The Internet of Things(IoT)is emerging as an innovative phenomenon concerned with the development of numerous vital applications.With the development of IoT devices,huge amounts of information,including users’private...The Internet of Things(IoT)is emerging as an innovative phenomenon concerned with the development of numerous vital applications.With the development of IoT devices,huge amounts of information,including users’private data,are generated.IoT systems face major security and data privacy challenges owing to their integral features such as scalability,resource constraints,and heterogeneity.These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data,creating an attractive opportunity for cyberattacks.To address these challenges,artificial intelligence(AI)techniques,such as machine learning(ML)and deep learning(DL),are utilized to build an intrusion detection system(IDS)that helps to secure IoT systems.Federated learning(FL)is a decentralized technique that can help to improve information privacy and performance by training the IDS on discrete linked devices.FL delivers an effectual tool to defend user confidentiality,mainly in the field of IoT,where IoT devices often obtain privacy-sensitive personal data.This study develops a Privacy-Enhanced Federated Learning for Intrusion Detection using the Chameleon Swarm Algorithm and Artificial Intelligence(PEFLID-CSAAI)technique.The main aim of the PEFLID-CSAAI method is to recognize the existence of attack behavior in IoT networks.First,the PEFLIDCSAAI technique involves data preprocessing using Z-score normalization to transformthe input data into a beneficial format.Then,the PEFLID-CSAAI method uses the Osprey Optimization Algorithm(OOA)for the feature selection(FS)model.For the classification of intrusion detection attacks,the Self-Attentive Variational Autoencoder(SA-VAE)technique can be exploited.Finally,the Chameleon Swarm Algorithm(CSA)is applied for the hyperparameter finetuning process that is involved in the SA-VAE model.A wide range of experiments were conducted to validate the execution of the PEFLID-CSAAI model.The simulated outcomes demonstrated that the PEFLID-CSAAI technique outperformed other recent models,highlighting its potential as a valuable tool for future applications in healthcare devices and small engineering systems.展开更多
Cloud computing facilitates the great potentiality of storing and managing remote access to services in terms of software as a service(SaaS).Several organizations have moved towards outsourcing over the cloud to reduc...Cloud computing facilitates the great potentiality of storing and managing remote access to services in terms of software as a service(SaaS).Several organizations have moved towards outsourcing over the cloud to reduce the burden on local resources.In this context,the metaheuristic optimization method is determined to be highly suitable for selecting appropriate services that comply with the requirements of the client’s requests,as the services stored over the cloud are too complex and scalable.To achieve better service composition,the parameters of Quality of Service(QoS)related to each service considered to be the best resource need to be selected and optimized for attaining potential services over the cloud.Thus,the cloud service composition needs to concentrate on the selection and integration of services over the cloud to satisfy the client’s requests.In this paper,a Hybrid Chameleon and Honey Badger Optimization Algorithm(HCHBOA)-based cloud service composition scheme is presented for achieving efficient services with satisfying the requirements ofQoS over the cloud.This proposed HCHBOA integrated the merits of the Chameleon Search Algorithm(CSA)and Honey Badger Optimization Algorithm(HBOA)for balancing the tradeoff between the rate of exploration and exploitation.It specifically used HBOA for tuning the parameters of CSA automatically so that CSA could adapt its performance depending on its incorporated tuning factors.The experimental results of the proposed HCHBOA with experimental datasets exhibited its predominance by improving the response time by 21.38%,availability by 20.93%and reliability by 19.31%with a minimized execution time of 23.18%,compared to the baseline cloud service composition schemes used for investigation.展开更多
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,under grant number NBU-FFR-2025-451-6.
文摘The Internet of Things(IoT)is emerging as an innovative phenomenon concerned with the development of numerous vital applications.With the development of IoT devices,huge amounts of information,including users’private data,are generated.IoT systems face major security and data privacy challenges owing to their integral features such as scalability,resource constraints,and heterogeneity.These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data,creating an attractive opportunity for cyberattacks.To address these challenges,artificial intelligence(AI)techniques,such as machine learning(ML)and deep learning(DL),are utilized to build an intrusion detection system(IDS)that helps to secure IoT systems.Federated learning(FL)is a decentralized technique that can help to improve information privacy and performance by training the IDS on discrete linked devices.FL delivers an effectual tool to defend user confidentiality,mainly in the field of IoT,where IoT devices often obtain privacy-sensitive personal data.This study develops a Privacy-Enhanced Federated Learning for Intrusion Detection using the Chameleon Swarm Algorithm and Artificial Intelligence(PEFLID-CSAAI)technique.The main aim of the PEFLID-CSAAI method is to recognize the existence of attack behavior in IoT networks.First,the PEFLIDCSAAI technique involves data preprocessing using Z-score normalization to transformthe input data into a beneficial format.Then,the PEFLID-CSAAI method uses the Osprey Optimization Algorithm(OOA)for the feature selection(FS)model.For the classification of intrusion detection attacks,the Self-Attentive Variational Autoencoder(SA-VAE)technique can be exploited.Finally,the Chameleon Swarm Algorithm(CSA)is applied for the hyperparameter finetuning process that is involved in the SA-VAE model.A wide range of experiments were conducted to validate the execution of the PEFLID-CSAAI model.The simulated outcomes demonstrated that the PEFLID-CSAAI technique outperformed other recent models,highlighting its potential as a valuable tool for future applications in healthcare devices and small engineering systems.
文摘Cloud computing facilitates the great potentiality of storing and managing remote access to services in terms of software as a service(SaaS).Several organizations have moved towards outsourcing over the cloud to reduce the burden on local resources.In this context,the metaheuristic optimization method is determined to be highly suitable for selecting appropriate services that comply with the requirements of the client’s requests,as the services stored over the cloud are too complex and scalable.To achieve better service composition,the parameters of Quality of Service(QoS)related to each service considered to be the best resource need to be selected and optimized for attaining potential services over the cloud.Thus,the cloud service composition needs to concentrate on the selection and integration of services over the cloud to satisfy the client’s requests.In this paper,a Hybrid Chameleon and Honey Badger Optimization Algorithm(HCHBOA)-based cloud service composition scheme is presented for achieving efficient services with satisfying the requirements ofQoS over the cloud.This proposed HCHBOA integrated the merits of the Chameleon Search Algorithm(CSA)and Honey Badger Optimization Algorithm(HBOA)for balancing the tradeoff between the rate of exploration and exploitation.It specifically used HBOA for tuning the parameters of CSA automatically so that CSA could adapt its performance depending on its incorporated tuning factors.The experimental results of the proposed HCHBOA with experimental datasets exhibited its predominance by improving the response time by 21.38%,availability by 20.93%and reliability by 19.31%with a minimized execution time of 23.18%,compared to the baseline cloud service composition schemes used for investigation.
