Machine learning empowered computer networks

被引：43
作者：
Cerquitelli, Tania [1 ]
Meo, Michela [2 ]
Curado, Marilia [3 ]
Skorin-Kapov, Lea [4 ]
Tsiropoulou, Eirini Eleni [5 ]
机构：
[1] Politecn Torino, Dept Control & Comp Engn, C Duca Abruzzi, 24, I-10129 Turin, Italy
[2] Politecn Torino, Dept Elect & Telecommun, C Duca Abruzzi, 24, I-10129 Turin, Italy
[3] Univ Coimbra, Ctr Informat & Syst, Dept Informat Engn, P-3030688 Coimbra, Portugal
[4] Univ Zagreb, Fac Elect Engn & Comp, Unska 3, Zagreb 10000, Croatia
[5] Univ New Mexico, Dept Elect & Comp Engn, 1 Univ New Mexico, MSC01 1100, Albuquerque, NM 87131 USA
来源：
COMPUTER NETWORKS | 2023年 / 230卷
基金：
美国国家科学基金会;
关键词：
Data-driven algorithms; Computer communication networking; Research findings; Resource-constrained devices; Multimodal deep learning; Explainable AI; Quality of service; Cyber-physical systems;
D O I：
10.1016/j.comnet.2023.109807
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This special issue explores how emerging machine learning (ML) and artificial intelligence (AI) algorithms can help computer networks become smarter. The final goal is to disseminate cutting-edge research findings and computer network advances on innovative data-driven methodologies and technologies to grow innovation in ML-empowered communication networks. This particular issue aims to present advances on cross-cutting edge machine learning solutions tailored to the computer communication networking area, focusing on algorithmic aspects. The objective is to present which ML methodologies are the most effective and promising ones in the networking context so that they can inspire other researchers and practitioners in the research area of computer networks.
引用
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页数：5
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[1]   PhishNot: A Cloud-Based Machine-Learning Approach to Phishing URL Detection [J].
Alani, Mohammed M. ;
Tawfik, Hissam .
COMPUTER NETWORKS, 2022, 218
[2]   Explainable Artificial Intelligence in communication networks: A use case for failure identification in microwave networks [J].
Ayoub, Omran ;
Di Cicco, Nicola ;
Ezzeddine, Fatima ;
Bruschetta, Federica ;
Rubino, Roberto ;
Nardecchia, Massimo ;
Milano, Michele ;
Musumeci, Francesco ;
Passera, Claudio ;
Tornatore, Massimo .
COMPUTER NETWORKS, 2022, 219
[3]   EvoIoT : An evolutionary IoT and non-IoT classification model in open environments [J].
Fan, Linna ;
He, Lin ;
Dong, Enhuan ;
Yang, Jiahai ;
Li, Chenglong ;
Lin, Jinlei ;
Wang, Zhiliang .
COMPUTER NETWORKS, 2022, 219
[4]   H-HOME: A learning framework of federated FANETs to provide edge computing to future delay-constrained IoT systems [J].
Grasso, Christian ;
Raftopoulos, Raoul ;
Schembra, Giovanni ;
Serrano, Salvatore .
COMPUTER NETWORKS, 2022, 219
[5]   Contextual counters and multimodal Deep Learning for activity-level traffic classification of mobile communication apps during COVID-19 pandemic [J].
Guarino, Idio ;
Aceto, Giuseppe ;
Ciuonzo, Domenico ;
Montieri, Antonio ;
Persico, Valerio ;
Pescape, Antonio .
COMPUTER NETWORKS, 2022, 219
[6]   Federated learning using game strategies: State-of-the-art and future trends [J].
Gupta, Rajni ;
Gupta, Juhi .
COMPUTER NETWORKS, 2023, 225
[7]   Adaptive QoS-aware multipath congestion control for live streaming [J].
Ji, Xiaolan ;
Han, Biao ;
Xu, Cao ;
Song, Congxi ;
Su, Jinshu .
COMPUTER NETWORKS, 2023, 220
[8]   Edge intelligence for service function chain deployment in NFV-enabled networks [J].
Khoshkholghi, Mohammad Ali ;
Mahmoodi, Toktam .
COMPUTER NETWORKS, 2022, 219
[9]   A generic learning simulation framework to assess security strategies in cyber-physical production systems [J].
Koita, Moussa ;
Diagana, Youssouf M. ;
Maiga, Oumar Y. ;
Traore, Mamadou K. .
COMPUTER NETWORKS, 2022, 218
[10]   QoS-oriented media access control using reinforcement learning for next-generation WLANs [J].
Lei, Jianjun ;
Li, Lu ;
Wang, Ying .
COMPUTER NETWORKS, 2022, 219
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