Pub Date : 2025-12-19DOI: 10.1109/TCE.2025.3633469
{"title":"IEEE Consumer Technology Society Board of Governors","authors":"","doi":"10.1109/TCE.2025.3633469","DOIUrl":"https://doi.org/10.1109/TCE.2025.3633469","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 4","pages":"C3-C3"},"PeriodicalIF":10.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11306169","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1109/TCE.2025.3599548
Yang Li;Muhammad Attique Khan;Muhammad Khurram Khan;Mohammad Kamrul Hasan
In recent years, the widespread application of agricultural consumer electronic devices, such as sensors, actuators, controllers, unmanned aerial vehicles (UAVs), and robots, has significantly enhanced the intelligence and efficiency of agricultural production. These devices have become indispensable tools for modern farming, enabling precise monitoring, control, and automation of various agricultural processes. However, the current technologies employed in these devices have certain limitations in terms of optimizing computational resources and reducing carbon emissions. This is a pressing issue that needs to be addressed, especially against the global backdrop of actively promoting carbon neutrality and developing low-carbon sustainable agriculture.
{"title":"Guest Editorial Sustainable Computing for Next-Generation Low-Carbon Agricultural Consumer Electronics","authors":"Yang Li;Muhammad Attique Khan;Muhammad Khurram Khan;Mohammad Kamrul Hasan","doi":"10.1109/TCE.2025.3599548","DOIUrl":"https://doi.org/10.1109/TCE.2025.3599548","url":null,"abstract":"In recent years, the widespread application of agricultural consumer electronic devices, such as sensors, actuators, controllers, unmanned aerial vehicles (UAVs), and robots, has significantly enhanced the intelligence and efficiency of agricultural production. These devices have become indispensable tools for modern farming, enabling precise monitoring, control, and automation of various agricultural processes. However, the current technologies employed in these devices have certain limitations in terms of optimizing computational resources and reducing carbon emissions. This is a pressing issue that needs to be addressed, especially against the global backdrop of actively promoting carbon neutrality and developing low-carbon sustainable agriculture.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 4","pages":"12358-12360"},"PeriodicalIF":10.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11306176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-19DOI: 10.1109/TCE.2025.3633470
{"title":"IEEE Consumer Technology Society Officers and Committee Chairs","authors":"","doi":"10.1109/TCE.2025.3633470","DOIUrl":"https://doi.org/10.1109/TCE.2025.3633470","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 4","pages":"C4-C4"},"PeriodicalIF":10.9,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11306168","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-11DOI: 10.1109/TCE.2025.3608519
{"title":"IEEE Consumer Technology Society Officers and Committee Chairs","authors":"","doi":"10.1109/TCE.2025.3608519","DOIUrl":"https://doi.org/10.1109/TCE.2025.3608519","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 3","pages":"C4-C4"},"PeriodicalIF":10.9,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11241060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145486519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-11DOI: 10.1109/TCE.2025.3608496
{"title":"IEEE Consumer Technology Society Board of Governors","authors":"","doi":"10.1109/TCE.2025.3608496","DOIUrl":"https://doi.org/10.1109/TCE.2025.3608496","url":null,"abstract":"","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 3","pages":"C3-C3"},"PeriodicalIF":10.9,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11241059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145486500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-11DOI: 10.1109/TCE.2025.3560705
Xin Ning;Lusi Li;Neeraj Kumar
The widespread adoption of Consumer Internet of Things (CIoT) devices has transformed consumer electronics, facilitating seamless integration between physical and digital domains. However, the surge in connected devices and the accompanying data explosion present significant challenges in latency, security, and privacy. Traditional cloud-based architectures are increasingly inadequate, as vulnerabilities like gradient leakage, poisoning attacks, and single-point failures compromise data integrity. The integration of Zero Trust Edge (ZTE) and Federated Learning (FL) offers a groundbreaking approach, providing decentralized security and privacy-preserving collaborative learning.
{"title":"Zero Trust Edge and Federated Learning for Consumer Internet of Things","authors":"Xin Ning;Lusi Li;Neeraj Kumar","doi":"10.1109/TCE.2025.3560705","DOIUrl":"https://doi.org/10.1109/TCE.2025.3560705","url":null,"abstract":"The widespread adoption of Consumer Internet of Things (CIoT) devices has transformed consumer electronics, facilitating seamless integration between physical and digital domains. However, the surge in connected devices and the accompanying data explosion present significant challenges in latency, security, and privacy. Traditional cloud-based architectures are increasingly inadequate, as vulnerabilities like gradient leakage, poisoning attacks, and single-point failures compromise data integrity. The integration of Zero Trust Edge (ZTE) and Federated Learning (FL) offers a groundbreaking approach, providing decentralized security and privacy-preserving collaborative learning.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 3","pages":"9132-9132"},"PeriodicalIF":10.9,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11241091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-23DOI: 10.1109/TCE.2025.3624764
Guoqiang Zhang;Yu Liang;Kaiyue Tian;Jiachen Yi;Hadeel Alsolai;Menglu Liu;Xiyuan Hu
Current deepfake detection methods primarily focus on exploring inter-frame inconsistencies using convolutional networks, neglecting the investigation of long-range spatiotemporal inconsistencies. Simultaneously, these methods rely on single-feature exploitation for forgery detection, resulting in limited generalization capability and robustness. To address these issues, this paper proposes a novel network that comprehensively utilizes illumination-geometric features and facial forgery trace features to excavate deepfake artifacts across multiple scales. The architecture comprises three main components: First, the Lighting-Geometric Information Capture Module (LGCM) integrates facial landmark normal vectors and illumination coefficients to construct comprehensive spatiotemporal representations. Then, the Bi-directional Multiscale Enhancement Module (BMEM) captures attention information between different frames in the spatial domain and models inter-frame discrepancy attention in the temporal domain. Furthermore, the Spatio-temporal Attention Module (STAM) mines global semantics and adaptively derives long-range spatiotemporal representations. Experimental results demonstrate that the proposed method achieves high AUC values on the four subsets of FF++ C40, Celeb-DF, and DFDC datasets, outperforming the comparative methods. Similarly, cross-forgery method detection validates the robustness and generalization capability of the proposed approach.
{"title":"Leveraging Spatial-Temporal Illumination Features and Convolution-Transformer Hybrid Networks for Deepfake Video Detection","authors":"Guoqiang Zhang;Yu Liang;Kaiyue Tian;Jiachen Yi;Hadeel Alsolai;Menglu Liu;Xiyuan Hu","doi":"10.1109/TCE.2025.3624764","DOIUrl":"https://doi.org/10.1109/TCE.2025.3624764","url":null,"abstract":"Current deepfake detection methods primarily focus on exploring inter-frame inconsistencies using convolutional networks, neglecting the investigation of long-range spatiotemporal inconsistencies. Simultaneously, these methods rely on single-feature exploitation for forgery detection, resulting in limited generalization capability and robustness. To address these issues, this paper proposes a novel network that comprehensively utilizes illumination-geometric features and facial forgery trace features to excavate deepfake artifacts across multiple scales. The architecture comprises three main components: First, the Lighting-Geometric Information Capture Module (LGCM) integrates facial landmark normal vectors and illumination coefficients to construct comprehensive spatiotemporal representations. Then, the Bi-directional Multiscale Enhancement Module (BMEM) captures attention information between different frames in the spatial domain and models inter-frame discrepancy attention in the temporal domain. Furthermore, the Spatio-temporal Attention Module (STAM) mines global semantics and adaptively derives long-range spatiotemporal representations. Experimental results demonstrate that the proposed method achieves high AUC values on the four subsets of FF++ C40, Celeb-DF, and DFDC datasets, outperforming the comparative methods. Similarly, cross-forgery method detection validates the robustness and generalization capability of the proposed approach.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 4","pages":"12479-12489"},"PeriodicalIF":10.9,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-23DOI: 10.1109/TCE.2025.3624567
Sunder Ali Khowaja;Muhammad Salman Pathan;Kapal Dev;Ik Hyun Lee
High Fidelity (HiFi) identity customization with text-to-image generation has gained a lot of interest from all four quadrants, such as industries, consumers, researchers, and digital content creators. Such generational models are capable of personalizing images with pretrained diffusion models without extensive fine-tuning. However, existing works often compromise HiFi or generative behavior of the original model due to computational constraints associated with training identity customization on consumer electronic devices. Furthermore, when using auxiliary images for fusion, existing models often compromise the identity customization. In this regard, we propose Dynamic high-fidelity Identity Customization and Enhancement (DICE) that integrates a vision transformer (ViT), specifically dealing with facial and non-facial images to extract semantic features, a dynamic and multi-model contrastive fusion strategy, denoising diffusion model, and a composite loss function. The DICE leverages evolved feature extraction, multi-scale feature fusion, adaptive contrastive paths, and adaptive composite loss to achieve high fidelity, editability, and minimal refinement to the base model even for the fusion of base image with the auxiliary one. Such tuning-free identity customization is appropriate for the consumers on their resource constrained electronic devices, as it requires no retraining, shifting the computational burden to a one-time, server-side training process. Experiments demonstrate that DICE outperforms existing state-of-the-art methods while offering a flexible solution for personalized image generation.
{"title":"DICE: Tuning-Free Dynamic High-Fidelity Identity Customization and Enhancement Using Multi-Modal Contrastive Fusion for Consumer Devices","authors":"Sunder Ali Khowaja;Muhammad Salman Pathan;Kapal Dev;Ik Hyun Lee","doi":"10.1109/TCE.2025.3624567","DOIUrl":"https://doi.org/10.1109/TCE.2025.3624567","url":null,"abstract":"High Fidelity (HiFi) identity customization with text-to-image generation has gained a lot of interest from all four quadrants, such as industries, consumers, researchers, and digital content creators. Such generational models are capable of personalizing images with pretrained diffusion models without extensive fine-tuning. However, existing works often compromise HiFi or generative behavior of the original model due to computational constraints associated with training identity customization on consumer electronic devices. Furthermore, when using auxiliary images for fusion, existing models often compromise the identity customization. In this regard, we propose Dynamic high-fidelity Identity Customization and Enhancement (DICE) that integrates a vision transformer (ViT), specifically dealing with facial and non-facial images to extract semantic features, a dynamic and multi-model contrastive fusion strategy, denoising diffusion model, and a composite loss function. The DICE leverages evolved feature extraction, multi-scale feature fusion, adaptive contrastive paths, and adaptive composite loss to achieve high fidelity, editability, and minimal refinement to the base model even for the fusion of base image with the auxiliary one. Such tuning-free identity customization is appropriate for the consumers on their resource constrained electronic devices, as it requires no retraining, shifting the computational burden to a one-time, server-side training process. Experiments demonstrate that DICE outperforms existing state-of-the-art methods while offering a flexible solution for personalized image generation.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 4","pages":"12510-12518"},"PeriodicalIF":10.9,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-22DOI: 10.1109/TCE.2025.3624585
Ahmed Barnawi;Ishan Budhiraja;Neeraj Kumar;Haneef Khan;Hussein Zangoti
The Internet of Things (IoT) has emerged as an innovative paradigm that interconnects a diverse array of devices and systems to enable uninterrupted communication and data transmission. Within this extensive architecture, device-to-device (D2D) communication is critical for facilitating direct interactions among linked devices. This aspect of IoT surpasses traditional modalities of engagement between humans and machines. It cultivates an interactive ecosystem wherein devices autonomously collaborate, share data, and perform designated functions. D2D communication offers a multitude of benefits, such as enabling short-range interactions, minimizing latency, enhancing scalability, and optimizing energy efficiency. Moreover, to promote widespread connectivity and exceptionally dependable low-latency performance within the fifth generation (5G) network, the adoption of non-orthogonal multiple access (NOMA) merits further exploration. In this regard, the impact of federated learning on NOMA-based D2D group users (DGUs) within wireless-powered communication frameworks is examined. Initially, the D2D transmitters (DDTs) harvest energy from the radio frequency signals emitted by the base station. Subsequently, the DDTs employ NOMA to establish communication with the D2D receivers (DDRs) by utilizing the energy they have accumulated. A stochastic optimization problem is formulated to enhance energy efficiency (EE) and minimize delay. This formulation incorporates both stochastic traffic arrivals and the time-varying conditions of the communication channel. By applying the Markov decision process, the non-convex optimization problem is transformed into a mathematical model that encapsulates decision-making scenarios. Furthermore, federated learning is implemented to achieve the objectives and accelerate the dissemination of local training data across the DGUs. Empirical results illustrate that the proposed methodology achieves performance metrics that are 8.47% and 66.10% superior to those of distributed and centralized schemes, respectively.
{"title":"An Opportunistic Energy Harvesting Scheme for Tactile NOMA-Based D2D Users Using Federated Learning","authors":"Ahmed Barnawi;Ishan Budhiraja;Neeraj Kumar;Haneef Khan;Hussein Zangoti","doi":"10.1109/TCE.2025.3624585","DOIUrl":"https://doi.org/10.1109/TCE.2025.3624585","url":null,"abstract":"The Internet of Things (IoT) has emerged as an innovative paradigm that interconnects a diverse array of devices and systems to enable uninterrupted communication and data transmission. Within this extensive architecture, device-to-device (D2D) communication is critical for facilitating direct interactions among linked devices. This aspect of IoT surpasses traditional modalities of engagement between humans and machines. It cultivates an interactive ecosystem wherein devices autonomously collaborate, share data, and perform designated functions. D2D communication offers a multitude of benefits, such as enabling short-range interactions, minimizing latency, enhancing scalability, and optimizing energy efficiency. Moreover, to promote widespread connectivity and exceptionally dependable low-latency performance within the fifth generation (5G) network, the adoption of non-orthogonal multiple access (NOMA) merits further exploration. In this regard, the impact of federated learning on NOMA-based D2D group users (DGUs) within wireless-powered communication frameworks is examined. Initially, the D2D transmitters (DDTs) harvest energy from the radio frequency signals emitted by the base station. Subsequently, the DDTs employ NOMA to establish communication with the D2D receivers (DDRs) by utilizing the energy they have accumulated. A stochastic optimization problem is formulated to enhance energy efficiency (EE) and minimize delay. This formulation incorporates both stochastic traffic arrivals and the time-varying conditions of the communication channel. By applying the Markov decision process, the non-convex optimization problem is transformed into a mathematical model that encapsulates decision-making scenarios. Furthermore, federated learning is implemented to achieve the objectives and accelerate the dissemination of local training data across the DGUs. Empirical results illustrate that the proposed methodology achieves performance metrics that are 8.47% and 66.10% superior to those of distributed and centralized schemes, respectively.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 4","pages":"12398-12417"},"PeriodicalIF":10.9,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-09DOI: 10.1109/TCE.2025.3619781
Ishtiaq Ahmad;Umair Ahmad Mughal;Liang Yang;Yazeed Alkhrijah;Ahmad Almadhor;Mohamad A. Alawad;Chau Yuen
The rapid advancement of consumer connected and autonomous vehicle (CAV) technologies offers significant improvements in transportation efficiency, safety, and user convenience. However, these benefits come with substantial cybersecurity risks, as in-vehicle networks and cloud connectivity expose CAVs to increasingly sophisticated cyberattacks. Conventional intrusion detection systems (IDS) often fall short in this domain, as they are not adaptive and struggle to handle the dynamic and stealthy nature of modern attacks. To address these limitations, we propose a novel IDS framework based on a stacking ensemble architecture that integrates multiple machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost), as base learners. A Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) serves as the meta-learner to capture temporal dependencies and sequential patterns in network traffic. To enhance the model’s generalization capability, we incorporate a model-agnostic meta-learning (MAML) approach into the LSTM-RNN meta-learner. The MAML-enhanced set of capabilities enables more effective detection of evolving and previously unseen attack scenarios. Simulation results demonstrate that the proposed framework consistently outperforms standalone LSTM-RNN models, traditional ensemble methods, and individual base learners in detecting complex cyberattack patterns in consumer CAV environments. These findings highlight the potential of meta-learning-driven ensemble IDS frameworks for securing next-generation intelligent transportation systems.
{"title":"Intelligent Ensemble Learning Framework for Intrusion Detection in Consumer Connected and Autonomous Vehicles","authors":"Ishtiaq Ahmad;Umair Ahmad Mughal;Liang Yang;Yazeed Alkhrijah;Ahmad Almadhor;Mohamad A. Alawad;Chau Yuen","doi":"10.1109/TCE.2025.3619781","DOIUrl":"https://doi.org/10.1109/TCE.2025.3619781","url":null,"abstract":"The rapid advancement of consumer connected and autonomous vehicle (CAV) technologies offers significant improvements in transportation efficiency, safety, and user convenience. However, these benefits come with substantial cybersecurity risks, as in-vehicle networks and cloud connectivity expose CAVs to increasingly sophisticated cyberattacks. Conventional intrusion detection systems (IDS) often fall short in this domain, as they are not adaptive and struggle to handle the dynamic and stealthy nature of modern attacks. To address these limitations, we propose a novel IDS framework based on a stacking ensemble architecture that integrates multiple machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost), as base learners. A Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) serves as the meta-learner to capture temporal dependencies and sequential patterns in network traffic. To enhance the model’s generalization capability, we incorporate a model-agnostic meta-learning (MAML) approach into the LSTM-RNN meta-learner. The MAML-enhanced set of capabilities enables more effective detection of evolving and previously unseen attack scenarios. Simulation results demonstrate that the proposed framework consistently outperforms standalone LSTM-RNN models, traditional ensemble methods, and individual base learners in detecting complex cyberattack patterns in consumer CAV environments. These findings highlight the potential of meta-learning-driven ensemble IDS frameworks for securing next-generation intelligent transportation systems.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"71 4","pages":"12437-12448"},"PeriodicalIF":10.9,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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