251 | -- | 252 | Selma Boumerdassi, Ruben H. Milocco, Leïla Saïdane, Nicolas Puech. Machine learning for networking |
253 | -- | 265 | Miguel Landry Foko Sindjoung, Pascale Minet. Estimating and predicting link quality in wireless IoT networks |
267 | -- | 280 | V. Ch Sekhar Rao Rayavarapu, Arunanshu Mahapatro. NLOS identification and mitigation in UWB positioning with bagging-based ensembled classifiers |
281 | -- | 295 | Golshan Famitafreshi, Cristina Cano. Achieving proportional fairness in WiFi networks via bandit convex optimization |
297 | -- | 309 | Ons Aouedi, Kandaraj Piamrat, Salima Hamma, Menuka Perera Jayasuriya Kuranage. Network traffic analysis using machine learning: an unsupervised approach to understand and slice your network |
311 | -- | 330 | Adel Djama, Badis Djamaa, Mustapha Réda Senouci, Nabil Khemache. LAFS: a learning-based adaptive forwarding strategy for NDN-based IoT networks |
331 | -- | 344 | Adda Boualem, Hacène Fouchal, Marwane Ayaida, Cyril De Runz. Fibonacci tiles strategy for optimal coverage in IoT networks |
345 | -- | 357 | Abdallah Sobehy, Éric Renault, Paul Mühlethaler. Generalization aspect of accurate machine learning models for CSI-based localization |
359 | -- | 370 | Samuel A. Ajila, Chung-Horng Lung, Anurag Das. Analysis of error-based machine learning algorithms in network anomaly detection and categorization |
371 | -- | 394 | Arnaud Rosay, Kévin Riou, Florent Carlier, Pascal Leroux. Multi-layer perceptron for network intrusion detection |
395 | -- | 406 | Ola Salman, Imad H. Elhajj, Ayman I. Kayssi, Ali Chehab. Mutated traffic detection and recovery: an adversarial generative deep learning approach |
407 | -- | 420 | Martín Panza, Diego Madariaga, Javier Bustos-Jiménez. Extracting human behavior patterns from DNS traffic |
421 | -- | 435 | Diogo Ferreira, Carlos R. Senna, Paulo Salvador, Luís Cortesão, Cristina Pires, Rui Pedro, Susana Sargento. Prediction of low accessibility in 4G networks |
437 | -- | 454 | Taku Wakui, Takao Kondo, Fumio Teraoka. GAMPAL: an anomaly detection mechanism for Internet backbone traffic by flow size prediction with LSTM-RNN |