IEEE Networking Letters最新文献_第9页

Symbolic and Safety-Centric Formal Verification of MAVLink for Autonomous eVTOL Systems 自主eVTOL系统MAVLink的符号和安全中心形式化验证

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-09-16 DOI: 10.1109/LNET.2025.3610580

Bing Mak;Jingda Yang;Juntao Chen;Paul Ratazzi;Ying Wang

引用次数: 0

AIoT-Driven Privacy-Preserving and Decentralized Ubiquitous Music Learning 物联网驱动的隐私保护和分散的无处不在的音乐学习

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-08-25 DOI: 10.1109/LNET.2025.3602131

Zhisheng Liu;Yaxin Zhao;Hu Xiong;Abubaker Wahaballa;Kuo-Hui Yeh

引用次数: 0

SliceWise: Traffic-Aware Multi-Agent RL with Temporal Encoding for Joint Slice Admission and Resource Orchestration in O-RAN 基于片的流量感知多代理RL：基于时间编码的O-RAN中联合片准入和资源编排

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-11-17 DOI: 10.1109/LNET.2025.3633686

Xingqi Wu;Yuhui Wang;Junaid Farooq;Hakim Ghazzai;Gianluca Setti

引用次数: 0

IEEE Networking Letters Publication Information IEEE网络通讯出版信息

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2026-01-16 DOI: 10.1109/LNET.2025.3636370

引用次数: 0

IDS-Twin: Digital Twin-Based Intrusion Detection Systems for Wireless Networks IDS-Twin：基于数字孪生的无线网络入侵检测系统

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-09-05 DOI: 10.1109/LNET.2025.3606789

Hassan El Alami;Danda B. Rawat

{"title":"IDS-Twin: Digital Twin-Based Intrusion Detection Systems for Wireless Networks","authors":"Hassan El Alami;Danda B. Rawat","doi":"10.1109/LNET.2025.3606789","DOIUrl":"https://doi.org/10.1109/LNET.2025.3606789","url":null,"abstract":"The rapid expansion of IoT devices has greatly enhanced wireless connectivity but also increased exposure to sophisticated and evolving cyber threats. Recent intrusion detection systems (IDS) based on Artificial Intelligence (AI) often rely on offline training and static models, limiting their ability to adapt in dynamic environments where device behavior and attack patterns continuously change. To address these challenges, we propose IDS-Twin, a Digital Twin (DT)-based intrusion detection framework designed for wireless IoT. IDS-Twin incorporates a self-adaptive anomaly detection model powered by contrastive learning, enabling real-time learning from streaming traffic with minimal labeled data. The DT component is implemented as a lightweight software replay engine that emulates real-time arrival by sequentially streaming flows in timestamp order, enabling the system to continuously detect and adapt to new or evolving attacks. Experimental validation on the UNSW-NB15 and CIC-IoT-2023 datasets demonstrates the superior performance and adaptability of IDS-Twin, outperforming state-of-the-art IDS solutions in detecting both known and unknown attacks in wireless networks.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 4","pages":"255-259"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Weighted Privacy-Preserving Federated Learning for Proton Exchange Membrane Fuel Cell 质子交换膜燃料电池的加权隐私保护联邦学习

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-10-08 DOI: 10.1109/LNET.2025.3619460

Jingxue Chen;Juhao Wang;Fazlullah Khan;Jingcheng Song;Chunhua Su

{"title":"A Weighted Privacy-Preserving Federated Learning for Proton Exchange Membrane Fuel Cell","authors":"Jingxue Chen;Juhao Wang;Fazlullah Khan;Jingcheng Song;Chunhua Su","doi":"10.1109/LNET.2025.3619460","DOIUrl":"https://doi.org/10.1109/LNET.2025.3619460","url":null,"abstract":"Proton exchange membrane fuel cells (PEMFCs) are emerging as a promising new energy technology with the potential to significantly transform the field of new energy vehicles. However, due to the limited availability of data at individual institutions—since PEMFCs technology remains in the developmental stage—collaborative learning across multiple institutions becomes essential. Given the high confidentiality of research data and models, institutions are unwilling to share raw data or trained models. The widely accepted approach is to utilize federated learning to balance privacy and data availability. However, existing federated learning schemes cannot meet the requirement of PEMFCs since low accuracy and high centralization. To address the limitations of existing methods, which suffer from centralization and low aggregation accuracy, this letter proposes a decentralized, high-precision, and dynamic privacy-preserving federated learning framework. The proposed approach employs aggregation techniques combined with homomorphic encryption to ensure privacy and decentralization. Unlike traditional federated learning frameworks, our protocol does not rely on any central server or coordination node; all participants are treated equally. The aggregation results are deterministic and not influenced by added random noise, thereby ensuring high model accuracy. Moreover, the system supports dynamic participation, allowing users to join or leave at any time. Experimental results demonstrate that our framework achieves high computational efficiency and robust performance, making it a practical and secure solution for collaborative PEMFC development.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 4","pages":"270-273"},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enabling End-to-End Determinism With Cross-Domain Traffic Scheduling Mechanism Towards 6G 面向6G的跨域流量调度机制实现端到端确定性

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-09-16 DOI: 10.1109/LNET.2025.3610703

Hongxing Li;Yali Li;Xiaoyu Zhao;Yifan Huang;Guochu Shou

引用次数: 0

Multi-Agent DDPG-Based Joint Beam Hopping and Resource Allocation for Cognitive GEO-LEO Satellite Networks 基于多agent ddpg的认知GEO-LEO卫星网络联合跳波束与资源分配

IF 3.3

IEEE Networking Letters Pub Date : 2025-12-01 Epub Date: 2025-09-02 DOI: 10.1109/LNET.2025.3605531

Quynh Tu Ngo;Ying He;Beeshanga Jayawickrama;Eryk Dutkiewicz

引用次数: 0

A Weighted Fault and Congestion Aware Routing Algorithm for Wireless Network-on-Chip Systems 无线片上网络系统的加权故障和拥塞感知路由算法