IEEE Networking Letters最新文献_第2页

Comprehensive Advanced Persistent Threats Dataset 综合高级持续威胁数据集

IEEE Networking Letters Pub Date : 2025-03-17 DOI: 10.1109/LNET.2025.3551989

Abdussamad Syed;Boubakr Nour;Makan Pourzandi;Chadi Assi;Mourad Debbabi

引用次数: 0

Deep Reinforcement Learning for AoI-Aware UAV-Assisted Networks With RF Energy Harvesting 基于射频能量收集的aoi感知无人机辅助网络的深度强化学习

IEEE Networking Letters Pub Date : 2025-03-13 DOI: 10.1109/LNET.2025.3550931

Gaurav Kumar Pandey;Devendra Singh Gurjar;Suneel Yadav;Xingwang Li

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Adaptive Trust Architecture for Secure IoT Communication in 6G 面向6G安全物联网通信的自适应信任架构

IEEE Networking Letters Pub Date : 2025-03-05 DOI: 10.1109/LNET.2025.3566909

Ijaz Ahmad;Shakthi Gimhana;Ijaz Ahmad;Erkki Harjula

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Secure Communication in Gaussian Multiple Access Wiretap Channels: A Deep Learning and Friendly Jamming Approach 高斯多址窃听信道中的安全通信：一种深度学习和友好干扰方法

IEEE Networking Letters Pub Date : 2025-03-01 DOI: 10.1109/LNET.2025.3566243

Sankalp;Lata;Gaurang Sondur;Mahendra K. Shukla;Om Jee Pandey;Maxime Guillaud

{"title":"Secure Communication in Gaussian Multiple Access Wiretap Channels: A Deep Learning and Friendly Jamming Approach","authors":"Sankalp;Lata;Gaurang Sondur;Mahendra K. Shukla;Om Jee Pandey;Maxime Guillaud","doi":"10.1109/LNET.2025.3566243","DOIUrl":"https://doi.org/10.1109/LNET.2025.3566243","url":null,"abstract":"The use of deep learning (DL) in communication systems shows great promise, particularly through DL-based physical-layer techniques with autoencoders (AEs) for end-to-end learning. This letter presents an AE-based DL framework to enhance physical-layer security in scenarios where multiple transmitters communicate with the receiver under eavesdropping threats, specifically within a Gaussian multiple-access wiretap channel. A key feature is a friendly jammer that emits a high-power Gaussian signal to disrupt eavesdroppers. The proposed framework is particularly relevant for security-critical applications such as wireless health monitoring systems, where safeguarding sensitive data is paramount. We assess secrecy performance by analyzing the symbol error rate among users in the presence of both an eavesdropper and a jammer. Simulation results show that our DL-based Gaussian jamming strategy significantly improves secrecy performance, effectively safeguarding communications from eavesdropping. This letter highlights the potential of DL techniques to enhance communication security in complex multi-user environments.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 2","pages":"78-82"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308550","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

Network Resource Optimization for ML-Based UAV Condition Monitoring With Vibration Analysis 基于机器学习的无人机状态监测网络资源优化与振动分析

IEEE Networking Letters Pub Date : 2025-02-24 DOI: 10.1109/LNET.2025.3545286

Alexandre Gemayel;Dimitrios Michael Manias;Abdallah Shami

引用次数: 0

2024 Index IEEE Networking Letters Vol. 6 IEEE网络通讯第6卷

IEEE Networking Letters Pub Date : 2025-02-24 DOI: 10.1109/LNET.2025.3544938

引用次数: 0

GNNPPOR: A Proximal Policy Optimization Multi-Factor Joint Routing Approach Based on Graph Neural Networks in FANETs GNNPPOR：基于图神经网络的近端策略优化多因素联合路由方法

IEEE Networking Letters Pub Date : 2025-02-17 DOI: 10.1109/LNET.2025.3542762

Jian Song;Jing Li;Qingwang Wang;Yebo Gu;Tao Shen

{"title":"GNNPPOR: A Proximal Policy Optimization Multi-Factor Joint Routing Approach Based on Graph Neural Networks in FANETs","authors":"Jian Song;Jing Li;Qingwang Wang;Yebo Gu;Tao Shen","doi":"10.1109/LNET.2025.3542762","DOIUrl":"https://doi.org/10.1109/LNET.2025.3542762","url":null,"abstract":"Given the significant challenges of low resource utilization, load imbalance, and difficulties in meeting quality of service requirements in Flying Ad Hoc Networks (FANETs) routing protocols, this letter proposes a Graph Neural Network (GNN)-based approach for proximal policy optimization routing (GNNPPOR). The approach aims to integrate traffic engineering into FANETs to effectively distribute network load and meet quality of service requirements. In GNNPPOR, we design a GNN model that first aggregates multi-dimensional network state information efficiently through a message-passing mechanism. Subsequently, the network state is updated in real-time using a gated recurrent unit to adapt to dynamic changes in the FANETs network state. Finally, a multi-factor joint decision-making approach is proposed to identify suitable routes for each traffic based on the current network state. Simulation results demonstrate that GNNPPOR outperforms existing methods in several key metrics. Specifically, packet delivery rate increased by 25.3%, while energy consumption and network jitter decreased by 12.8% and 24.9%, respectively.","PeriodicalId":100628,"journal":{"name":"IEEE Networking Letters","volume":"7 2","pages":"130-134"},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308552","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

Approximation Polynomial-Time Algorithms for Consistency-Aware Multi-Server Network Design in Delay-Sensitive Applications 延迟敏感应用中一致性感知多服务器网络设计的近似多项式时间算法

IEEE Networking Letters Pub Date : 2025-02-12 DOI: 10.1109/LNET.2025.3541351

Masaki Oda;Akio Kawabata;Eiji Oki

引用次数: 0

Editorial SI on Advances in AI for 6G Networks 人工智能在6G网络中的进展

IEEE Networking Letters Pub Date : 2025-02-11 DOI: 10.1109/LNET.2024.3519937

Hatim Chergui;Kamel Tourki;Jun Wu

引用次数: 0

IEEE Networking Letters Author Guidelines IEEE网络通讯作者指南

IEEE Networking Letters Pub Date : 2025-02-11 DOI: 10.1109/LNET.2025.3526350

引用次数: 0