期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到21篇文章
< 1 2 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Communication-efficient federated multi-domain learning for network anomaly detection 认领 引用
1
作者 Chunjiong Zhang Gaoyang Shan Byeong-hee Roh 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期472-481,共10页
Efficient and accurate anomaly detection in a network is of great significance for maintaining network and device security.Most anomaly detection methods assume that different anomalous network data distributions are ... Efficient and accurate anomaly detection in a network is of great significance for maintaining network and device security.Most anomaly detection methods assume that different anomalous network data distributions are the same or similar and ignore data privacy preservation.In this paper,a novel Federated Learning(FL)is proposed that it can quickly detect different types of anomalies in Non-Independent and Identically Distributed(Non-IID)data.First,we design a multi-domain machine learning model for multi-domain data,named Aegean,which consists of two modules:an ensemble AutoEncoder(AE)and a Generative Adversarial Network(GAN).Second,because data from different domains are non-IID,we model the anomaly detection problem as a dual problem,which can be recast as a robust optimization problem.The robust optimization problem is non-convex and therefore difficult to solve.As a remedy,we formulate and solve a dual problem by taking the Lagrangian dual function of the original problem.Experiments demonstrate that Aegean significantly outperforms the current state-of-the-art methods,with a 16%F1 score improvement over that of a One-Class Support Vector Machine(OCSVM).The designed FL significantly reduces the communication overhead of FedAvg without sacrificing anomaly detection performance. 展开更多
关键词 Federated learning Multi-domain Network anomaly detection Communication
暂未订购 下载PDF
Secure monitoring of Internet of vehicles in 6G networks through intelligent re-flecting surfaces leveraging AI 认领 引用
2
作者 Sharanya Selvaraj Balasubramanian Prabhu Kavin +3 位作者 Priyan Malarvizhi Kumar Mohammed J.F.Alenazi Zaid Bin Faheem Jehad Ali 《Digital Communications and Networks》 SCIE EI CSCD 2025年第6期2003-2015,共13页
The ensemble of Information and Communication Technology(ICT)and Artificial Intelligence(AI)has catalysed many developments and innovations in the automotive industry.6G networks emerge as a promising technology for r... The ensemble of Information and Communication Technology(ICT)and Artificial Intelligence(AI)has catalysed many developments and innovations in the automotive industry.6G networks emerge as a promising technology for realising Intelligent Transport Systems(ITS),which benefits the drivers and society.As the network is highly heterogeneous and robust,the physical layer security and node reliability of the vehicles hold paramount significance.This work presents a novel methodology that integrates the prowess of computer vision techniques and the Lightweight Super Learning Ensemble(LSLE)of Machine Learning(ML)algorithms to predict the presence of intruders in the network.Furthermore,our work utilizes a Deep Convolutional Neural Network(DCNN)to detect obstacles by identifying the Region of Interest(ROI)in the images.As the network utilizes mm-waves with shorter wavelengths,Intelligent Reflecting Surfaces(IRS)are employed to redirect signals to legitimate nodes,thereby mitigating the malicious activity of intruders.The experimental simulation shows that the proposed LSLE outperforms the state-of-the-art techniques in terms of accuracy,False Positive Rate(FPR),Recall,F1-Score,and Precision.A consistent performance improvement with an average FPR of 85.08%and accuracy of 92.01%is achieved by the model.Thus,in the future,detecting moving obstacles and real-time network traffic monitoring can be included to achieve more realistic results. 展开更多
关键词 Intelligent reflecting surface 6G AI Deep convolution neural network Super learning Meta learner Intelligent transport systems
暂未订购 下载PDF
Secure Malicious Node Detection in Decentralized Healthcare Networks Using Cloud and Edge Computing with Blockchain-Enabled Federated Learning 认领 引用 被引量：1
3
作者 Raj Sonani Reham Alhejaili +2 位作者 Pushpalika Chatterjee Khalid Hamad Alnafisah Jehad Ali 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第9期3169-3189,共21页
Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes... Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92). 展开更多
关键词 Authentication blockchain deep learning federated learning healthcare network machine learning wearable sensor nodes
暂未订购 下载PDF
A Novel Features Prioritization Mechanism for Controllers in Software-Defined Networking 认领 引用 被引量：1
4
作者 Jehad Ali Byungkyu Lee +2 位作者 Jimyung Oh Jungtae Lee Byeong-hee Roh 《Computers, Materials & Continua》 SCIE EI 2021年第10期267-282,共16页
The controller in software-defined networking(SDN)acts as strategic point of control for the underlying network.Multiple controllers are available,and every single controller retains a number of features such as the O... The controller in software-defined networking(SDN)acts as strategic point of control for the underlying network.Multiple controllers are available,and every single controller retains a number of features such as the OpenFlow version,clustering,modularity,platform,and partnership support,etc.They are regarded as vital when making a selection among a set of controllers.As such,the selection of the controller becomes a multi-criteria decision making(MCDM)problem with several features.Hence,an increase in this number will increase the computational complexity of the controller selection process.Previously,the selection of controllers based on features has been studied by the researchers.However,the prioritization of features has gotten less attention.Moreover,several features increase the computational complexity of the selection process.In this paper,we propose a mathematical modeling for feature prioritization with analytical network process(ANP)bridge model for SDN controllers.The results indicate that a prioritized features model lead to a reduction in the computational complexity of the selection of SDN controller.In addition,our model generates prioritized features for SDN controllers. 展开更多
关键词 Software-defined networking controllers feature-based selection quality-of-service analytical network process analytical hierarchy process
暂未订购 下载PDF
An Intelligent Hybrid Mutual Authentication Scheme for Industrial Internet of Thing Networks 认领 引用
5
作者 Muhammad Adil Jehad Ali +6 位作者 Muhammad Sajjad Khan Junsu Kim Ryan Alturki Mohammad Zakarya Mukhtaj Khan Rahim Khan Su Min Kim 《Computers, Materials & Continua》 SCIE EI 2021年第7期447-470,共24页
Internet of Things(IoT)network used for industrial management is vulnerable to different security threats due to its unstructured deployment,and dynamic communication behavior.In literature various mechanisms addresse... Internet of Things(IoT)network used for industrial management is vulnerable to different security threats due to its unstructured deployment,and dynamic communication behavior.In literature various mechanisms addressed the security issue of Industrial IoT networks,but proper maintenance of the performance reliability is among the common challenges.In this paper,we proposed an intelligent mutual authentication scheme leveraging authentication aware node(AAN)and base station(BS)to identify routing attacks in Industrial IoT networks.The AAN and BS uses the communication parameter such as a route request(RREQ),node-ID,received signal strength(RSS),and round-trip time(RTT)information to identify malicious devices and routes in the deployed network.The feasibility of the proposed model is validated in the simulation environment,where OMNeT++was used as a simulation tool.We compare the results of the proposed model with existing field-proven schemes in terms of routing attacks detection,communication cost,latency,computational cost,and throughput.The results show that our proposed scheme surpasses the previous schemes regarding these performance parameters with the attack detection rate of 97.7%. 展开更多
关键词 Security industrial Internet of Things routing attacks routing protocols base station authentication aware nodes
暂未订购 下载PDF
Novel Architecture of OneM2M-Based Convergence Platform for Mixed Reality and IoT 认领 引用
6
作者 Seungwoon Lee Woogeun Kil +2 位作者 Byeong-hee Roh SJ Kim Jin-suk Kang 《Computers, Materials & Continua》 SCIE EI 2022年第4期51-69,共19页
There have been numerous works proposed to merge augmented reality/mixed reality(AR/MR)and Internet of Things(IoT)in various ways.However,they have focused on their specific target applications and have limitations on... There have been numerous works proposed to merge augmented reality/mixed reality(AR/MR)and Internet of Things(IoT)in various ways.However,they have focused on their specific target applications and have limitations on interoperability or reusability when utilizing them to different domains or adding other devices to the system.This paper proposes a novel architecture of a convergence platform for AR/MR and IoT systems and services.The proposed architecture adopts the oneM2M IoT standard as the basic framework that converges AR/MR and IoT systems and enables the development of application services used in general-purpose environments without being subordinate to specific systems,domains,and device manufacturers.We implement the proposed architecture utilizing the open-source oneM2M-based IoT server and device platforms released by the open alliance for IoT standards(OCEAN)and Microsoft HoloLens as an MR device platform.We also suggest and demonstrate the practical use cases and discuss the advantages of the proposed architecture. 展开更多
关键词 Internet of things(IoT) augmented realty(AR) mixed reality(MR) microsoft HoloLens oneM2M convergence platform
暂未订购 下载PDF
Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders(E-HAE) 认领 引用
7
作者 Lelisa Adeba Jilcha Deuk-Hun Kim +1 位作者 Julian Jang-Jaccard Jin Kwak 《Computer Systems Science & Engineering》 SCIE EI 2023年第9期3261-3284,共24页
Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the co... Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge effortlessly.As such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems.To address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.We collectively refer to the convergence of different technology sectors as the internet of blended environment.The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder.An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection accuracy.Furthermore,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational autoencoder.The proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively. 展开更多
关键词 Network intrusion detection anomaly detection TON_IoT dataset smart grid smart city smart factory digital healthcare autoencoder variational autoencoder LSTM convolutional variational autoencoder ensemble learning
暂未订购 下载PDF
Decentralized Authentication and Secure Distributed File Storage for Healthcare Systems Using Blockchain and IPFS 认领 引用
8
作者 Maazen Alsabaan Jasmin Praful Bharadiya +3 位作者 Vishwanath Eswarakrishnan Adnan Mustafa Cheema Zaid Bin Faheem Jehad Ali 《Computers, Materials & Continua》 SCIE EI 2025年第10期1135-1160,共26页
The healthcare sector involves many steps to ensure efficient care for patients,such as appointment scheduling,consultation plans,online follow-up,and more.However,existing healthcare mechanisms are unable to facilita... The healthcare sector involves many steps to ensure efficient care for patients,such as appointment scheduling,consultation plans,online follow-up,and more.However,existing healthcare mechanisms are unable to facilitate a large number of patients,as these systems are centralized and hence vulnerable to various issues,including single points of failure,performance bottlenecks,and substantial monetary costs.Furthermore,these mechanisms are unable to provide an efficient mechanism for saving data against unauthorized access.To address these issues,this study proposes a blockchain-based authentication mechanism that authenticates all healthcare stakeholders based on their credentials.Furthermore,also utilize the capabilities of the InterPlanetary File System(IPFS)to store the Electronic Health Record(EHR)in a distributed way.This IPFS platform addresses not only the issue of high data storage costs on blockchain but also the issue of a single point of failure in the traditional centralized data storage model.The simulation results demonstrate that our model outperforms the benchmark schemes and provides an efficient mechanism for managing healthcare sector operations.The results show that it takes approximately 3.5 s for the smart contract to authenticate the node and provide it with the decryption key,which is ultimately used to access the data.The simulation results show that our proposed model outperforms existing solutions in terms of execution time and scalability.The execution time of our model smart contract is around 9000 transactions in just 6.5 s,while benchmark schemes require approximately 7 s for the same number of transactions. 展开更多
关键词 InterPlanetary file system healthcare electronic health record
暂未订购 下载PDF
3D Head Pose Estimation through Facial Features and Deep Convolutional Neural Networks 认领 引用 被引量：2
9
作者 Khalil Khan Jehad Ali +6 位作者 Kashif Ahmad Asma Gul Ghulam Sarwar Sahib Khan Qui Thanh Hoai Ta Tae-Sun Chung Muhammad Attique 《Computers, Materials & Continua》 SCIE EI 2021年第2期1757-1770,共14页
Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis ... Face image analysis is one among several important cues in computer vision.Over the last five decades,methods for face analysis have received immense attention due to large scale applications in various face analysis tasks.Face parsing strongly benefits various human face image analysis tasks inducing face pose estimation.In this paper we propose a 3D head pose estimation framework developed through a prior end to end deep face parsing model.We have developed an end to end face parts segmentation framework through deep convolutional neural networks(DCNNs).For training a deep face parts parsing model,we label face images for seven different classes,including eyes,brows,nose,hair,mouth,skin,and back.We extract features from gray scale images by using DCNNs.We train a classifier using the extracted features.We use the probabilistic classification method to produce gray scale images in the form of probability maps for each dense semantic class.We use a next stage of DCNNs and extract features from grayscale images created as probability maps during the segmentation phase.We assess the performance of our newly proposed model on four standard head pose datasets,including Pointing’04,Annotated Facial Landmarks in the Wild(AFLW),Boston University(BU),and ICT-3DHP,obtaining superior results as compared to previous results. 展开更多
关键词 Face image analysis face parsing face pose estimation
暂未订购 下载PDF
An Efficient Blockchain-Based Healthcare System Using Artificial Intelligence 认领 引用 被引量：1
10
作者 Aitizaz Ali Muhammad Fermi Pasha +3 位作者 Ong Huey Fang Jehad Ali Mohammed A.Al.Zain Mehedi Masud 《Computers, Materials & Continua》 SCIE EI 2022年第5期2721-2738,共18页
Personal health records and electronic health records are considered as the most sensitive information in the healthcare domain.Several solutions have been provided for implementing the digital health system using blo... Personal health records and electronic health records are considered as the most sensitive information in the healthcare domain.Several solutions have been provided for implementing the digital health system using blockchain,but there are several challenges,such as secure access control and privacy is one of the prominent issues.Hence,we propose a novel framework and implemented an attribute-based access control system using blockchain.Moreover,we have also integrated artificial intelligence(AI)based approach to identify the behavior and activity for security reasons.The current methods only focus on the related clinical records received from a medical diagnosis.Moreover,existing methods are too inflexible to resourcefully sustenance metadata changes.A secure patient data access framework is proposed in this research,integrating blockchain,trust chain,and blockchain methods to overcome these problems in the literature for sharing and accessing digital healthcare data.We have used a neural network and classifier to categorize the user access to our proposed system.Our proposed scheme provides an intelligent and secure blockchain-based access control system in the digital healthcare system.Experimental results surpass the existing solutions by collecting attributes such as the number of transactions,number of nodes,transaction delay,block creation,and signature verification time. 展开更多
关键词 Personal health records blockchain technology artificial intelligence access control attributes health care system
暂未订购 下载PDF
Effective Controller Placement in Software-Defined Internet-of-Things Leveraging Deep Q-Learning (DQL) 认领 引用
11
作者 Jehad Ali Mohammed J.F.Alenazi 《Computers, Materials & Continua》 SCIE EI 2024年第12期4015-4032,共18页
The controller is a main component in the Software-Defined Networking(SDN)framework,which plays a significant role in enabling programmability and orchestration for 5G and next-generation networks.In SDN,frequent comm... The controller is a main component in the Software-Defined Networking(SDN)framework,which plays a significant role in enabling programmability and orchestration for 5G and next-generation networks.In SDN,frequent communication occurs between network switches and the controller,which manages and directs traffic flows.If the controller is not strategically placed within the network,this communication can experience increased delays,negatively affecting network performance.Specifically,an improperly placed controller can lead to higher end-to-end(E2E)delay,as switches must traverse more hops or encounter greater propagation delays when communicating with the controller.This paper introduces a novel approach using Deep Q-Learning(DQL)to dynamically place controllers in Software-Defined Internet of Things(SD-IoT)environments,with the goal of minimizing E2E delay between switches and controllers.E2E delay,a crucial metric for network performance,is influenced by two key factors:hop count,which measures the number of network nodes data must traverse,and propagation delay,which accounts for the physical distance between nodes.Our approach models the controller placement problem as a Markov Decision Process(MDP).In this model,the network configuration at any given time is represented as a“state,”while“actions”correspond to potential decisions regarding the placement of controllers or the reassignment of switches to controllers.Using a Deep Q-Network(DQN)to approximate the Q-function,the system learns the optimal controller placement by maximizing the cumulative reward,which is defined as the negative of the E2E delay.Essentially,the lower the delay,the higher the reward the system receives,enabling it to continuously improve its controller placement strategy.The experimental results show that our DQL-based method significantly reduces E2E delay when compared to traditional benchmark placement strategies.By dynamically learning from the network’s real-time conditions,the proposed method ensures that controller placement remains efficient and responsive,reducing communication delays and enhancing overall network performance. 展开更多
关键词 Software-defined networking deep Q-learning controller placement quality of service
暂未订购 下载PDF
A Hierarchal Clustered Based Proactive Caching in NDN-Based Vehicular Network 认领 引用 被引量：1
12
作者 Muhammad Yasir Khan Muhammad Adnan +3 位作者 Jawaid Iqbal Noor ul Amin Byeong-Hee Roh Jehad Ali 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期1185-1208,共24页
An Information-Centric Network(ICN)provides a promising paradigm for the upcoming internet architecture,which will struggle with steady growth in data and changes in accessmodels.Various ICN architectures have been de... An Information-Centric Network(ICN)provides a promising paradigm for the upcoming internet architecture,which will struggle with steady growth in data and changes in accessmodels.Various ICN architectures have been designed,including Named Data Networking(NDN),which is designed around content delivery instead of hosts.As data is the central part of the network.Therefore,NDN was developed to get rid of the dependency on IP addresses and provide content effectively.Mobility is one of the major research dimensions for this upcoming internet architecture.Some research has been carried out to solve the mobility issues,but it still has problems like handover delay and packet loss ratio during real-time video streaming in the case of consumer and producer mobility.To solve this issue,an efficient hierarchical Cluster Base Proactive Caching for Device Mobility Management(CB-PC-DMM)in NDN Vehicular Networks(NDN-VN)is proposed,through which the consumer receives the contents proactively after handover during the mobility of the consumer.When a consumer moves to the next destination,a handover interest is sent to the connected router,then the router multicasts the consumer’s desired data packet to the next hop of neighboring routers.Thus,once the handover process is completed,consumers can easily get the content to the newly connected router.A CB-PCDMM in NDN-VN is proposed that improves the packet delivery ratio and reduces the handover delay aswell as cluster overhead.Moreover,the intra and inter-domain handover handling procedures in CB-PC-DMM for NDN-VN have been described.For the validation of our proposed scheme,MATLAB simulations are conducted.The simulation results show that our proposed scheme reduces the handover delay and increases the consumer’s interest satisfaction ratio.The proposed scheme is compared with the existing stateof-the-art schemes,and the total percentage of handover delays is decreased by up to 0.1632%,0.3267%,2.3437%,2.3255%,and 3.7313%at the mobility speeds of 5 m/s,10 m/s,15 m/s,20 m/s,and 25 m/s,and the efficiency of the packet delivery ratio is improved by up to 1.2048%,5.0632%,6.4935%,6.943%,and 8.4507%.Furthermore,the simulation results of our proposed scheme show better efficiency in terms of Packet Delivery Ratio(PDR)from 0.071 to 0.077 and a decrease in the handover delay from 0.1334 to 0.129. 展开更多
关键词 Vehicular network named data networking caching hierarchical architecture
暂未订购 下载PDF
A Hybrid and Lightweight Device-to-Server Authentication Technique for the Internet of Things 认领 引用
13
作者 Shaha Al-Otaibi Rahim Khan +3 位作者 Hashim Ali Aftab Ahmed Khan Amir Saeed Jehad Ali 《Computers, Materials & Continua》 SCIE EI 2024年第3期3805-3823,共19页
The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective se... The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective server module.Although IoTs are cornerstones in different application domains,the device’s authenticity,i.e.,of server(s)and ordinary devices,is the most crucial issue and must be resolved on a priority basis.Therefore,various field-proven methodologies were presented to streamline the verification process of the communicating devices;however,location-aware authentication has not been reported as per our knowledge,which is a crucial metric,especially in scenarios where devices are mobile.This paper presents a lightweight and location-aware device-to-server authentication technique where the device’s membership with the nearest server is subjected to its location information along with other measures.Initially,Media Access Control(MAC)address and Advance Encryption Scheme(AES)along with a secret shared key,i.e.,λi of 128 bits,have been utilized by Trusted Authority(TA)to generate MaskIDs,which are used instead of the original ID,for every device,i.e.,server and member,and are shared in the offline phase.Secondly,TA shares a list of authentic devices,i.e.,server Sj and members Ci,with every device in the IoT for the onward verification process,which is required to be executed before the initialization of the actual communication process.Additionally,every device should be located such that it lies within the coverage area of a server,and this location information is used in the authentication process.A thorough analytical analysis was carried out to check the susceptibility of the proposed and existing authentication approaches against well-known intruder attacks,i.e.,man-in-the-middle,masquerading,device,and server impersonations,etc.,especially in the IoT domain.Moreover,proposed authentication and existing state-of-the-art approaches have been simulated in the real environment of IoT to verify their performance,particularly in terms of various evaluation metrics,i.e.,processing,communication,and storage overheads.These results have verified the superiority of the proposed scheme against existing state-of-the-art approaches,preferably in terms of communication,storage,and processing costs. 展开更多
关键词 Internet of things authenticity security location communication
暂未订购 下载PDF
An Intelligent Admission Control Scheme for Dynamic Slice Handover Policy in 5G Network Slicing 认领 引用
14
作者 Ratih Hikmah Puspita Jehad Ali Byeong-hee Roh 《Computers, Materials & Continua》 SCIE EI 2023年第5期4611-4631,共21页
5G use cases,for example enhanced mobile broadband(eMBB),massive machine-type communications(mMTC),and an ultra-reliable low latency communication(URLLC),need a network architecture capable of sustaining stringent lat... 5G use cases,for example enhanced mobile broadband(eMBB),massive machine-type communications(mMTC),and an ultra-reliable low latency communication(URLLC),need a network architecture capable of sustaining stringent latency and bandwidth requirements;thus,it should be extremely flexible and dynamic.Slicing enables service providers to develop various network slice architectures.As users travel from one coverage region to another area,the callmust be routed to a slice thatmeets the same or different expectations.This research aims to develop and evaluate an algorithm to make handover decisions appearing in 5G sliced networks.Rules of thumb which indicates the accuracy regarding the training data classification schemes within machine learning should be considered for validation and selection of the appropriate machine learning strategies.Therefore,this study discusses the network model’s design and implementation of self-optimization Fuzzy Qlearning of the decision-making algorithm for slice handover.The algorithm’s performance is assessed by means of connection-level metrics considering the Quality of Service(QoS),specifically the probability of the new call to be blocked and the probability of a handoff call being dropped.Hence,within the network model,the call admission control(AC)method is modeled by leveraging supervised learning algorithm as prior knowledge of additional capacity.Moreover,to mitigate high complexity,the integration of fuzzy logic as well as Fuzzy Q-Learning is used to discretize state and the corresponding action spaces.The results generated from our proposal surpass the traditional methods without the use of supervised learning and fuzzy-Q learning. 展开更多
关键词 5g network slice fuzzy q-Learning slice handover
暂未订购 下载PDF
Intrusion Detection in NSL-KDD Dataset Using Hybrid Self-Organizing Map Model 认领 引用 被引量：5
15
作者 Noveela Iftikhar Mujeeb Ur Rehman +2 位作者 Mumtaz Ali Shah Mohammed J.F.Alenazi Jehad Ali 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期639-671,共33页
Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few years.These devices are now easy targets for hackers because of their built-in security flaws.Combining a Self-Org... Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few years.These devices are now easy targets for hackers because of their built-in security flaws.Combining a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion detection.The proposed model is evaluated on the NSL-KDD dataset.The hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world applicability.Therefore,this paper proposes a highly efficient deployment strategy for resource-constrained network edges.The results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)classes.In particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively. 展开更多
关键词 Intrusion detection self-organizing map Internet of Things dimensionality reduction
暂未订购 下载PDF
A Common Architecture-Based Smart Home Tools and Applications Forensics for Scalable Investigations 认领 引用 被引量：1
16
作者 Sungbum Kim Gwangsik Lee +2 位作者 Jian Song Insoo Lee Taeshik Shon 《Computers, Materials & Continua》 SCIE EI 2025年第4期661-683,共23页
The smart home platform integrates with Internet of Things(IoT)devices,smartphones,and cloud servers,enabling seamless and convenient services.It gathers and manages extensive user data,including personal information,... The smart home platform integrates with Internet of Things(IoT)devices,smartphones,and cloud servers,enabling seamless and convenient services.It gathers and manages extensive user data,including personal information,device operations,and patterns of user behavior.Such data plays an essential role in criminal inves-tigations,highlighting the growing importance of specialized smart home forensics.Given the rapid advancement in smart home software and hardware technologies,many companies are introducing new devices and services that expand the market.Consequently,scalable and platform-specific forensic research is necessary to support efficient digital investigations across diverse smart home ecosystems.This study thoroughly examines the core components and structures of smart homes,proposing a generalized architecture that represents various operational environments.A three-stage smart home forensics framework is introduced:(1)analyzing application functions to infer relevant data,(2)extracting and processing data from interconnected devices,and(3)identifying data valuable for investigative purposes.The framework’s applicability is validated using testbeds from Samsung SmartThings and Xiaomi Mi Home platforms,offering practical insights for real-world forensic applications.The results demonstrate that the proposed forensic framework effectively acquires and classifies relevant digital evidence in smart home platforms,confirming its practical applicability in smart home forensic investigations. 展开更多
关键词 Digital forensic forensic framework internet of things smart home smart home platform
暂未订购 下载PDF
Image Watermarking Algorithm Base on the Second Order Derivative and Discrete Wavelet Transform 认领 引用
17
作者 Maazen Alsabaan Zaid Bin Faheem +1 位作者 Yuanyuan Zhu Jehad Ali 《Computers, Materials & Continua》 SCIE EI 2025年第7期491-512,共22页
Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense mechanisms.Image watermarking can be used to protect the copyright of digital media by embe... Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense mechanisms.Image watermarking can be used to protect the copyright of digital media by embedding a unique identifier that identifies the owner of the content.Image watermarking can also be used to verify the authenticity of digital media,such as images or videos,by ascertaining the watermark information.In this paper,a mathematical chaos-based image watermarking technique is proposed using discrete wavelet transform(DWT),chaotic map,and Laplacian operator.The DWT can be used to decompose the image into its frequency components,chaos is used to provide extra security defense by encrypting the watermark signal,and the Laplacian operator with optimization is applied to the mid-frequency bands to find the sharp areas in the image.These mid-frequency bands are used to embed the watermarks by modifying the coefficients in these bands.The mid-sub-band maintains the invisible property of the watermark,and chaos combined with the second-order derivative Laplacian is vulnerable to attacks.Comprehensive experiments demonstrate that this approach is effective for common signal processing attacks,i.e.,compression,noise addition,and filtering.Moreover,this approach also maintains image quality through peak signal-to-noise ratio(PSNR)and structural similarity index metrics(SSIM).The highest achieved PSNR and SSIM values are 55.4 dB and 1.In the same way,normalized correlation(NC)values are almost 10%–20%higher than comparative research.These results support assistance in copyright protection in multimedia content. 展开更多
关键词 Discrete wavelet transform laplacian image watermarking chaos multimedia security
暂未订购 下载PDF
Race Classification Using Deep Learning 认领 引用 被引量：4
18
作者 Khalil Khan Rehan Ullah Khan +3 位作者 Jehad Ali Irfan Uddin Sahib Khan Byeong-hee Roh 《Computers, Materials & Continua》 SCIE EI 2021年第9期3483-3498,共16页
Race classification is a long-standing challenge in the field of face image analysis.The investigation of salient facial features is an important task to avoid processing all face parts.Face segmentation strongly bene... Race classification is a long-standing challenge in the field of face image analysis.The investigation of salient facial features is an important task to avoid processing all face parts.Face segmentation strongly benefits several face analysis tasks,including ethnicity and race classification.We propose a race-classification algorithm using a prior face segmentation framework.A deep convolutional neural network(DCNN)was used to construct a face segmentation model.For training the DCNN,we label face images according to seven different classes,that is,nose,skin,hair,eyes,brows,back,and mouth.The DCNN model developed in the first phase was used to create segmentation results.The probabilistic classification method is used,and probability maps(PMs)are created for each semantic class.We investigated five salient facial features from among seven that help in race classification.Features are extracted from the PMs of five classes,and a new model is trained based on the DCNN.We assessed the performance of the proposed race classification method on four standard face datasets,reporting superior results compared with previous studies. 展开更多
关键词 Deep learning facial feature face analysis learning race race classification
暂未订购 下载PDF
Wireless,multimodal sensors for continuous measurement of pressure,temperature,and hydration of patients in wheelchair 认领 引用 被引量：13
19
作者 Seokjoo Cho Hyeonseok Han +37 位作者 Hyunwoo Park Sung-Uk Lee Jae-Hwan Kim Sung Woo Jeon Mengqiu Wang Raudel Avila Zhaoqian Xi Kabseok Ko Minsu Park Jungyup Lee Myungwoo Choi Je-Sang Lee Weon Gi Min Byeong-Ju Lee Soyeong Lee Jungrak Choi Jimin Gu Jaeho Park Min Seong Kim Junseong Ahn Osman Gul Chankyu Han Gihun Lee Seunghwan Kim Kyuyoung Kim Jeonghyun Kim Chang-Mo Kang Jahyun Koo Sung Soo Kwak Sungbong Kim Dong Yun Choi Seokwoo Jeon Hyung Jin Sung Yong Bae Park Minkyu Je Young Tae Cho Yong Suk Oh Inkyu Park 《npj Flexible Electronics》 SCIE 2023年第1期479-493,共15页
Individuals who are unable to walk independently spend most of the day in a wheelchair.This population is at high risk for developing pressure injuries caused by sitting.However,early diagnosis and prevention of these... Individuals who are unable to walk independently spend most of the day in a wheelchair.This population is at high risk for developing pressure injuries caused by sitting.However,early diagnosis and prevention of these injuries still remain challenging.Herein,we introduce battery-free,wireless,multimodal sensors and a movable system for continuous measurement of pressure,temperature,and hydration at skin interfaces.The device design includes a crack-activated pressure sensor with nanoscale encapsulations for enhanced sensitivity,a temperature sensor for measuring skin temperature,and a galvanic skin response sensor for measuring skin hydration levels.The movable system enables power harvesting,and data communication to multiple wireless devices mounted at skin-cushion interfaces of wheelchair users over full body coverage.Experimental evaluations and numerical simulations of the devices,together with clinical trials for wheelchair patients,demonstrate the feasibility and stability of the sensor system for preventing pressure injuries caused by sitting. 展开更多
关键词 injuries modal mounted
Complex Problems Solution as a Service Based on Predictive Optimization and Tasks Orchestration in Smart Cities 认领 引用
20
作者 Shabir Ahmad Jehad Ali +2 位作者 Faisal Jamil Taeg Keun Whangbo DoHyeun Kim 《Computers, Materials & Continua》 SCIE EI 2021年第10期1271-1288,共18页
Smart cities have different contradicting goals having no apparent solution.The selection of the appropriate solution,which is considered the best compromise among the candidates,is known as complex problem-solving.Sm... Smart cities have different contradicting goals having no apparent solution.The selection of the appropriate solution,which is considered the best compromise among the candidates,is known as complex problem-solving.Smart city administrators face different problems of complex nature,such as optimal energy trading in microgrids and optimal comfort index in smart homes,to mention a few.This paper proposes a novel architecture to offer complex problem solutions as a service(CPSaaS)based on predictive model optimization and optimal task orchestration to offer solutions to different problems in a smart city.Predictive model optimization uses a machine learning module and optimization objective to compute the given problem’s solutions.The task orchestration module helps decompose the complex problem in small tasks and deploy them on real-world physical sensors and actuators.The proposed architecture is hierarchical and modular,making it robust against faults and easy to maintain.The proposed architecture’s evaluation results highlight its strengths in fault tolerance,accuracy,and processing speed. 展开更多
关键词 Internet of things complex problem solving task modeling embedded IoT systems predictive optimization artificial cognition task orchestration
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈