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Demystifying User-based Active Learning for Network Monitoring Tasks

, , , , and . 2nd Workshop on Machine Learning & Networking (MaLeNe) as part of NetSys'23, Potsdam, Germany, (September 2023)

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Understanding HTTP Traffic and CDN Behavior from the Eyes of a Mobile ISP., , and . PAM, volume 8362 of Lecture Notes in Computer Science, page 268-271. Springer, (2014)Looking for Network Latency Clusters in the LAC Region., and . LANCOMM@SIGCOMM, page 10-12. ACM, (2016)Diagnosing Device-Specific Anomalies in Cellular Networks., , , and . CoNEXT Student Workshop, page 18-20. ACM, (2014)Detecting and diagnosing anomalies in cellular networks using Random Neural Networks., , , and . IWCMC, page 351-356. IEEE, (2016)On the use of Sub-Space Clustering & Evidence Accumulation for traffic analysis & classification., , and . IWCMC, page 1016-1021. IEEE, (2011)YouTube all around: Characterizing YouTube from mobile and fixed-line network vantage points., , , , , and . EuCNC, page 1-5. IEEE, (2014)Network security and anomaly detection with Big-DAMA, a big data analytics framework., , , , and . CloudNet, page 16-22. IEEE, (2017)DeepSec meets RawPower - Deep Learning for Detection of Network Attacks Using Raw Representations., , and . SIGMETRICS Perform. Evaluation Rev., 46 (3): 147-150 (2018)Machine-Learning Based Approaches for Anomaly Detection and Classification in Cellular Networks., , and . TMA, IFIP, (2016)Considering User Behavior in the Quality of Experience Cycle: Towards Proactive QoE-aware Traffic Management, , and . IEEE Communications Letters, 23 (7): 1145-1148 (July 2019)