The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
Educational institutions are soft targets for the terrorist with massive and defenseless people.In the recent past,numbers of such attacks have been executed around the world.Conducting research,in order to provide a ...Educational institutions are soft targets for the terrorist with massive and defenseless people.In the recent past,numbers of such attacks have been executed around the world.Conducting research,in order to provide a secure environment to the educational institutions is a challenging task.This effort is motivated by recent assaults,made at Army Public School Peshawar,following another attack at Charsada University,Khyber Pukhtun Khwa,Pakistan and also the Santa Fe High School Texas,USA massacre.This study uses the basic technologies of edge computing,cloud computing and IoT to design a smart emergency alarm system framework.IoT is engaged in developing this world smarter,can contribute significantly to design the Smart Security Framework(SSF)for educational institutions.In the emergency situation,all the command and control centres must be informed within seconds to halt or minimize the loss.In this article,the SSF is proposed.This framework works on three layers.The first layer is the sensors and smart devices layer.All these sensors and smart devices are connected to the Emergency Control Room(ECR),which is the second layer of the proposed framework.The second layer uses edge computing technologies to process massive data and information locally.The third layer uses cloud computing techniques to transmit and process data and information to different command and control centres.The proposed system was tested on Cisco Packet Tracer 7.The result shows that this approach can play an efficient role in security alert,not only in the educational institutions but also in other organizations too.展开更多
The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)...The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.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
The rapid expansion of the Internet of Things(IoT)has introduced significant security challenges due to the scale,complexity,and heterogeneity of interconnected devices.The current traditional centralized security mod...The rapid expansion of the Internet of Things(IoT)has introduced significant security challenges due to the scale,complexity,and heterogeneity of interconnected devices.The current traditional centralized security models are deemed irrelevant in dealing with these threats,especially in decentralized applications where the IoT devices may at times operate on minimal resources.The emergence of new technologies,including Artificial Intelligence(AI),blockchain,edge computing,and Zero-Trust-Architecture(ZTA),is offering potential solutions as it helps with additional threat detection,data integrity,and system resilience in real-time.AI offers sophisticated anomaly detection and prediction analytics,and blockchain delivers decentralized and tamper-proof insurance over device communication and exchange of information.Edge computing enables low-latency character processing by distributing and moving the computational workload near the devices.The ZTA enhances security by continuously verifying each device and user on the network,adhering to the“never trust,always verify”ideology.The present research paper is a review of these technologies,finding out how they are used in securing IoT ecosystems,the issues of such integration,and the possibility of developing a multi-layered,adaptive security structure.Major concerns,such as scalability,resource limitations,and interoperability,are identified,and the way to optimize the application of AI,blockchain,and edge computing in zero-trust IoT systems in the future is discussed.展开更多
The Internet of Things(IoT)is a smart infrastructure where devices share captured data with the respective server or edge modules.However,secure and reliable communication is among the challenging tasks in these netwo...The Internet of Things(IoT)is a smart infrastructure where devices share captured data with the respective server or edge modules.However,secure and reliable communication is among the challenging tasks in these networks,as shared channels are used to transmit packets.In this paper,a decision tree is integrated with other metrics to form a secure distributed communication strategy for IoT.Initially,every device works collaboratively to form a distributed network.In this model,if a device is deployed outside the coverage area of the nearest server,it communicates indirectly through the neighboring devices.For this purpose,every device collects data from the respective neighboring devices,such as hop count,average packet transmission delay,criticality factor,link reliability,and RSSI value,etc.These parameters are used to find an optimal route from the source to the destination.Secondly,the proposed approach has enabled devices to learn from the environment and adjust the optimal route-finding formula accordingly.Moreover,these devices and server modules must ensure that every packet is transmitted securely,which is possible only if it is encrypted with an encryption algorithm.For this purpose,a decision tree-enabled device-to-server authentication algorithm is presented where every device and server must take part in the offline phase.Simulation results have verified that the proposed distributed communication approach has the potential to ensure the integrity and confidentiality of data during transmission.Moreover,the proposed approach has outperformed the existing approaches in terms of communication cost,processing overhead,end-to-end delay,packet loss ratio,and throughput.Finally,the proposed approach is adoptable in different networking infrastructures.展开更多
The rapid proliferation of Internet of Things(IoT)devices has heightened security concerns,making intrusion detection a pivotal challenge in safeguarding these networks.Traditional centralized Intrusion Detection Syst...The rapid proliferation of Internet of Things(IoT)devices has heightened security concerns,making intrusion detection a pivotal challenge in safeguarding these networks.Traditional centralized Intrusion Detection Systems(IDS)often fail to meet the privacy requirements and scalability demands of large-scale IoT ecosystems.To address these challenges,we propose an innovative privacy-preserving approach leveraging Federated Learning(FL)for distributed intrusion detection.Our model eliminates the need for aggregating sensitive data on a central server by training locally on IoT devices and sharing only encrypted model updates,ensuring enhanced privacy and scalability without compromising detection accuracy.Key innovations of this research include the integration of advanced deep learning techniques for real-time threat detection with minimal latency and a novel model to fortify the system’s resilience against diverse cyber-attacks such as Distributed Denial of Service(DDoS)and malware injections.Our evaluation on three benchmark IoT datasets demonstrates significant improvements:achieving 92.78%accuracy on NSL-KDD,91.47%on BoT-IoT,and 92.05%on UNSW-NB15.The precision,recall,and F1-scores for all datasets consistently exceed 91%.Furthermore,the communication overhead was reduced to 85 MB for NSL-KDD,105 MB for BoT-IoT,and 95 MB for UNSW-NB15—substantially lower than traditional centralized IDS approaches.This study contributes to the domain by presenting a scalable,secure,and privacy-preserving solution tailored to the unique characteristics of IoT environments.The proposed framework is adaptable to dynamic and heterogeneous settings,with potential applications extending to other privacy-sensitive domains.Future work will focus on enhancing the system’s efficiency and addressing emerging challenges such as model poisoning attacks in federated environments.展开更多
The Internet of Things (IoT) and edge-assisted networking infrastructures are capable of bringing data processing and accessibility services locally at the respective edge rather than at a centralized module. These in...The Internet of Things (IoT) and edge-assisted networking infrastructures are capable of bringing data processing and accessibility services locally at the respective edge rather than at a centralized module. These infrastructures are very effective in providing a fast response to the respective queries of the requesting modules, but their distributed nature has introduced other problems such as security and privacy. To address these problems, various security-assisted communication mechanisms have been developed to safeguard every active module, i.e., devices and edges, from every possible vulnerability in the IoT. However, these methodologies have neglected one of the critical issues, which is the prediction of fraudulent devices, i.e., adversaries, preferably as early as possible in the IoT. In this paper, a hybrid communication mechanism is presented where the Hidden Markov Model (HMM) predicts the legitimacy of the requesting device (both source and destination), and the Advanced Encryption Standard (AES) safeguards the reliability of the transmitted data over a shared communication medium, preferably through a secret shared key, i.e., , and timestamp information. A device becomes trusted if it has passed both evaluation levels, i.e., HMM and message decryption, within a stipulated time interval. The proposed hybrid, along with existing state-of-the-art approaches, has been simulated in the realistic environment of the IoT to verify the security measures. These evaluations were carried out in the presence of intruders capable of launching various attacks simultaneously, such as man-in-the-middle, device impersonations, and masquerading attacks. Moreover, the proposed approach has been proven to be more effective than existing state-of-the-art approaches due to its exceptional performance in communication, processing, and storage overheads, i.e., 13%, 19%, and 16%, respectively. Finally, the proposed hybrid approach is pruned against well-known security attacks in the IoT.展开更多
The Internet of Medical Things(IoMT)is transforming healthcare by enabling real-time data collection,analysis,and personalized treatment through interconnected devices such as sensors and wearables.The integration of ...The Internet of Medical Things(IoMT)is transforming healthcare by enabling real-time data collection,analysis,and personalized treatment through interconnected devices such as sensors and wearables.The integration of Digital Twins(DTs),the virtual replicas of physical components and processes,has also been found to be a game changer for the ever-evolving IoMT.However,these advancements in the healthcare domain come with significant cybersecurity challenges,exposing it to malicious attacks and several security threats.Intrusion Detection Systems(IDSs)serve as a critical defense mechanism,yet traditional IDS approaches often struggle with the complexity and scale of IoMT networks.With this context,this paper follows a systematic approach to analyze the existing literature and highlight the current trends and challenges related to IDS in the IoMT domain.We leveraged techniques like bibliographic and keyword analysis to collect 832 research works published from 2007 to 2025,aligned with the theme“Digital Twins and IDS in IoMT.”It was found that by simulating device behaviours and network interactions in IoMT,DTs not only provide a proactive platform for early threat detection,but also offer a scalable and adaptive approach to mitigating evolving security threats in IoMT.Overall,this review provides a closer look into the role of IDS and DT in securing IoMT systems and sheds light on the possible research directions for developers and the research community.展开更多
One of the buzzwords in the Information Technology is Internet of Things (IoT). The future is Internet of Things, which will transform the real world objects into intelligent virtual objects. The IoT aims to unify eve...One of the buzzwords in the Information Technology is Internet of Things (IoT). The future is Internet of Things, which will transform the real world objects into intelligent virtual objects. The IoT aims to unify everything in our world under a common infrastructure, giving us not only control of things around us, but also keeping us informed of the state of the things. In Light of this, present study addresses IoT concepts through systematic review of scholarly research papers, corporate white papers, professional discussions with experts and online databases. Moreover this research article focuses on definitions, geneses, basic requirements, characteristics and aliases of Internet of Things. The main objective of this paper is to provide an overview of Internet of Things, architectures, and vital technologies and their usages in our daily life. However, this manuscript will give good comprehension for the new researchers, who want to do research in this field of Internet of Things (Technological GOD) and facilitate knowledge accumulation in efficiently.展开更多
Unquestionably, communicating entities （object, or things） in the Internet of Things （IoT） context are playing an active role in human activities, systems and processes. The high connectivity of intelligent object...Unquestionably, communicating entities （object, or things） in the Internet of Things （IoT） context are playing an active role in human activities, systems and processes. The high connectivity of intelligent objects and their severe constraints lead to many security challenges, which are not included in the classical formulation of security problems and solutions. The Security Shield for IoT has been identified by DARPA （Defense Advanced Research Projects Agency） as one of the four projects with a potential impact broader than the Internet itself. To help interested researchers contribute to this research area, an overview of the loT security roadmap overview is presented in this paper based on a novel cognitive and systemic approach. The role of each component of the approach is explained, we also study its interactions with the other main components, and their impact on the overall. A case study is presented to highlight the components and interactions of the systemic and cognitive approach. Then, security questions about privacy, trust, identification, and access control are discussed. According to the novel taxonomy of the loT framework, different research challenges are highlighted, important solutions and research activities are revealed, and interesting research directions are proposed. In addition, current stan dardization activities are surveyed and discussed to the ensure the security of loT components and applications.展开更多
As the Internet of Things (IoT) is emerging as an attractive paradigm, a typical IoT architecture that U2IoT (Unit IoT and Ubiquitous IoT) model has been presented for the future IoT. Based on the U2IoT model, this pa...As the Internet of Things (IoT) is emerging as an attractive paradigm, a typical IoT architecture that U2IoT (Unit IoT and Ubiquitous IoT) model has been presented for the future IoT. Based on the U2IoT model, this paper proposes a cyber-physical-social based security architecture (IPM) to deal with Information, Physical, and Management security perspectives, and presents how the architectural abstractions support U2IoT model. In particular, 1) an information security model is established to describe the mapping relations among U2IoT, security layer, and security requirement, in which social layer and additional intelligence and compatibility properties are infused into IPM;2) physical security referring to the external context and inherent infrastructure are inspired by artificial immune algorithms;3) recommended security strategies are suggested for social management control. The proposed IPM combining the cyber world, physical world and human social provides constructive proposal towards the future IoT security and privacy protection.展开更多
With the development of information technology,the Internet of Things(IoT)has gradually become the third wave of the worldwide information industry revolution after the computer and the Internet.The application of the...With the development of information technology,the Internet of Things(IoT)has gradually become the third wave of the worldwide information industry revolution after the computer and the Internet.The application of the IoT has brought great convenience to people’s production and life.However,the potential information security problems in various IoT applications are gradually exposed and people pay more attention to them.The traditional centralized data storage and management model of the IoT is easy to cause transmission delay,single point of failure,privacy disclosure and other problems,and eventually leads to unpredictable behavior of the system.Blockchain technology can effectively improve the operation and data security status of the IoT.Referring to the storage model of the Fabric blockchain project,this paper designs a data security storage model suitable for the IoT system.The simulation results show that the model is not only effective and extensible,but also can better protect the data security of the Internet of Things.展开更多
By analyzing existed Internet of Things＇ system security vulnerabilities, a security architecture on trusting one is constructed. In the infrastructure, an off-line identity authentication based on the combined publi...By analyzing existed Internet of Things＇ system security vulnerabilities, a security architecture on trusting one is constructed. In the infrastructure, an off-line identity authentication based on the combined public key （CPK） mechanism is proposed, which solves the problems about a mass amount of authentications and the cross-domain authentication by integrating nodes＇ validity of identity authentication and uniqueness of identification. Moreover, the proposal of constructing nodes＇ authentic identification, valid authentication and credible communication connection at the application layer through the perception layer impels the formation of trust chain and relationship among perceptional nodes. Consequently, a trusting environment of the Internet of Things is built, by which a guidance of designing the trusted one would be provided.展开更多
Aiming at the issues of privacy security in Internet of Things （IoT） applications, we propose an effective risk assessment model to handle probabilistic causality of evaluation factors and derive weights of influenc...Aiming at the issues of privacy security in Internet of Things （IoT） applications, we propose an effective risk assessment model to handle probabilistic causality of evaluation factors and derive weights of influence-relation of propagation paths. The model undertakes probabilistic inference and generates values of risk probability for assets and propagation paths by using Bayesian causal relation-network and prior probability. According to Bayes- ian network （BN） structure, the risk analysts can easily find out relevant risk propagation paths and calculate weight values of each path by using decision-making trial and evaluation laboratory （DEMATEL）. This model is applied to determine the risk level of assets and each risk propagation path as well as implement countermeasure of recommendation in accordance with evaluation results. The simulation analysis shows that this model efficiently revises recommendation of countermeasures for decision-makers and mitigates risk to an acceptable range, in addition, it provides the theoretical basis for decision-making of privacy security risk assessment （PSRA） for further development in lot area.展开更多
The Internet of Things (IoT) represents a technologically optimistic future where objects will be connected to the internet and make intelligent collaborations with other objects anywhere, anytime. Although it makes a...The Internet of Things (IoT) represents a technologically optimistic future where objects will be connected to the internet and make intelligent collaborations with other objects anywhere, anytime. Although it makes appreciable development, there are still uncertainties about security concepts of its usage that is usually considered as a major concern in the design of IoT architectures. This paper presents a general survey of all the security issues in IoT along with an analysis of IoT architectures. The study defines security requirements and challenges that are common in IoT implementations and discusses security threats and related solutions on each layer of IoT architecture to make this technology secure and more widespread accordingly.展开更多
Internet of things (IoT) is a developing technology with a lot of scope in the future. It can ease various different tasks for us. On one hand, IoT is useful for us, on the other hand, it has many serious security thr...Internet of things (IoT) is a developing technology with a lot of scope in the future. It can ease various different tasks for us. On one hand, IoT is useful for us, on the other hand, it has many serious security threats, like data breaches, side-channel attacks, and virus and data authentication. Classical cryptographic algorithms, like the Rivest-Shamir-Adleman (RSA) algorithm, work well under the classical computers. But the technology is slowly shifting towards quantum computing, which has immense processing power and is more than enough to break the current cryptographic algorithms easily. So it is required that we have to design quantum cryptographic algorithms to prevent our systems from security breaches even before quantum computers come in the market for commercial uses. IoT will also be one of the disciplines, which needs to be secured to prevent any malicious activities. In this paper, we review the common security threats in IoT and the presently available solutions with their drawbacks. Then quantum cryptography is introduced with some of its variations. And finally, the analysis has been carried out in terms of the pros and cons of implementing quantum cryptography for IoT security.展开更多
The world is rapidly changing with the advance of information technology.The expansion of the Internet of Things(IoT)is a huge step in the development of the smart city.The IoT consists of connected devices that trans...The world is rapidly changing with the advance of information technology.The expansion of the Internet of Things(IoT)is a huge step in the development of the smart city.The IoT consists of connected devices that transfer information.The IoT architecture permits on-demand services to a public pool of resources.Cloud computing plays a vital role in developing IoT-enabled smart applications.The integration of cloud computing enhances the offering of distributed resources in the smart city.Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability,security,performance,condentiality,and privacy.The key reason for cloud-and IoT-enabled smart city application failure is improper security practices at the early stages of development.This article proposes a framework to collect security requirements during the initial development phase of cloud-assisted IoT-enabled smart city applications.Its three-layered architecture includes privacy preserved stakeholder analysis(PPSA),security requirement modeling and validation(SRMV),and secure cloud-assistance(SCA).A case study highlights the applicability and effectiveness of the proposed framework.A hybrid survey enables the identication and evaluation of signicant challenges.展开更多
As time and space constraints decrease due to the development of wireless communication network technology,the scale and scope of cyber-attacks targeting the Internet of Things(IoT)are increasing.However,it is difficu...As time and space constraints decrease due to the development of wireless communication network technology,the scale and scope of cyber-attacks targeting the Internet of Things(IoT)are increasing.However,it is difficult to apply high-performance security modules to the IoT owing to the limited battery,memory capacity,and data transmission performance depend-ing on the size of the device.Conventional research has mainly reduced power consumption by lightening encryption algorithms.However,it is difficult to defend large-scale information systems and networks against advanced and intelligent attacks because of the problem of deteriorating security perfor-mance.In this study,we propose wake-up security(WuS),a low-power security architecture that can utilize high-performance security algorithms in an IoT environment.By introducing a small logic that performs anomaly detection on the IoT platform and executes the security module only when necessary according to the anomaly detection result,WuS improves security and power efficiency while using a relatively high-complexity security module in a low-power environment compared to the conventional method of periodically exe-cuting a high-performance security module.In this study,a Python simulator based on the UNSW-NB15 dataset is used to evaluate the power consumption,latency,and security of the proposed method.The evaluation results reveal that the power consumption of the proposed WuS mechanism is approxi-mately 51.8%and 27.2%lower than those of conventional high-performance security and lightweight security modules,respectively.Additionally,the laten-cies are approximately 74.8%and 65.9%lower,respectively.Furthermore,the WuS mechanism achieved a high detection accuracy of approximately 96.5%or greater,proving that the detection efficiency performance improved by approximately 33.5%compared to the conventional model.The performance evaluation results for the proposed model varied depending on the applied anomaly-detection model.Therefore,they can be used in various ways by selecting suitable models based on the performance levels required in each industry.展开更多
In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost e...In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
文摘Educational institutions are soft targets for the terrorist with massive and defenseless people.In the recent past,numbers of such attacks have been executed around the world.Conducting research,in order to provide a secure environment to the educational institutions is a challenging task.This effort is motivated by recent assaults,made at Army Public School Peshawar,following another attack at Charsada University,Khyber Pukhtun Khwa,Pakistan and also the Santa Fe High School Texas,USA massacre.This study uses the basic technologies of edge computing,cloud computing and IoT to design a smart emergency alarm system framework.IoT is engaged in developing this world smarter,can contribute significantly to design the Smart Security Framework(SSF)for educational institutions.In the emergency situation,all the command and control centres must be informed within seconds to halt or minimize the loss.In this article,the SSF is proposed.This framework works on three layers.The first layer is the sensors and smart devices layer.All these sensors and smart devices are connected to the Emergency Control Room(ECR),which is the second layer of the proposed framework.The second layer uses edge computing technologies to process massive data and information locally.The third layer uses cloud computing techniques to transmit and process data and information to different command and control centres.The proposed system was tested on Cisco Packet Tracer 7.The result shows that this approach can play an efficient role in security alert,not only in the educational institutions but also in other organizations too.
基金funded and supported by the Ongoing Research Funding program(ORF-2025-314),King Saud University,Riyadh,Saudi Arabia.
文摘The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.
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.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘The rapid expansion of the Internet of Things(IoT)has introduced significant security challenges due to the scale,complexity,and heterogeneity of interconnected devices.The current traditional centralized security models are deemed irrelevant in dealing with these threats,especially in decentralized applications where the IoT devices may at times operate on minimal resources.The emergence of new technologies,including Artificial Intelligence(AI),blockchain,edge computing,and Zero-Trust-Architecture(ZTA),is offering potential solutions as it helps with additional threat detection,data integrity,and system resilience in real-time.AI offers sophisticated anomaly detection and prediction analytics,and blockchain delivers decentralized and tamper-proof insurance over device communication and exchange of information.Edge computing enables low-latency character processing by distributing and moving the computational workload near the devices.The ZTA enhances security by continuously verifying each device and user on the network,adhering to the“never trust,always verify”ideology.The present research paper is a review of these technologies,finding out how they are used in securing IoT ecosystems,the issues of such integration,and the possibility of developing a multi-layered,adaptive security structure.Major concerns,such as scalability,resource limitations,and interoperability,are identified,and the way to optimize the application of AI,blockchain,and edge computing in zero-trust IoT systems in the future is discussed.
基金supported by the Princess Nourah bint Abdulrahman University Riyadh,Saudi Arabia,through Project number(PNURSP2025R235).
文摘The Internet of Things(IoT)is a smart infrastructure where devices share captured data with the respective server or edge modules.However,secure and reliable communication is among the challenging tasks in these networks,as shared channels are used to transmit packets.In this paper,a decision tree is integrated with other metrics to form a secure distributed communication strategy for IoT.Initially,every device works collaboratively to form a distributed network.In this model,if a device is deployed outside the coverage area of the nearest server,it communicates indirectly through the neighboring devices.For this purpose,every device collects data from the respective neighboring devices,such as hop count,average packet transmission delay,criticality factor,link reliability,and RSSI value,etc.These parameters are used to find an optimal route from the source to the destination.Secondly,the proposed approach has enabled devices to learn from the environment and adjust the optimal route-finding formula accordingly.Moreover,these devices and server modules must ensure that every packet is transmitted securely,which is possible only if it is encrypted with an encryption algorithm.For this purpose,a decision tree-enabled device-to-server authentication algorithm is presented where every device and server must take part in the offline phase.Simulation results have verified that the proposed distributed communication approach has the potential to ensure the integrity and confidentiality of data during transmission.Moreover,the proposed approach has outperformed the existing approaches in terms of communication cost,processing overhead,end-to-end delay,packet loss ratio,and throughput.Finally,the proposed approach is adoptable in different networking infrastructures.
基金supported and funded by the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘The rapid proliferation of Internet of Things(IoT)devices has heightened security concerns,making intrusion detection a pivotal challenge in safeguarding these networks.Traditional centralized Intrusion Detection Systems(IDS)often fail to meet the privacy requirements and scalability demands of large-scale IoT ecosystems.To address these challenges,we propose an innovative privacy-preserving approach leveraging Federated Learning(FL)for distributed intrusion detection.Our model eliminates the need for aggregating sensitive data on a central server by training locally on IoT devices and sharing only encrypted model updates,ensuring enhanced privacy and scalability without compromising detection accuracy.Key innovations of this research include the integration of advanced deep learning techniques for real-time threat detection with minimal latency and a novel model to fortify the system’s resilience against diverse cyber-attacks such as Distributed Denial of Service(DDoS)and malware injections.Our evaluation on three benchmark IoT datasets demonstrates significant improvements:achieving 92.78%accuracy on NSL-KDD,91.47%on BoT-IoT,and 92.05%on UNSW-NB15.The precision,recall,and F1-scores for all datasets consistently exceed 91%.Furthermore,the communication overhead was reduced to 85 MB for NSL-KDD,105 MB for BoT-IoT,and 95 MB for UNSW-NB15—substantially lower than traditional centralized IDS approaches.This study contributes to the domain by presenting a scalable,secure,and privacy-preserving solution tailored to the unique characteristics of IoT environments.The proposed framework is adaptable to dynamic and heterogeneous settings,with potential applications extending to other privacy-sensitive domains.Future work will focus on enhancing the system’s efficiency and addressing emerging challenges such as model poisoning attacks in federated environments.
基金supported by the Deanship of Graduate Studies and Scientific Research at Qassim University via Grant No.(QU-APC-2025).
文摘The Internet of Things (IoT) and edge-assisted networking infrastructures are capable of bringing data processing and accessibility services locally at the respective edge rather than at a centralized module. These infrastructures are very effective in providing a fast response to the respective queries of the requesting modules, but their distributed nature has introduced other problems such as security and privacy. To address these problems, various security-assisted communication mechanisms have been developed to safeguard every active module, i.e., devices and edges, from every possible vulnerability in the IoT. However, these methodologies have neglected one of the critical issues, which is the prediction of fraudulent devices, i.e., adversaries, preferably as early as possible in the IoT. In this paper, a hybrid communication mechanism is presented where the Hidden Markov Model (HMM) predicts the legitimacy of the requesting device (both source and destination), and the Advanced Encryption Standard (AES) safeguards the reliability of the transmitted data over a shared communication medium, preferably through a secret shared key, i.e., , and timestamp information. A device becomes trusted if it has passed both evaluation levels, i.e., HMM and message decryption, within a stipulated time interval. The proposed hybrid, along with existing state-of-the-art approaches, has been simulated in the realistic environment of the IoT to verify the security measures. These evaluations were carried out in the presence of intruders capable of launching various attacks simultaneously, such as man-in-the-middle, device impersonations, and masquerading attacks. Moreover, the proposed approach has been proven to be more effective than existing state-of-the-art approaches due to its exceptional performance in communication, processing, and storage overheads, i.e., 13%, 19%, and 16%, respectively. Finally, the proposed hybrid approach is pruned against well-known security attacks in the IoT.
基金This research is conducted as part of the project titled“Digital Twin-based Intrusion Detection System Using Federated Learning for IoMT”(2024-2027),supported by C3iHub,IIT Kanpur,India,under Sanction Order No.:IHUB-NTIHAC/2024/01/3.
文摘The Internet of Medical Things(IoMT)is transforming healthcare by enabling real-time data collection,analysis,and personalized treatment through interconnected devices such as sensors and wearables.The integration of Digital Twins(DTs),the virtual replicas of physical components and processes,has also been found to be a game changer for the ever-evolving IoMT.However,these advancements in the healthcare domain come with significant cybersecurity challenges,exposing it to malicious attacks and several security threats.Intrusion Detection Systems(IDSs)serve as a critical defense mechanism,yet traditional IDS approaches often struggle with the complexity and scale of IoMT networks.With this context,this paper follows a systematic approach to analyze the existing literature and highlight the current trends and challenges related to IDS in the IoMT domain.We leveraged techniques like bibliographic and keyword analysis to collect 832 research works published from 2007 to 2025,aligned with the theme“Digital Twins and IDS in IoMT.”It was found that by simulating device behaviours and network interactions in IoMT,DTs not only provide a proactive platform for early threat detection,but also offer a scalable and adaptive approach to mitigating evolving security threats in IoMT.Overall,this review provides a closer look into the role of IDS and DT in securing IoMT systems and sheds light on the possible research directions for developers and the research community.
文摘One of the buzzwords in the Information Technology is Internet of Things (IoT). The future is Internet of Things, which will transform the real world objects into intelligent virtual objects. The IoT aims to unify everything in our world under a common infrastructure, giving us not only control of things around us, but also keeping us informed of the state of the things. In Light of this, present study addresses IoT concepts through systematic review of scholarly research papers, corporate white papers, professional discussions with experts and online databases. Moreover this research article focuses on definitions, geneses, basic requirements, characteristics and aliases of Internet of Things. The main objective of this paper is to provide an overview of Internet of Things, architectures, and vital technologies and their usages in our daily life. However, this manuscript will give good comprehension for the new researchers, who want to do research in this field of Internet of Things (Technological GOD) and facilitate knowledge accumulation in efficiently.
文摘Unquestionably, communicating entities （object, or things） in the Internet of Things （IoT） context are playing an active role in human activities, systems and processes. The high connectivity of intelligent objects and their severe constraints lead to many security challenges, which are not included in the classical formulation of security problems and solutions. The Security Shield for IoT has been identified by DARPA （Defense Advanced Research Projects Agency） as one of the four projects with a potential impact broader than the Internet itself. To help interested researchers contribute to this research area, an overview of the loT security roadmap overview is presented in this paper based on a novel cognitive and systemic approach. The role of each component of the approach is explained, we also study its interactions with the other main components, and their impact on the overall. A case study is presented to highlight the components and interactions of the systemic and cognitive approach. Then, security questions about privacy, trust, identification, and access control are discussed. According to the novel taxonomy of the loT framework, different research challenges are highlighted, important solutions and research activities are revealed, and interesting research directions are proposed. In addition, current stan dardization activities are surveyed and discussed to the ensure the security of loT components and applications.
文摘As the Internet of Things (IoT) is emerging as an attractive paradigm, a typical IoT architecture that U2IoT (Unit IoT and Ubiquitous IoT) model has been presented for the future IoT. Based on the U2IoT model, this paper proposes a cyber-physical-social based security architecture (IPM) to deal with Information, Physical, and Management security perspectives, and presents how the architectural abstractions support U2IoT model. In particular, 1) an information security model is established to describe the mapping relations among U2IoT, security layer, and security requirement, in which social layer and additional intelligence and compatibility properties are infused into IPM;2) physical security referring to the external context and inherent infrastructure are inspired by artificial immune algorithms;3) recommended security strategies are suggested for social management control. The proposed IPM combining the cyber world, physical world and human social provides constructive proposal towards the future IoT security and privacy protection.
基金supported by the National Social Science Foundation Project of China under Grant 16BTQ085.
文摘With the development of information technology,the Internet of Things(IoT)has gradually become the third wave of the worldwide information industry revolution after the computer and the Internet.The application of the IoT has brought great convenience to people’s production and life.However,the potential information security problems in various IoT applications are gradually exposed and people pay more attention to them.The traditional centralized data storage and management model of the IoT is easy to cause transmission delay,single point of failure,privacy disclosure and other problems,and eventually leads to unpredictable behavior of the system.Blockchain technology can effectively improve the operation and data security status of the IoT.Referring to the storage model of the Fabric blockchain project,this paper designs a data security storage model suitable for the IoT system.The simulation results show that the model is not only effective and extensible,but also can better protect the data security of the Internet of Things.
基金supported by the 863 Program under Grant No. 2008AA04A107
文摘By analyzing existed Internet of Things＇ system security vulnerabilities, a security architecture on trusting one is constructed. In the infrastructure, an off-line identity authentication based on the combined public key （CPK） mechanism is proposed, which solves the problems about a mass amount of authentications and the cross-domain authentication by integrating nodes＇ validity of identity authentication and uniqueness of identification. Moreover, the proposal of constructing nodes＇ authentic identification, valid authentication and credible communication connection at the application layer through the perception layer impels the formation of trust chain and relationship among perceptional nodes. Consequently, a trusting environment of the Internet of Things is built, by which a guidance of designing the trusted one would be provided.
基金Supported by the National Key Technology Research and Development Program in the 12th Five year Plan of China(2012BAH08B02)the National Natural Science Foundation of China(61272513)the Project of Humanities and Social Sciences of Ministry of Education in China(10YJC630385)
文摘Aiming at the issues of privacy security in Internet of Things （IoT） applications, we propose an effective risk assessment model to handle probabilistic causality of evaluation factors and derive weights of influence-relation of propagation paths. The model undertakes probabilistic inference and generates values of risk probability for assets and propagation paths by using Bayesian causal relation-network and prior probability. According to Bayes- ian network （BN） structure, the risk analysts can easily find out relevant risk propagation paths and calculate weight values of each path by using decision-making trial and evaluation laboratory （DEMATEL）. This model is applied to determine the risk level of assets and each risk propagation path as well as implement countermeasure of recommendation in accordance with evaluation results. The simulation analysis shows that this model efficiently revises recommendation of countermeasures for decision-makers and mitigates risk to an acceptable range, in addition, it provides the theoretical basis for decision-making of privacy security risk assessment （PSRA） for further development in lot area.
文摘The Internet of Things (IoT) represents a technologically optimistic future where objects will be connected to the internet and make intelligent collaborations with other objects anywhere, anytime. Although it makes appreciable development, there are still uncertainties about security concepts of its usage that is usually considered as a major concern in the design of IoT architectures. This paper presents a general survey of all the security issues in IoT along with an analysis of IoT architectures. The study defines security requirements and challenges that are common in IoT implementations and discusses security threats and related solutions on each layer of IoT architecture to make this technology secure and more widespread accordingly.
文摘Internet of things (IoT) is a developing technology with a lot of scope in the future. It can ease various different tasks for us. On one hand, IoT is useful for us, on the other hand, it has many serious security threats, like data breaches, side-channel attacks, and virus and data authentication. Classical cryptographic algorithms, like the Rivest-Shamir-Adleman (RSA) algorithm, work well under the classical computers. But the technology is slowly shifting towards quantum computing, which has immense processing power and is more than enough to break the current cryptographic algorithms easily. So it is required that we have to design quantum cryptographic algorithms to prevent our systems from security breaches even before quantum computers come in the market for commercial uses. IoT will also be one of the disciplines, which needs to be secured to prevent any malicious activities. In this paper, we review the common security threats in IoT and the presently available solutions with their drawbacks. Then quantum cryptography is introduced with some of its variations. And finally, the analysis has been carried out in terms of the pros and cons of implementing quantum cryptography for IoT security.
基金Taif University Researchers Supporting Project No.(TURSP-2020/126),Taif University,Taif,Saudi Arabia。
文摘The world is rapidly changing with the advance of information technology.The expansion of the Internet of Things(IoT)is a huge step in the development of the smart city.The IoT consists of connected devices that transfer information.The IoT architecture permits on-demand services to a public pool of resources.Cloud computing plays a vital role in developing IoT-enabled smart applications.The integration of cloud computing enhances the offering of distributed resources in the smart city.Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability,security,performance,condentiality,and privacy.The key reason for cloud-and IoT-enabled smart city application failure is improper security practices at the early stages of development.This article proposes a framework to collect security requirements during the initial development phase of cloud-assisted IoT-enabled smart city applications.Its three-layered architecture includes privacy preserved stakeholder analysis(PPSA),security requirement modeling and validation(SRMV),and secure cloud-assistance(SCA).A case study highlights the applicability and effectiveness of the proposed framework.A hybrid survey enables the identication and evaluation of signicant challenges.
基金supplemented by a paper presented at the 6th International Symposium on Mobile Internet Security(MobiSec 2022).
文摘As time and space constraints decrease due to the development of wireless communication network technology,the scale and scope of cyber-attacks targeting the Internet of Things(IoT)are increasing.However,it is difficult to apply high-performance security modules to the IoT owing to the limited battery,memory capacity,and data transmission performance depend-ing on the size of the device.Conventional research has mainly reduced power consumption by lightening encryption algorithms.However,it is difficult to defend large-scale information systems and networks against advanced and intelligent attacks because of the problem of deteriorating security perfor-mance.In this study,we propose wake-up security(WuS),a low-power security architecture that can utilize high-performance security algorithms in an IoT environment.By introducing a small logic that performs anomaly detection on the IoT platform and executes the security module only when necessary according to the anomaly detection result,WuS improves security and power efficiency while using a relatively high-complexity security module in a low-power environment compared to the conventional method of periodically exe-cuting a high-performance security module.In this study,a Python simulator based on the UNSW-NB15 dataset is used to evaluate the power consumption,latency,and security of the proposed method.The evaluation results reveal that the power consumption of the proposed WuS mechanism is approxi-mately 51.8%and 27.2%lower than those of conventional high-performance security and lightweight security modules,respectively.Additionally,the laten-cies are approximately 74.8%and 65.9%lower,respectively.Furthermore,the WuS mechanism achieved a high detection accuracy of approximately 96.5%or greater,proving that the detection efficiency performance improved by approximately 33.5%compared to the conventional model.The performance evaluation results for the proposed model varied depending on the applied anomaly-detection model.Therefore,they can be used in various ways by selecting suitable models based on the performance levels required in each industry.
文摘In order to incorporate smart elements into distribution networks at ITELCA laboratories in Bogotá-Colombia, a Machine-to-Machine-based solution has been developed. This solution aids in the process of low-cost electrical fault location, which contributes to improving quality of service, particularly by shortening interruption time spans in mid-voltage grids. The implementation makes use of MQTT protocol with an intensive use of Internet of things (IoT) environment which guarantees the following properties within the automation process: Advanced reports and statistics, remote command execution on one or more units (groups of units), detailed monitoring of remote units and custom alarm mechanism and firmware upgrade on one or more units (groups of units). This kind of implementation is the first one in Colombia and it is able to automatically recover from an N-1 fault.
