof.Kevin Ash-ton in 1999.The implementation of IoT was mainly through RFID in its early time.With advanced technology and manufacture,diverse impleme...The concept of Internet of Things（IoT）was first proposed by MIT Prof.Kevin Ash-ton in 1999.The implementation of IoT was mainly through RFID in its early time.With advanced technology and manufacture,diverse implementation forms of IoT are becoming possible.Wearable devices,as an essential branch of IoT,will have broad application prospects in health monitoring and intelligent healthcare.展开更多
The emergence of the Internet of Things(IoT)has triggered a massive digital transformation across numerous sectors.This transformation requires efficient wireless communication and connectivity,which depend on the opt...The emergence of the Internet of Things(IoT)has triggered a massive digital transformation across numerous sectors.This transformation requires efficient wireless communication and connectivity,which depend on the optimal utilization of the available spectrum resource.Given the limited availability of spectrum resources,spectrum sharing has emerged as a favored solution to empower IoT deployment and connectivity,so adequate planning of the spectrum resource utilization is thus essential to pave the way for the next generation of IoT applications,including 5G and beyond.This article presents a comprehensive study of prevalent wireless technologies employed in the field of the spectrum,with a primary focus on spectrum-sharing solutions,including shared spectrum.It highlights the associated security and privacy concerns when the IoT devices access the shared spectrum.This survey examines the benefits and drawbacks of various spectrum-sharing technologies and their solutions for various IoT applications.Lastly,it identifies future IoT obstacles and suggests potential research directions to address them.展开更多
1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves ...1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves bandwidth.However,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency bottlenecks.It is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.展开更多
文摘5G provides a unified authentication architecture and access management for IoT(Internet of Things)devices.But existing authentication services cannot cover massive IoT devices with various computing capabilities.In addition,with the development of quantum computing,authentication schemes based on traditional digital signature technology may not be as secure as we expected.This paper studies the authentication mechanism from the user equipment to the external data network in 5G and proposed an authentication protocol prototype that conforms to the Third Generation Partnership Program(3GPP)standard.This prototype can accommodate various Hash-based signature technologies,applying their advantages in resource consumption to meet the authentication requirements of multiple types of IoT devices.The operation of the proposed authentication scheme is mainly based on the Hash function,which is more efficient than the traditional authentication scheme.It provides flexible and high-quality authentication services for IoT devices cluster in the 5G environment combining the advantages of Hash-based signature technology and 5G architecture.
基金The authors would like to thank Princess Nourah bint Abdulrahman University for funding this project through the researchers supporting project(PNURSP2024R435)and this research was funded by the Prince Sultan University,Riyadh,Saudi Arabia.
文摘The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks.Further,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT systems.The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis dataset.Moreover,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and recall.The study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT environments.Furthermore,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested methodology.This research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT devices.However,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world datasets.Utilizing WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT ecosystems.To summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning methods.The research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance analysis.Future research could focus on enhancing the recommended approach by adding more datasets and leveraging real-time monitoring capabilities to strengthen IoT devices’defenses against new cybersecurity threats.
文摘The Internet of Things(IoT)has become an integral part of various industries,from smart cities to healthcare,driving the need for energy-efficient and stable devices,especially in complex and unpredictable environments.This research investigates the optimization of energy consumption and the enhancement of stability in IoT devices operating in such environments.The study addresses key challenges,including resource constraints,fluctuating environmental conditions,and the increasing complexity of IoT networks.It explores various energy optimization techniques,such as low-power communication protocols,edge and cloud computing,and machine learning models,that help reduce energy usage while maintaining performance.Furthermore,it examines stability enhancement strategies,including fault-tolerant mechanisms,resilient network architectures,and real-time monitoring and adaptive control,that ensure the continuous and reliable operation of IoT devices despite external disruptions.The findings of this research contribute to the development of next-generation IoT systems that are both energy-efficient and resilient,thereby promoting sustainable deployment in real-world applications.
文摘Robust encryption techniques require heavy computational capability and consume large amount of memory which are unaffordable for resource constrained IoT devices and Cyber-Physical Systems with an inclusion of general-purpose data manipulation tasks.Many encryption techniques have been introduced to address the inability of such devices,lacking in robust security provision at low cost.This article presents an encryption technique,implemented on a resource constrained IoT device(AVR ATmega2560)through utilizing fast execution and less memory consumption properties of curve25519 in a novel and efficient lightweight hash function.The hash function utilizes GMP library for multi-precision arithmetic calculations and pre-calculated curve points to devise a good cipher block using ECDH based key exchange protocols and large random prime number generator function.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,under grant number NBU-FFR-2025-451-6.
文摘The Internet of Things(IoT)is emerging as an innovative phenomenon concerned with the development of numerous vital applications.With the development of IoT devices,huge amounts of information,including users’private data,are generated.IoT systems face major security and data privacy challenges owing to their integral features such as scalability,resource constraints,and heterogeneity.These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data,creating an attractive opportunity for cyberattacks.To address these challenges,artificial intelligence(AI)techniques,such as machine learning(ML)and deep learning(DL),are utilized to build an intrusion detection system(IDS)that helps to secure IoT systems.Federated learning(FL)is a decentralized technique that can help to improve information privacy and performance by training the IDS on discrete linked devices.FL delivers an effectual tool to defend user confidentiality,mainly in the field of IoT,where IoT devices often obtain privacy-sensitive personal data.This study develops a Privacy-Enhanced Federated Learning for Intrusion Detection using the Chameleon Swarm Algorithm and Artificial Intelligence(PEFLID-CSAAI)technique.The main aim of the PEFLID-CSAAI method is to recognize the existence of attack behavior in IoT networks.First,the PEFLIDCSAAI technique involves data preprocessing using Z-score normalization to transformthe input data into a beneficial format.Then,the PEFLID-CSAAI method uses the Osprey Optimization Algorithm(OOA)for the feature selection(FS)model.For the classification of intrusion detection attacks,the Self-Attentive Variational Autoencoder(SA-VAE)technique can be exploited.Finally,the Chameleon Swarm Algorithm(CSA)is applied for the hyperparameter finetuning process that is involved in the SA-VAE model.A wide range of experiments were conducted to validate the execution of the PEFLID-CSAAI model.The simulated outcomes demonstrated that the PEFLID-CSAAI technique outperformed other recent models,highlighting its potential as a valuable tool for future applications in healthcare devices and small engineering systems.
文摘Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security,reliability,and efficiency.The healthcare sector becomes advantageous and offers different ways to manage patient information in order to improve healthcare service quality.The futuristic sustainable computing solutions in e-healthcare applications depend upon Internet of Things(IoT)in cloud computing environment.The energy consumed during data communication from IoT devices to cloud server is significantly high and it needs to be reduced with the help of clustering techniques.The current research article presents a new Oppositional Glowworm Swarm Optimization(OGSO)algorithmbased clustering with Deep Neural Network(DNN)called OGSO-DNN model for distributed healthcare systems.The OGSO algorithm was applied in this study to select the Cluster Heads(CHs)from the available IoT devices.The selected CHs transmit the data to cloud server,which then executes DNN-based classification process for healthcare diagnosis.An extensive simulation analysis was carried out utilizing a student perspective healthcare data generated from UCI repository and IoT devices to forecast the severity level of the disease among students.The proposed OGSO-DNN model outperformed previous methods by attaining the maximum average sensitivity of 96.956%,specificity of 95.076%,the accuracy of 95.764%and F-score value of 96.888%.
基金a grant from Technology-based Startups of New Energy and Industrial Technology Development Organization.
文摘Purpose This study examines the impact of 26-week exercise intervention facilitated by IoT devices on cognitive function,hippocampal volume,and health indicators in a real-world setting based on the Transtheoretical Model.Methods Middle-aged participants(n=121,mean age=49.8±7.62 years)were randomly assigned to BrainUp(n=61)or Sham(n=60)application groups.Both groups engaged in voluntary aerobic exercise over a period of 26 weeks using an IoT device.The BrainUp application was designed to deliver personalized exercise routines aimed at enhancing participants’cognitive function and hippocampal volume based on their individual level.Results Linear mixed models revealed that both groups exhibited improvements in cognitive function and health indicators at post-intervention compared to pre-intervention,but no significant changes in hippocampal volume in either group over time.Path analysis and structural equation modeling indicated that motivation for exercise at 26 weeks played a key role in improving cognitive function and blood glucose,and alleviating depression.Conclusions Integrating physical activity with IoT devices in real-world settings shows promise for enhancing brain health and preventing hippocampal volume loss due to aging.Motivation might play a crucial role in maximizing the health benefits of physical activity,especially during the transition from the Action to Maintenance stages of behavioral changes to an active lifestyle.
文摘In the modern world,women now have tremendous success in every field.They can play,learn,and earn as much as men.But what about safety?Do they have the same secure environment that men and boys do?The answer is“NO”.Women and girls have been subjected to numerous incidents,including acid throwing,rape,kidnapping,and harassment.It is common to read a lot of news like this in newspapers every day.These incidents make women feel unsafe in this society.Our freedom came a long time ago,but women still lack complete security in this society.All women cannot fight or shout all the time when some danger is happening to them.What can the physically challenged person and Children do?To make women feel safe,we designed“Wrist Band”using IoT for women safety.As the sensors sense information from the body,it will always update the information such as pulse,temperature,and vibration to the well-wishers through the Blynk app.
文摘Every day,more and more data is being produced by the Internet of Things(IoT)applications.IoT data differ in amount,diversity,veracity,and velocity.Because of latency,various types of data handling in cloud computing are not suitable for many time-sensitive applications.When users move from one site to another,mobility also adds to the latency.By placing computing close to IoT devices with mobility support,fog computing addresses these problems.An efficient Load Balancing Algorithm(LBA)improves user experience and Quality of Service(QoS).Classification of Request(CoR)based Resource Adaptive LBA is suggested in this research.This technique clusters fog nodes using an efficient K-means clustering algorithm and then uses a Decision Tree approach to categorize the request.The decision-making process for time-sensitive and delay-tolerable requests is facilitated by the classification of requests.LBA does the operation based on these classifications.The MobFogSim simulation program is utilized to assess how well the algorithm with mobility features performs.The outcome demonstrates that the LBA algorithm’s performance enhances the total system performance,which was attained by(90.8%).Using LBA,several metrics may be examined,including Response Time(RT),delay(d),Energy Consumption(EC),and latency.Through the on-demand provisioning of necessary resources to IoT users,our suggested LBA assures effective resource usage.
文摘At present,the network security situation is becoming more and more serious.Malicious network attacks such as computer viruses,Trojans and hacker attacks are becoming more and more rampant.National and group network attacks such as network information war and network terrorism have a serious damage to the production and life of the whole society.At the same time,with the rapid development of Internet of Things and the arrival of 5G era,IoT devices as an important part of industrial Internet system,have become an important target of infiltration attacks by hostile forces.This paper describes the challenges facing firmware vulnerability detection at this stage,and introduces four automatic detection and utilization technologies in detail:based on patch comparison,based on control flow,based on data flow and ROP attack against buffer vulnerabilities.On the basis of clarifying its core idea,main steps and experimental results,the limitations of its method are proposed.Finally,combined with four automatic detection methods,this paper summarizes the known vulnerability detection steps based on firmware analysis,and looks forward to the follow-up work.
文摘The concept of Internet of Things（IoT）was first proposed by MIT Prof.Kevin Ash-ton in 1999.The implementation of IoT was mainly through RFID in its early time.With advanced technology and manufacture,diverse implementation forms of IoT are becoming possible.Wearable devices,as an essential branch of IoT,will have broad application prospects in health monitoring and intelligent healthcare.
文摘The emergence of the Internet of Things(IoT)has triggered a massive digital transformation across numerous sectors.This transformation requires efficient wireless communication and connectivity,which depend on the optimal utilization of the available spectrum resource.Given the limited availability of spectrum resources,spectrum sharing has emerged as a favored solution to empower IoT deployment and connectivity,so adequate planning of the spectrum resource utilization is thus essential to pave the way for the next generation of IoT applications,including 5G and beyond.This article presents a comprehensive study of prevalent wireless technologies employed in the field of the spectrum,with a primary focus on spectrum-sharing solutions,including shared spectrum.It highlights the associated security and privacy concerns when the IoT devices access the shared spectrum.This survey examines the benefits and drawbacks of various spectrum-sharing technologies and their solutions for various IoT applications.Lastly,it identifies future IoT obstacles and suggests potential research directions to address them.
基金supported by the National Science Fund for Distinguished Young Scholars(62025205)the National Natural Science Foundation of China(Grant Nos.62032020,62102317)CityU APRC Grant(9610633).
文摘1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves bandwidth.However,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency bottlenecks.It is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.
